Datasets:

ngqtrung

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MMT_MI-Benchmark

like 0

Please provide the bounding box coordinates for the described object or area using the format [x1, y1, x2, y2]. Here, [x1, y1] represent the top-left coordinates and [x2, y2] the bottom-right coordinates within a normalized range of 0 to 1, where [0, 0] is the top-left corner and [1, 1] is the bottom-right corner of the image. Note that the width of the input image is 500 and the height is 166. QUESTION:right watch

A

[0.672, 0.012, 0.992, 0.976]

[0.672, 0.012, 0.94, 1.127]

[0.672, 0.012, 0.936, 0.97]

[0.512, 0.0, 0.832, 0.964]

referring_detection

visual_grounding

VAL

Please provide the bounding box coordinates for the described object or area using the format [x1, y1, x2, y2]. Here, [x1, y1] represent the top-left coordinates and [x2, y2] the bottom-right coordinates within a normalized range of 0 to 1, where [0, 0] is the top-left corner and [1, 1] is the bottom-right corner of the image. Note that the width of the input image is 640 and the height is 426. QUESTION:woman facing us

B

[0.497, 0.113, 0.925, 0.277]

[0.553, 0.322, 0.752, 0.739]

[0.553, 0.322, 0.761, 0.784]

[0.553, 0.322, 0.761, 0.714]

referring_detection

visual_grounding

VAL

Please provide the bounding box coordinates for the described object or area using the format [x1, y1, x2, y2]. Here, [x1, y1] represent the top-left coordinates and [x2, y2] the bottom-right coordinates within a normalized range of 0 to 1, where [0, 0] is the top-left corner and [1, 1] is the bottom-right corner of the image. Note that the width of the input image is 480 and the height is 640. QUESTION:a women was smilling and cooking

C

[0.019, 0.628, 0.435, 0.734]

[0.463, 0.186, 0.492, 0.341]

[0.204, 0.164, 0.544, 0.991]

[0.217, 0.173, 0.556, 1.0]

referring_detection

visual_grounding

VAL

Please provide the bounding box coordinates for the described object or area using the format [x1, y1, x2, y2]. Here, [x1, y1] represent the top-left coordinates and [x2, y2] the bottom-right coordinates within a normalized range of 0 to 1, where [0, 0] is the top-left corner and [1, 1] is the bottom-right corner of the image. Note that the width of the input image is 500 and the height is 375. QUESTION:The freshly made cake has had a few slices taken out of it already.

A

[0.604, 0.357, 0.876, 0.584]

[0.676, 0.416, 0.948, 0.643]

[0.604, 0.357, 0.844, 0.587]

[0.604, 0.357, 0.836, 0.579]

referring_detection

visual_grounding

VAL

Please provide the bounding box coordinates for the described object or area using the format [x1, y1, x2, y2]. Here, [x1, y1] represent the top-left coordinates and [x2, y2] the bottom-right coordinates within a normalized range of 0 to 1, where [0, 0] is the top-left corner and [1, 1] is the bottom-right corner of the image. Note that the width of the input image is 612 and the height is 612. QUESTION:A wooden table with a bowl on it

D

[0.502, 0.342, 1.033, 0.523]

[0.418, 0.353, 0.668, 0.632]

[0.516, 0.307, 1.0, 0.461]

[0.502, 0.342, 0.985, 0.495]

referring_detection

visual_grounding

VAL

Please provide the bounding box coordinates for the described object or area using the format [x1, y1, x2, y2]. Here, [x1, y1] represent the top-left coordinates and [x2, y2] the bottom-right coordinates within a normalized range of 0 to 1, where [0, 0] is the top-left corner and [1, 1] is the bottom-right corner of the image. Note that the width of the input image is 640 and the height is 480. QUESTION:A small bench farthest to the left of similar benches.

D

[0.766, 0.64, 0.972, 0.975]

[0.109, 0.71, 0.234, 0.787]

[0.188, 0.469, 0.463, 0.938]

[0.116, 0.367, 0.391, 0.835]

referring_detection

visual_grounding

VAL

Please provide the bounding box coordinates for the described object or area using the format [x1, y1, x2, y2]. Here, [x1, y1] represent the top-left coordinates and [x2, y2] the bottom-right coordinates within a normalized range of 0 to 1, where [0, 0] is the top-left corner and [1, 1] is the bottom-right corner of the image. Note that the width of the input image is 640 and the height is 425. QUESTION:A pizza being bent.

A

[0.559, 0.296, 0.93, 0.602]

[0.03, 0.139, 0.127, 0.638]

[0.559, 0.296, 0.87, 0.546]

[0.63, 0.231, 1.0, 0.536]

referring_detection

visual_grounding

VAL

Please provide the bounding box coordinates for the described object or area using the format [x1, y1, x2, y2]. Here, [x1, y1] represent the top-left coordinates and [x2, y2] the bottom-right coordinates within a normalized range of 0 to 1, where [0, 0] is the top-left corner and [1, 1] is the bottom-right corner of the image. Note that the width of the input image is 500 and the height is 375. QUESTION:A man in jeans raising his Wii Remote.

A

[0.364, 0.155, 0.608, 0.989]

[0.364, 0.155, 0.61, 0.997]

[0.036, 0.48, 0.48, 0.624]

[0.486, 0.0, 0.73, 0.835]

referring_detection

visual_grounding

VAL

Please provide the bounding box coordinates for the described object or area using the format [x1, y1, x2, y2]. Here, [x1, y1] represent the top-left coordinates and [x2, y2] the bottom-right coordinates within a normalized range of 0 to 1, where [0, 0] is the top-left corner and [1, 1] is the bottom-right corner of the image. Note that the width of the input image is 426 and the height is 640. QUESTION:Skateboarder wearing shoes with blue laces.

C

[0.732, 0.472, 0.967, 0.548]

[0.031, 0.091, 0.561, 0.855]

[0.031, 0.091, 0.664, 0.88]

[0.031, 0.091, 0.709, 0.952]

referring_detection

visual_grounding

VAL

Please provide the bounding box coordinates for the described object or area using the format [x1, y1, x2, y2]. Here, [x1, y1] represent the top-left coordinates and [x2, y2] the bottom-right coordinates within a normalized range of 0 to 1, where [0, 0] is the top-left corner and [1, 1] is the bottom-right corner of the image. Note that the width of the input image is 500 and the height is 375. QUESTION:A lunch tray with a blue and white toy.

D

[0.628, 0.0, 0.936, 0.715]

[0.324, 0.264, 0.348, 0.507]

[0.55, 0.061, 0.928, 0.411]

[0.692, 0.16, 1.0, 0.875]

referring_detection

visual_grounding

VAL

Please provide the bounding box coordinates for the described object or area using the format [x1, y1, x2, y2]. Here, [x1, y1] represent the top-left coordinates and [x2, y2] the bottom-right coordinates within a normalized range of 0 to 1, where [0, 0] is the top-left corner and [1, 1] is the bottom-right corner of the image. Note that the width of the input image is 612 and the height is 612. QUESTION:man in tan shirt riding a street bike with a boy sitting on the back

B

[0.193, 0.662, 0.379, 1.0]

[0.261, 0.621, 0.448, 0.959]

[0.261, 0.621, 0.482, 1.016]

[0.261, 0.621, 0.438, 0.995]

referring_detection

visual_grounding

VAL

Please provide the bounding box coordinates for the described object or area using the format [x1, y1, x2, y2]. Here, [x1, y1] represent the top-left coordinates and [x2, y2] the bottom-right coordinates within a normalized range of 0 to 1, where [0, 0] is the top-left corner and [1, 1] is the bottom-right corner of the image. Note that the width of the input image is 612 and the height is 612. QUESTION:a chocolate donut

C

[0.358, 0.338, 0.737, 0.526]

[0.186, 0.35, 0.531, 0.508]

[0.358, 0.338, 0.703, 0.497]

[0.358, 0.338, 0.719, 0.528]

referring_detection

visual_grounding

VAL

Please provide the bounding box coordinates for the described object or area using the format [x1, y1, x2, y2]. Here, [x1, y1] represent the top-left coordinates and [x2, y2] the bottom-right coordinates within a normalized range of 0 to 1, where [0, 0] is the top-left corner and [1, 1] is the bottom-right corner of the image. Note that the width of the input image is 640 and the height is 516. QUESTION:woman on left

A

[0.156, 0.153, 0.453, 0.622]

[0.117, 0.721, 0.245, 0.983]

[0.016, 0.029, 0.312, 0.498]

[0.65, 0.828, 0.697, 0.969]

referring_detection

visual_grounding

VAL

Please provide the bounding box coordinates for the described object or area using the format [x1, y1, x2, y2]. Here, [x1, y1] represent the top-left coordinates and [x2, y2] the bottom-right coordinates within a normalized range of 0 to 1, where [0, 0] is the top-left corner and [1, 1] is the bottom-right corner of the image. Note that the width of the input image is 640 and the height is 432. QUESTION:BEACH UMBRELLA

D

[0.389, 0.537, 0.881, 0.917]

[0.005, 0.014, 0.963, 0.262]

[0.287, 0.495, 0.319, 0.845]

[0.005, 0.014, 0.816, 0.303]

referring_detection

visual_grounding

VAL

Please provide the bounding box coordinates for the described object or area using the format [x1, y1, x2, y2]. Here, [x1, y1] represent the top-left coordinates and [x2, y2] the bottom-right coordinates within a normalized range of 0 to 1, where [0, 0] is the top-left corner and [1, 1] is the bottom-right corner of the image. Note that the width of the input image is 375 and the height is 500. QUESTION:the empty pavilion

C

[0.392, 0.422, 0.808, 0.78]

[0.003, 0.55, 1.112, 0.694]

[0.003, 0.55, 0.941, 0.688]

[0.003, 0.55, 1.032, 0.676]

referring_detection

visual_grounding

VAL

Please provide the bounding box coordinates for the described object or area using the format [x1, y1, x2, y2]. Here, [x1, y1] represent the top-left coordinates and [x2, y2] the bottom-right coordinates within a normalized range of 0 to 1, where [0, 0] is the top-left corner and [1, 1] is the bottom-right corner of the image. Note that the width of the input image is 427 and the height is 640. QUESTION:Water glass on a table

C

[0.04, 0.498, 0.379, 0.692]

[0.614, 0.205, 0.916, 0.62]

[0.101, 0.178, 0.363, 0.388]

[0.101, 0.178, 0.398, 0.37]

referring_detection

visual_grounding

VAL

Please provide the bounding box coordinates for the described object or area using the format [x1, y1, x2, y2]. Here, [x1, y1] represent the top-left coordinates and [x2, y2] the bottom-right coordinates within a normalized range of 0 to 1, where [0, 0] is the top-left corner and [1, 1] is the bottom-right corner of the image. Note that the width of the input image is 640 and the height is 480. QUESTION:The urinal on the left.

D

[0.138, 0.485, 0.333, 0.829]

[0.205, 0.333, 0.422, 0.627]

[0.181, 0.415, 0.644, 0.623]

[0.205, 0.333, 0.4, 0.677]

referring_detection

visual_grounding

VAL

Please provide the bounding box coordinates for the described object or area using the format [x1, y1, x2, y2]. Here, [x1, y1] represent the top-left coordinates and [x2, y2] the bottom-right coordinates within a normalized range of 0 to 1, where [0, 0] is the top-left corner and [1, 1] is the bottom-right corner of the image. Note that the width of the input image is 640 and the height is 480. QUESTION:The bed nearest the window

D

[0.608, 0.481, 0.961, 0.779]

[0.484, 0.577, 0.947, 0.867]

[0.43, 0.554, 0.822, 0.85]

[0.608, 0.481, 1.0, 0.777]

referring_detection

visual_grounding

VAL

Please provide the bounding box coordinates for the described object or area using the format [x1, y1, x2, y2]. Here, [x1, y1] represent the top-left coordinates and [x2, y2] the bottom-right coordinates within a normalized range of 0 to 1, where [0, 0] is the top-left corner and [1, 1] is the bottom-right corner of the image. Note that the width of the input image is 500 and the height is 331. QUESTION:The longest wood skis in the scene being carried.

C

[0.634, 0.296, 0.94, 0.468]

[0.556, 0.299, 0.816, 0.489]

[0.556, 0.299, 0.862, 0.471]

[0.594, 0.381, 0.9, 0.553]

referring_detection

visual_grounding

VAL

Following the structural and analogical relations, which image best completes the problem matrix?

B

Choice 0

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ravens_progressive_matrices

intelligence_quotient_test

VAL

Following the structural and analogical relations, which image best completes the problem matrix?

G

Choice 0

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ravens_progressive_matrices

intelligence_quotient_test

VAL

Following the structural and analogical relations, which image best completes the problem matrix?

G

Choice 0

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ravens_progressive_matrices

intelligence_quotient_test

VAL

Following the structural and analogical relations, which image best completes the problem matrix?

C

Choice 0

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ravens_progressive_matrices

intelligence_quotient_test

VAL

Following the structural and analogical relations, which image best completes the problem matrix?

D

Choice 0

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ravens_progressive_matrices

intelligence_quotient_test

VAL

Following the structural and analogical relations, which image best completes the problem matrix?

H

Choice 0

Choice 1

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ravens_progressive_matrices

intelligence_quotient_test

VAL

Following the structural and analogical relations, which image best completes the problem matrix?

B

Choice 0

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ravens_progressive_matrices

intelligence_quotient_test

VAL

Following the structural and analogical relations, which image best completes the problem matrix?

D

Choice 0

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ravens_progressive_matrices

intelligence_quotient_test

VAL

Following the structural and analogical relations, which image best completes the problem matrix?

A

Choice 0

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ravens_progressive_matrices

intelligence_quotient_test

VAL

Following the structural and analogical relations, which image best completes the problem matrix?

B

Choice 0

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ravens_progressive_matrices

intelligence_quotient_test

VAL

Following the structural and analogical relations, which image best completes the problem matrix?

C

Choice 0

Choice 1

Choice 2

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Choice 4

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ravens_progressive_matrices

intelligence_quotient_test

VAL

Following the structural and analogical relations, which image best completes the problem matrix?

E

Choice 0

Choice 1

Choice 2

Choice 3

Choice 4

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ravens_progressive_matrices

intelligence_quotient_test

VAL

Following the structural and analogical relations, which image best completes the problem matrix?

F

Choice 0

Choice 1

Choice 2

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ravens_progressive_matrices

intelligence_quotient_test

VAL

Following the structural and analogical relations, which image best completes the problem matrix?

E

Choice 0

Choice 1

Choice 2

Choice 3

Choice 4

Choice 5

Choice 6

Choice 7

ravens_progressive_matrices

intelligence_quotient_test

VAL

Following the structural and analogical relations, which image best completes the problem matrix?

H

Choice 0

Choice 1

Choice 2

Choice 3

Choice 4

Choice 5

Choice 6

Choice 7

ravens_progressive_matrices

intelligence_quotient_test

VAL

Following the structural and analogical relations, which image best completes the problem matrix?

C

Choice 0

Choice 1

Choice 2

Choice 3

Choice 4

Choice 5

Choice 6

Choice 7

ravens_progressive_matrices

intelligence_quotient_test

VAL

Following the structural and analogical relations, which image best completes the problem matrix?

D

Choice 0

Choice 1

Choice 2

Choice 3

Choice 4

Choice 5

Choice 6

Choice 7

ravens_progressive_matrices

intelligence_quotient_test

VAL

Following the structural and analogical relations, which image best completes the problem matrix?

E

Choice 0

Choice 1

Choice 2

Choice 3

Choice 4

Choice 5

Choice 6

Choice 7

ravens_progressive_matrices

intelligence_quotient_test

VAL

Following the structural and analogical relations, which image best completes the problem matrix?

G

Choice 0

Choice 1

Choice 2

Choice 3

Choice 4

Choice 5

Choice 6

Choice 7

ravens_progressive_matrices

intelligence_quotient_test

VAL

Following the structural and analogical relations, which image best completes the problem matrix?

E

Choice 0

Choice 1

Choice 2

Choice 3

Choice 4

Choice 5

Choice 6

Choice 7

ravens_progressive_matrices

intelligence_quotient_test

VAL

You are a professional image matting expert. What is the alpha value of the pixel point at coordinates (0.479, 0.921) in the image for image matting purposes? The alpha value represents the degree of transparency of the salient object against the background at this specific pixel. In this context, an alpha value of 0 indicates complete transparency, meaning the pixel is entirely invisible, while an alpha value of 255 represents complete opacity, meaning the pixel is fully visible. The dimensions of the input image are given as 1444 in width and 1080 in height. The coordinates of the top left corner of the image are (0, 0), and those of the bottom right corner are (1.0, 1.0).

B

0

176

255

217

image_matting

pixel_level_perception

VAL

You are a professional image matting expert. What is the alpha value of the pixel point at coordinates (0.157, 1.124) in the image for image matting purposes? The alpha value represents the degree of transparency of the salient object against the background at this specific pixel. In this context, an alpha value of 0 indicates complete transparency, meaning the pixel is entirely invisible, while an alpha value of 255 represents complete opacity, meaning the pixel is fully visible. The dimensions of the input image are given as 1919 in width and 1080 in height. The coordinates of the top left corner of the image are (0, 0), and those of the bottom right corner are (1.0, 1.0).

B

0

4

34

255

image_matting

pixel_level_perception

VAL

You are a professional image matting expert. What is the alpha value of the pixel point at coordinates (1.328, 0.342) in the image for image matting purposes? The alpha value represents the degree of transparency of the salient object against the background at this specific pixel. In this context, an alpha value of 0 indicates complete transparency, meaning the pixel is entirely invisible, while an alpha value of 255 represents complete opacity, meaning the pixel is fully visible. The dimensions of the input image are given as 1080 in width and 1620 in height. The coordinates of the top left corner of the image are (0, 0), and those of the bottom right corner are (1.0, 1.0).

C

250

204

255

0

image_matting

pixel_level_perception

VAL

You are a professional image matting expert. What is the alpha value of the pixel point at coordinates (0.771, 0.557) in the image for image matting purposes? The alpha value represents the degree of transparency of the salient object against the background at this specific pixel. In this context, an alpha value of 0 indicates complete transparency, meaning the pixel is entirely invisible, while an alpha value of 255 represents complete opacity, meaning the pixel is fully visible. The dimensions of the input image are given as 1080 in width and 1439 in height. The coordinates of the top left corner of the image are (0, 0), and those of the bottom right corner are (1.0, 1.0).

A

255

181

0

227

image_matting

pixel_level_perception

VAL

You are a professional image matting expert. What is the alpha value of the pixel point at coordinates (0.894, 0.28) in the image for image matting purposes? The alpha value represents the degree of transparency of the salient object against the background at this specific pixel. In this context, an alpha value of 0 indicates complete transparency, meaning the pixel is entirely invisible, while an alpha value of 255 represents complete opacity, meaning the pixel is fully visible. The dimensions of the input image are given as 1080 in width and 1439 in height. The coordinates of the top left corner of the image are (0, 0), and those of the bottom right corner are (1.0, 1.0).

B

0

6

102

255

image_matting

pixel_level_perception

VAL

You are a professional image matting expert. What is the alpha value of the pixel point at coordinates (0.389, 0.688) in the image for image matting purposes? The alpha value represents the degree of transparency of the salient object against the background at this specific pixel. In this context, an alpha value of 0 indicates complete transparency, meaning the pixel is entirely invisible, while an alpha value of 255 represents complete opacity, meaning the pixel is fully visible. The dimensions of the input image are given as 1620 in width and 1080 in height. The coordinates of the top left corner of the image are (0, 0), and those of the bottom right corner are (1.0, 1.0).

C

9

0

34

255

image_matting

pixel_level_perception

VAL

You are a professional image matting expert. What is the alpha value of the pixel point at coordinates (1.327, 0.274) in the image for image matting purposes? The alpha value represents the degree of transparency of the salient object against the background at this specific pixel. In this context, an alpha value of 0 indicates complete transparency, meaning the pixel is entirely invisible, while an alpha value of 255 represents complete opacity, meaning the pixel is fully visible. The dimensions of the input image are given as 1080 in width and 1619 in height. The coordinates of the top left corner of the image are (0, 0), and those of the bottom right corner are (1.0, 1.0).

B

255

23

131

0

image_matting

pixel_level_perception

VAL

You are a professional image matting expert. What is the alpha value of the pixel point at coordinates (0.252, 1.258) in the image for image matting purposes? The alpha value represents the degree of transparency of the salient object against the background at this specific pixel. In this context, an alpha value of 0 indicates complete transparency, meaning the pixel is entirely invisible, while an alpha value of 255 represents complete opacity, meaning the pixel is fully visible. The dimensions of the input image are given as 1613 in width and 1080 in height. The coordinates of the top left corner of the image are (0, 0), and those of the bottom right corner are (1.0, 1.0).

A

255

0

168

127

image_matting

pixel_level_perception

VAL

You are a professional image matting expert. What is the alpha value of the pixel point at coordinates (0.973, 0.428) in the image for image matting purposes? The alpha value represents the degree of transparency of the salient object against the background at this specific pixel. In this context, an alpha value of 0 indicates complete transparency, meaning the pixel is entirely invisible, while an alpha value of 255 represents complete opacity, meaning the pixel is fully visible. The dimensions of the input image are given as 1080 in width and 1630 in height. The coordinates of the top left corner of the image are (0, 0), and those of the bottom right corner are (1.0, 1.0).

B

219

254

0

255

image_matting

pixel_level_perception

VAL

You are a professional image matting expert. What is the alpha value of the pixel point at coordinates (0.379, 0.574) in the image for image matting purposes? The alpha value represents the degree of transparency of the salient object against the background at this specific pixel. In this context, an alpha value of 0 indicates complete transparency, meaning the pixel is entirely invisible, while an alpha value of 255 represents complete opacity, meaning the pixel is fully visible. The dimensions of the input image are given as 1620 in width and 1080 in height. The coordinates of the top left corner of the image are (0, 0), and those of the bottom right corner are (1.0, 1.0).

A

107

0

77

255

image_matting

pixel_level_perception

VAL

You are a professional image matting expert. What is the alpha value of the pixel point at coordinates (1.144, 0.43) in the image for image matting purposes? The alpha value represents the degree of transparency of the salient object against the background at this specific pixel. In this context, an alpha value of 0 indicates complete transparency, meaning the pixel is entirely invisible, while an alpha value of 255 represents complete opacity, meaning the pixel is fully visible. The dimensions of the input image are given as 1080 in width and 1440 in height. The coordinates of the top left corner of the image are (0, 0), and those of the bottom right corner are (1.0, 1.0).

C

238

101

255

0

image_matting

pixel_level_perception

VAL

You are a professional image matting expert. What is the alpha value of the pixel point at coordinates (0.581, 0.94) in the image for image matting purposes? The alpha value represents the degree of transparency of the salient object against the background at this specific pixel. In this context, an alpha value of 0 indicates complete transparency, meaning the pixel is entirely invisible, while an alpha value of 255 represents complete opacity, meaning the pixel is fully visible. The dimensions of the input image are given as 1620 in width and 1080 in height. The coordinates of the top left corner of the image are (0, 0), and those of the bottom right corner are (1.0, 1.0).

A

0

42

255

37

image_matting

pixel_level_perception

VAL

You are a professional image matting expert. What is the alpha value of the pixel point at coordinates (0.62, 1.488) in the image for image matting purposes? The alpha value represents the degree of transparency of the salient object against the background at this specific pixel. In this context, an alpha value of 0 indicates complete transparency, meaning the pixel is entirely invisible, while an alpha value of 255 represents complete opacity, meaning the pixel is fully visible. The dimensions of the input image are given as 1620 in width and 1080 in height. The coordinates of the top left corner of the image are (0, 0), and those of the bottom right corner are (1.0, 1.0).

D

198

112

255

0

image_matting

pixel_level_perception

VAL

You are a professional image matting expert. What is the alpha value of the pixel point at coordinates (0.63, 0.881) in the image for image matting purposes? The alpha value represents the degree of transparency of the salient object against the background at this specific pixel. In this context, an alpha value of 0 indicates complete transparency, meaning the pixel is entirely invisible, while an alpha value of 255 represents complete opacity, meaning the pixel is fully visible. The dimensions of the input image are given as 1620 in width and 1080 in height. The coordinates of the top left corner of the image are (0, 0), and those of the bottom right corner are (1.0, 1.0).

D

255

177

0

254

image_matting

pixel_level_perception

VAL

You are a professional image matting expert. What is the alpha value of the pixel point at coordinates (0.245, 1.096) in the image for image matting purposes? The alpha value represents the degree of transparency of the salient object against the background at this specific pixel. In this context, an alpha value of 0 indicates complete transparency, meaning the pixel is entirely invisible, while an alpha value of 255 represents complete opacity, meaning the pixel is fully visible. The dimensions of the input image are given as 1920 in width and 1080 in height. The coordinates of the top left corner of the image are (0, 0), and those of the bottom right corner are (1.0, 1.0).

D

16

170

0

255

image_matting

pixel_level_perception

VAL

You are a professional image matting expert. What is the alpha value of the pixel point at coordinates (0.622, 0.216) in the image for image matting purposes? The alpha value represents the degree of transparency of the salient object against the background at this specific pixel. In this context, an alpha value of 0 indicates complete transparency, meaning the pixel is entirely invisible, while an alpha value of 255 represents complete opacity, meaning the pixel is fully visible. The dimensions of the input image are given as 1080 in width and 1655 in height. The coordinates of the top left corner of the image are (0, 0), and those of the bottom right corner are (1.0, 1.0).

D

0

10

255

254

image_matting

pixel_level_perception

VAL

You are a professional image matting expert. What is the alpha value of the pixel point at coordinates (0.88, 0.535) in the image for image matting purposes? The alpha value represents the degree of transparency of the salient object against the background at this specific pixel. In this context, an alpha value of 0 indicates complete transparency, meaning the pixel is entirely invisible, while an alpha value of 255 represents complete opacity, meaning the pixel is fully visible. The dimensions of the input image are given as 1080 in width and 1620 in height. The coordinates of the top left corner of the image are (0, 0), and those of the bottom right corner are (1.0, 1.0).

D

255

43

181

0

image_matting

pixel_level_perception

VAL

You are a professional image matting expert. What is the alpha value of the pixel point at coordinates (0.255, 0.334) in the image for image matting purposes? The alpha value represents the degree of transparency of the salient object against the background at this specific pixel. In this context, an alpha value of 0 indicates complete transparency, meaning the pixel is entirely invisible, while an alpha value of 255 represents complete opacity, meaning the pixel is fully visible. The dimensions of the input image are given as 1618 in width and 1080 in height. The coordinates of the top left corner of the image are (0, 0), and those of the bottom right corner are (1.0, 1.0).

D

241

255

0

247

image_matting

pixel_level_perception

VAL

You are a professional image matting expert. What is the alpha value of the pixel point at coordinates (0.927, 0.408) in the image for image matting purposes? The alpha value represents the degree of transparency of the salient object against the background at this specific pixel. In this context, an alpha value of 0 indicates complete transparency, meaning the pixel is entirely invisible, while an alpha value of 255 represents complete opacity, meaning the pixel is fully visible. The dimensions of the input image are given as 1080 in width and 1437 in height. The coordinates of the top left corner of the image are (0, 0), and those of the bottom right corner are (1.0, 1.0).

D

0

255

84

243

image_matting

pixel_level_perception

VAL

You are a professional image matting expert. What is the alpha value of the pixel point at coordinates (0.196, 0.894) in the image for image matting purposes? The alpha value represents the degree of transparency of the salient object against the background at this specific pixel. In this context, an alpha value of 0 indicates complete transparency, meaning the pixel is entirely invisible, while an alpha value of 255 represents complete opacity, meaning the pixel is fully visible. The dimensions of the input image are given as 1620 in width and 1080 in height. The coordinates of the top left corner of the image are (0, 0), and those of the bottom right corner are (1.0, 1.0).

C

255

0

254

143

image_matting

pixel_level_perception

VAL

What is the depth (in meters) at the coordinates (0.225, 1.125) in the figure? The camera intrinsic parameters are as follows, Focal Length: 518.8579, Principal Point: (519.46961, 325.58245), Distortion Parameters: 253.73617

C

1.403

0.091

0.783

0.101

depth_estimation

pixel_level_perception

VAL

What is the depth (in meters) at the coordinates (0.464, 1.144) in the figure? The camera intrinsic parameters are as follows, Focal Length: 518.8579, Principal Point: (519.46961, 325.58245), Distortion Parameters: 253.73617

B

2.179

3.213

5.608

4.223

depth_estimation

pixel_level_perception

VAL

What is the depth (in meters) at the coordinates (0.414, 1.21) in the figure? The camera intrinsic parameters are as follows, Focal Length: 518.8579, Principal Point: (519.46961, 325.58245), Distortion Parameters: 253.73617

D

5.661

0.96

3.897

3.603

depth_estimation

pixel_level_perception

VAL

What is the depth (in meters) at the coordinates (0.18, 0.715) in the figure? The camera intrinsic parameters are as follows, Focal Length: 721.5377, Principal Point: (721.5377, 609.5593), Distortion Parameters: 172.854

A

27.1640625

19.926

53.428

31.937

depth_estimation

pixel_level_perception

VAL

What is the depth (in meters) at the coordinates (0.225, 1.259) in the figure? The camera intrinsic parameters are as follows, Focal Length: 721.5377, Principal Point: (721.5377, 609.5593), Distortion Parameters: 172.854

A

11.56640625

17.426

20.768

11.259

depth_estimation

pixel_level_perception

VAL

What is the depth (in meters) at the coordinates (0.24, 2.88) in the figure? The camera intrinsic parameters are as follows, Focal Length: 721.5377, Principal Point: (721.5377, 609.5593), Distortion Parameters: 172.854

B

4.458

8.50390625

4.764

12.443

depth_estimation

pixel_level_perception

VAL

What is the depth (in meters) at the coordinates (0.199, 2.096) in the figure? The camera intrinsic parameters are as follows, Focal Length: 721.5377, Principal Point: (721.5377, 609.5593), Distortion Parameters: 172.854

B

3.413

12.7734375

2.555

9.476

depth_estimation

pixel_level_perception

VAL

What is the depth (in meters) at the coordinates (0.218, 1.465) in the figure? The camera intrinsic parameters are as follows, Focal Length: 721.5377, Principal Point: (721.5377, 609.5593), Distortion Parameters: 172.854

D

4.312

18.945

20.441

12.02734375

depth_estimation

pixel_level_perception

VAL

What is the depth (in meters) at the coordinates (0.279, 1.661) in the figure? The camera intrinsic parameters are as follows, Focal Length: 721.5377, Principal Point: (721.5377, 609.5593), Distortion Parameters: 172.854

A

6.80078125

2.975

7.84

5.655

depth_estimation

pixel_level_perception

VAL

What is the depth (in meters) at the coordinates (0.169, 1.472) in the figure? The camera intrinsic parameters are as follows, Focal Length: 721.5377, Principal Point: (721.5377, 609.5593), Distortion Parameters: 172.854

A

32.703125

25.846

30.798

28.812

depth_estimation

pixel_level_perception

VAL

What is the depth (in meters) at the coordinates (0.211, 2.043) in the figure? The camera intrinsic parameters are as follows, Focal Length: 721.5377, Principal Point: (721.5377, 609.5593), Distortion Parameters: 172.854

D

3.677

0.368

22.269

11.78125

depth_estimation

pixel_level_perception

VAL

What is the depth (in meters) at the coordinates (0.224, 2.947) in the figure? The camera intrinsic parameters are as follows, Focal Length: 721.5377, Principal Point: (721.5377, 609.5593), Distortion Parameters: 172.854

B

12.785

11.17578125

21.288

15.106

depth_estimation

pixel_level_perception

VAL

What is the depth (in meters) at the coordinates (0.25, 1.216) in the figure? The camera intrinsic parameters are as follows, Focal Length: 721.5377, Principal Point: (721.5377, 609.5593), Distortion Parameters: 172.854

D

1.848

13.267

6.072

8.4765625

depth_estimation

pixel_level_perception

VAL

What is the depth (in meters) at the coordinates (0.62, 1.129) in the figure? The camera intrinsic parameters are as follows, Focal Length: 518.8579, Principal Point: (519.46961, 325.58245), Distortion Parameters: 253.73617

B

2.002

1.279

2.213

1.074

depth_estimation

pixel_level_perception

VAL

What is the depth (in meters) at the coordinates (0.2, 2.147) in the figure? The camera intrinsic parameters are as follows, Focal Length: 721.5377, Principal Point: (721.5377, 609.5593), Distortion Parameters: 172.854

D

11.525

25.068

17.117

14.87109375

depth_estimation

pixel_level_perception

VAL

What is the depth (in meters) at the coordinates (0.212, 2.131) in the figure? The camera intrinsic parameters are as follows, Focal Length: 721.5377, Principal Point: (721.5377, 609.5593), Distortion Parameters: 172.854

D

14.065

23.368

22.82

13.359375

depth_estimation

pixel_level_perception

VAL

What is the depth (in meters) at the coordinates (0.252, 1.936) in the figure? The camera intrinsic parameters are as follows, Focal Length: 721.5377, Principal Point: (721.5377, 609.5593), Distortion Parameters: 172.854

C

11.501

12.198

8.7578125

0.145

depth_estimation

pixel_level_perception

VAL

What is the depth (in meters) at the coordinates (0.171, 0.792) in the figure? The camera intrinsic parameters are as follows, Focal Length: 721.5377, Principal Point: (721.5377, 609.5593), Distortion Parameters: 172.854

B

12.081

31.93359375

18.879

13.233

depth_estimation

pixel_level_perception

VAL

What is the depth (in meters) at the coordinates (0.164, 1.315) in the figure? The camera intrinsic parameters are as follows, Focal Length: 721.5377, Principal Point: (721.5377, 609.5593), Distortion Parameters: 172.854

D

12.6

2.697

8.668

10.3828125

depth_estimation

pixel_level_perception

VAL

What is the depth (in meters) at the coordinates (0.259, 1.181) in the figure? The camera intrinsic parameters are as follows, Focal Length: 721.5377, Principal Point: (721.5377, 609.5593), Distortion Parameters: 172.854

C

4.899

14.012

7.96875

3.777

depth_estimation

pixel_level_perception

VAL

Please detect all instances of the following categories in this image: person, bicycle, car, motorcycle, airplane, bus, train, truck, boat, traffic light, fire hydrant, stop sign, parking meter, bench, bird, cat, dog, horse, sheep, cow, elephant, bear, zebra, giraffe, backpack, umbrella, handbag, tie, suitcase, frisbee, skis, snowboard, sports ball, kite, baseball bat, baseball glove, skateboard, surfboard, tennis racket, bottle, wine glass, cup, fork, knife, spoon, bowl, banana, apple, sandwich, orange, broccoli, carrot, hot dog, pizza, donut, cake, chair, couch, potted plant, bed, dining table, toilet, tv, laptop, mouse, remote, keyboard, cell phone, microwave, oven, toaster, sink, refrigerator, book, clock, vase, scissors, teddy bear, hair drier, toothbrush. For each detected object, provide the output in the format: category: ((x1, y1), (x2, y2)). The point (x1, y1) represents a coordinate on the detected object, and the point (x2, y2) represents a coordinate outside the detected object. Note that the width of the input image is given as 640 and the height as 640.

B

('train', '450', '217', '113', '182'): [(0.703, 0.339), (0.177, 0.284)], ('train', '450', '217', '113', '182'): [(0.247, 0.836), (0.428, 0.031)]

('train', '260', '88', '416', '364'): [(0.406, 0.138), (0.65, 0.569)], ('train', '260', '88', '416', '364'): [(0.955, 0.431), (0.369, 0.775)]

('train', '595', '165', '142', '141'): [(0.93, 0.258), (0.222, 0.22)], ('train', '595', '165', '142', '141'): [(0.766, 0.548), (0.708, 0.723)]

('train', '277', '211', '135', '62'): [(0.433, 0.33), (0.211, 0.097)], ('train', '277', '211', '135', '62'): [(0.244, 0.669), (0.087, 0.791)]

pixel_localization

pixel_level_perception

VAL

Please detect all instances of the following categories in this image: person, bicycle, car, motorcycle, airplane, bus, train, truck, boat, traffic light, fire hydrant, stop sign, parking meter, bench, bird, cat, dog, horse, sheep, cow, elephant, bear, zebra, giraffe, backpack, umbrella, handbag, tie, suitcase, frisbee, skis, snowboard, sports ball, kite, baseball bat, baseball glove, skateboard, surfboard, tennis racket, bottle, wine glass, cup, fork, knife, spoon, bowl, banana, apple, sandwich, orange, broccoli, carrot, hot dog, pizza, donut, cake, chair, couch, potted plant, bed, dining table, toilet, tv, laptop, mouse, remote, keyboard, cell phone, microwave, oven, toaster, sink, refrigerator, book, clock, vase, scissors, teddy bear, hair drier, toothbrush. For each detected object, provide the output in the format: category: ((x1, y1), (x2, y2)). The point (x1, y1) represents a coordinate on the detected object, and the point (x2, y2) represents a coordinate outside the detected object. Note that the width of the input image is given as 640 and the height as 480.

B

('bus', '21', '285', '328', '134'): [(0.033, 0.594), (0.512, 0.279)], ('bus', '21', '285', '328', '134'): [(0.38, 0.579), (0.377, 0.585)]

('bus', '202', '369', '410', '381'): [(0.316, 0.769), (0.641, 0.794)], ('bus', '202', '369', '410', '381'): [(0.372, 0.621), (0.034, 1.085)]

('bus', '356', '420', '244', '281'): [(0.556, 0.875), (0.381, 0.585)], ('bus', '356', '420', '244', '281'): [(0.372, 0.621), (0.034, 1.085)]

('bus', '215', '424', '240', '298'): [(0.336, 0.883), (0.375, 0.621)], ('bus', '215', '424', '240', '298'): [(0.372, 0.623), (0.383, 0.61)]

pixel_localization

pixel_level_perception

VAL

Please detect all instances of the following categories in this image: person, bicycle, car, motorcycle, airplane, bus, train, truck, boat, traffic light, fire hydrant, stop sign, parking meter, bench, bird, cat, dog, horse, sheep, cow, elephant, bear, zebra, giraffe, backpack, umbrella, handbag, tie, suitcase, frisbee, skis, snowboard, sports ball, kite, baseball bat, baseball glove, skateboard, surfboard, tennis racket, bottle, wine glass, cup, fork, knife, spoon, bowl, banana, apple, sandwich, orange, broccoli, carrot, hot dog, pizza, donut, cake, chair, couch, potted plant, bed, dining table, toilet, tv, laptop, mouse, remote, keyboard, cell phone, microwave, oven, toaster, sink, refrigerator, book, clock, vase, scissors, teddy bear, hair drier, toothbrush. For each detected object, provide the output in the format: category: ((x1, y1), (x2, y2)). The point (x1, y1) represents a coordinate on the detected object, and the point (x2, y2) represents a coordinate outside the detected object. Note that the width of the input image is given as 640 and the height as 480.

A

('clock', '331', '208', '454', '14'): [(0.517, 0.433), (0.709, 0.029)], ('clock', '331', '208', '454', '14'): [(0.483, 1.085), (0.669, 1.304)]

('clock', '226', '119', '161', '1'): [(0.353, 0.248), (0.252, 0.002)], ('clock', '226', '119', '161', '1'): [(0.359, 1.308), (0.586, 0.458)]

('clock', '346', '519', '94', '358'): [(0.541, 1.081), (0.147, 0.746)], ('clock', '346', '519', '94', '358'): [(0.616, 0.938), (0.453, 0.423)]

('clock', '303', '580', '408', '519'): [(0.473, 1.208), (0.637, 1.081)], ('clock', '303', '580', '408', '519'): [(0.731, 0.317), (0.527, 0.456)]

pixel_localization

pixel_level_perception

VAL

Please detect all instances of the following categories in this image: person, bicycle, car, motorcycle, airplane, bus, train, truck, boat, traffic light, fire hydrant, stop sign, parking meter, bench, bird, cat, dog, horse, sheep, cow, elephant, bear, zebra, giraffe, backpack, umbrella, handbag, tie, suitcase, frisbee, skis, snowboard, sports ball, kite, baseball bat, baseball glove, skateboard, surfboard, tennis racket, bottle, wine glass, cup, fork, knife, spoon, bowl, banana, apple, sandwich, orange, broccoli, carrot, hot dog, pizza, donut, cake, chair, couch, potted plant, bed, dining table, toilet, tv, laptop, mouse, remote, keyboard, cell phone, microwave, oven, toaster, sink, refrigerator, book, clock, vase, scissors, teddy bear, hair drier, toothbrush. For each detected object, provide the output in the format: category: ((x1, y1), (x2, y2)). The point (x1, y1) represents a coordinate on the detected object, and the point (x2, y2) represents a coordinate outside the detected object. Note that the width of the input image is given as 427 and the height as 640.

B

('person', '529', '201', '225', '268'): [(1.239, 0.314), (0.527, 0.419)], ('person', '529', '201', '225', '268'): [(1.192, 0.414), (0.475, 0.328)]

('person', '183', '288', '94', '71'): [(0.429, 0.45), (0.22, 0.111)], ('person', '183', '288', '94', '71'): [(0.602, 0.034), (0.096, 0.584)]

('person', '357', '78', '456', '114'): [(0.836, 0.122), (1.068, 0.178)], ('person', '357', '78', '456', '114'): [(1.159, 0.031), (0.993, 0.047)]

('person', '82', '310', '141', '213'): [(0.192, 0.484), (0.33, 0.333)], ('person', '82', '310', '141', '213'): [(0.246, 0.305), (1.227, 0.469)]

pixel_localization

pixel_level_perception

VAL

Please detect all instances of the following categories in this image: person, bicycle, car, motorcycle, airplane, bus, train, truck, boat, traffic light, fire hydrant, stop sign, parking meter, bench, bird, cat, dog, horse, sheep, cow, elephant, bear, zebra, giraffe, backpack, umbrella, handbag, tie, suitcase, frisbee, skis, snowboard, sports ball, kite, baseball bat, baseball glove, skateboard, surfboard, tennis racket, bottle, wine glass, cup, fork, knife, spoon, bowl, banana, apple, sandwich, orange, broccoli, carrot, hot dog, pizza, donut, cake, chair, couch, potted plant, bed, dining table, toilet, tv, laptop, mouse, remote, keyboard, cell phone, microwave, oven, toaster, sink, refrigerator, book, clock, vase, scissors, teddy bear, hair drier, toothbrush. For each detected object, provide the output in the format: category: ((x1, y1), (x2, y2)). The point (x1, y1) represents a coordinate on the detected object, and the point (x2, y2) represents a coordinate outside the detected object. Note that the width of the input image is given as 640 and the height as 427.

D

('airplane', '207', '255', '310', '244'): [(0.323, 0.597), (0.484, 0.571)]

('airplane', '237', '87', '227', '87'): [(0.37, 0.204), (0.355, 0.204)]

('airplane', '267', '293', '228', '311'): [(0.417, 0.686), (0.356, 0.728)]

('airplane', '235', '203', '407', '370'): [(0.367, 0.475), (0.636, 0.867)]

pixel_localization

pixel_level_perception

VAL

Please detect all instances of the following categories in this image: person, bicycle, car, motorcycle, airplane, bus, train, truck, boat, traffic light, fire hydrant, stop sign, parking meter, bench, bird, cat, dog, horse, sheep, cow, elephant, bear, zebra, giraffe, backpack, umbrella, handbag, tie, suitcase, frisbee, skis, snowboard, sports ball, kite, baseball bat, baseball glove, skateboard, surfboard, tennis racket, bottle, wine glass, cup, fork, knife, spoon, bowl, banana, apple, sandwich, orange, broccoli, carrot, hot dog, pizza, donut, cake, chair, couch, potted plant, bed, dining table, toilet, tv, laptop, mouse, remote, keyboard, cell phone, microwave, oven, toaster, sink, refrigerator, book, clock, vase, scissors, teddy bear, hair drier, toothbrush. For each detected object, provide the output in the format: category: ((x1, y1), (x2, y2)). The point (x1, y1) represents a coordinate on the detected object, and the point (x2, y2) represents a coordinate outside the detected object. Note that the width of the input image is given as 640 and the height as 427.

D

('person', '186', '294', '49', '106'): [(0.291, 0.689), (0.077, 0.248)], ('person', '186', '294', '49', '106'): [(0.464, 0.782), (0.481, 0.794)]

('person', '185', '321', '210', '302'): [(0.289, 0.752), (0.328, 0.707)], ('person', '185', '321', '210', '302'): [(0.005, 0.159), (0.014, 0.384)]

('person', '280', '550', '62', '570'): [(0.438, 1.288), (0.097, 1.335)], ('person', '280', '550', '62', '570'): [(0.545, 0.363), (0.614, 0.438)]

('person', '186', '294', '49', '106'): [(0.291, 0.689), (0.077, 0.248)], ('person', '186', '294', '49', '106'): [(0.478, 0.803), (0.059, 0.225)]

pixel_localization

pixel_level_perception

VAL

Please detect all instances of the following categories in this image: person, bicycle, car, motorcycle, airplane, bus, train, truck, boat, traffic light, fire hydrant, stop sign, parking meter, bench, bird, cat, dog, horse, sheep, cow, elephant, bear, zebra, giraffe, backpack, umbrella, handbag, tie, suitcase, frisbee, skis, snowboard, sports ball, kite, baseball bat, baseball glove, skateboard, surfboard, tennis racket, bottle, wine glass, cup, fork, knife, spoon, bowl, banana, apple, sandwich, orange, broccoli, carrot, hot dog, pizza, donut, cake, chair, couch, potted plant, bed, dining table, toilet, tv, laptop, mouse, remote, keyboard, cell phone, microwave, oven, toaster, sink, refrigerator, book, clock, vase, scissors, teddy bear, hair drier, toothbrush. For each detected object, provide the output in the format: category: ((x1, y1), (x2, y2)). The point (x1, y1) represents a coordinate on the detected object, and the point (x2, y2) represents a coordinate outside the detected object. Note that the width of the input image is given as 500 and the height as 375.

D

('cat', '327', '314', '275', '271'): [(0.654, 0.837), (0.55, 0.723)], ('cat', '327', '314', '275', '271'): [(0.476, 0.147), (0.486, 0.885)]

('cat', '147', '430', '124', '263'): [(0.294, 1.147), (0.248, 0.701)], ('cat', '147', '430', '124', '263'): [(0.008, 0.389), (0.178, 0.304)]

('cat', '240', '324', '271', '337'): [(0.48, 0.864), (0.542, 0.899)], ('cat', '240', '324', '271', '337'): [(0.28, 0.048), (0.536, 0.832)]

('cat', '244', '271', '263', '171'): [(0.488, 0.723), (0.526, 0.456)], ('cat', '244', '271', '263', '171'): [(0.252, 0.827), (0.438, 0.44)]

pixel_localization

pixel_level_perception

VAL

Please detect all instances of the following categories in this image: person, bicycle, car, motorcycle, airplane, bus, train, truck, boat, traffic light, fire hydrant, stop sign, parking meter, bench, bird, cat, dog, horse, sheep, cow, elephant, bear, zebra, giraffe, backpack, umbrella, handbag, tie, suitcase, frisbee, skis, snowboard, sports ball, kite, baseball bat, baseball glove, skateboard, surfboard, tennis racket, bottle, wine glass, cup, fork, knife, spoon, bowl, banana, apple, sandwich, orange, broccoli, carrot, hot dog, pizza, donut, cake, chair, couch, potted plant, bed, dining table, toilet, tv, laptop, mouse, remote, keyboard, cell phone, microwave, oven, toaster, sink, refrigerator, book, clock, vase, scissors, teddy bear, hair drier, toothbrush. For each detected object, provide the output in the format: category: ((x1, y1), (x2, y2)). The point (x1, y1) represents a coordinate on the detected object, and the point (x2, y2) represents a coordinate outside the detected object. Note that the width of the input image is given as 640 and the height as 512.

C

('bird', '408', '173', '282', '409'): [(0.637, 0.338), (0.441, 0.799)]

('bird', '190', '434', '112', '191'): [(0.297, 0.848), (0.175, 0.373)]

('bird', '325', '464', '87', '466'): [(0.508, 0.906), (0.136, 0.91)]

('bird', '41', '360', '167', '92'): [(0.064, 0.703), (0.261, 0.18)]

pixel_localization

pixel_level_perception

VAL

Please detect all instances of the following categories in this image: person, bicycle, car, motorcycle, airplane, bus, train, truck, boat, traffic light, fire hydrant, stop sign, parking meter, bench, bird, cat, dog, horse, sheep, cow, elephant, bear, zebra, giraffe, backpack, umbrella, handbag, tie, suitcase, frisbee, skis, snowboard, sports ball, kite, baseball bat, baseball glove, skateboard, surfboard, tennis racket, bottle, wine glass, cup, fork, knife, spoon, bowl, banana, apple, sandwich, orange, broccoli, carrot, hot dog, pizza, donut, cake, chair, couch, potted plant, bed, dining table, toilet, tv, laptop, mouse, remote, keyboard, cell phone, microwave, oven, toaster, sink, refrigerator, book, clock, vase, scissors, teddy bear, hair drier, toothbrush. For each detected object, provide the output in the format: category: ((x1, y1), (x2, y2)). The point (x1, y1) represents a coordinate on the detected object, and the point (x2, y2) represents a coordinate outside the detected object. Note that the width of the input image is given as 640 and the height as 427.

D

('bear', '409', '279', '168', '201'): [(0.639, 0.653), (0.263, 0.471)]

('bear', '263', '138', '373', '163'): [(0.411, 0.323), (0.583, 0.382)]

('bear', '43', '268', '208', '82'): [(0.067, 0.628), (0.325, 0.192)]

('bear', '265', '95', '57', '45'): [(0.414, 0.222), (0.089, 0.105)]

pixel_localization

pixel_level_perception

VAL

Please detect all instances of the following categories in this image: person, bicycle, car, motorcycle, airplane, bus, train, truck, boat, traffic light, fire hydrant, stop sign, parking meter, bench, bird, cat, dog, horse, sheep, cow, elephant, bear, zebra, giraffe, backpack, umbrella, handbag, tie, suitcase, frisbee, skis, snowboard, sports ball, kite, baseball bat, baseball glove, skateboard, surfboard, tennis racket, bottle, wine glass, cup, fork, knife, spoon, bowl, banana, apple, sandwich, orange, broccoli, carrot, hot dog, pizza, donut, cake, chair, couch, potted plant, bed, dining table, toilet, tv, laptop, mouse, remote, keyboard, cell phone, microwave, oven, toaster, sink, refrigerator, book, clock, vase, scissors, teddy bear, hair drier, toothbrush. For each detected object, provide the output in the format: category: ((x1, y1), (x2, y2)). The point (x1, y1) represents a coordinate on the detected object, and the point (x2, y2) represents a coordinate outside the detected object. Note that the width of the input image is given as 426 and the height as 640.

A

('tie', '627', '226', '97', '261'): [(1.472, 0.353), (0.228, 0.408)], ('tie', '627', '226', '97', '261'): [(0.258, 0.548), (0.009, 0.655)], ('tie', '627', '226', '97', '261'): [(1.446, 0.123), (0.869, 0.603)]

('tie', '267', '183', '249', '291'): [(0.627, 0.286), (0.585, 0.455)], ('tie', '267', '183', '249', '291'): [(0.953, 0.492), (1.408, 0.25)], ('tie', '267', '183', '249', '291'): [(1.392, 0.147), (1.46, 0.169)]

('tie', '525', '226', '549', '216'): [(1.232, 0.353), (1.289, 0.338)], ('tie', '525', '226', '549', '216'): [(0.345, 0.236), (0.042, 0.114)], ('tie', '525', '226', '549', '216'): [(1.369, 0.173), (1.472, 0.119)]

('tie', '584', '216', '504', '239'): [(1.371, 0.338), (1.183, 0.373)], ('tie', '584', '216', '504', '239'): [(0.103, 0.603), (0.101, 0.661)], ('tie', '584', '216', '504', '239'): [(1.484, 0.097), (0.915, 0.188)]

pixel_localization

pixel_level_perception

VAL

Please detect all instances of the following categories in this image: person, bicycle, car, motorcycle, airplane, bus, train, truck, boat, traffic light, fire hydrant, stop sign, parking meter, bench, bird, cat, dog, horse, sheep, cow, elephant, bear, zebra, giraffe, backpack, umbrella, handbag, tie, suitcase, frisbee, skis, snowboard, sports ball, kite, baseball bat, baseball glove, skateboard, surfboard, tennis racket, bottle, wine glass, cup, fork, knife, spoon, bowl, banana, apple, sandwich, orange, broccoli, carrot, hot dog, pizza, donut, cake, chair, couch, potted plant, bed, dining table, toilet, tv, laptop, mouse, remote, keyboard, cell phone, microwave, oven, toaster, sink, refrigerator, book, clock, vase, scissors, teddy bear, hair drier, toothbrush. For each detected object, provide the output in the format: category: ((x1, y1), (x2, y2)). The point (x1, y1) represents a coordinate on the detected object, and the point (x2, y2) represents a coordinate outside the detected object. Note that the width of the input image is given as 612 and the height as 612.

C

('cow', '77', '129', '422', '403'): [(0.126, 0.211), (0.69, 0.658)], ('cow', '77', '129', '422', '403'): [(0.693, 0.663), (0.699, 0.582)], ('cow', '77', '129', '422', '403'): [(0.327, 0.843), (0.498, 0.358)]

('cow', '65', '77', '157', '206'): [(0.106, 0.126), (0.257, 0.337)], ('cow', '65', '77', '157', '206'): [(0.252, 0.773), (0.404, 0.003)], ('cow', '65', '77', '157', '206'): [(0.258, 0.391), (0.374, 0.523)]

('cow', '355', '252', '541', '28'): [(0.58, 0.412), (0.884, 0.046)], ('cow', '355', '252', '541', '28'): [(0.252, 0.773), (0.404, 0.003)], ('cow', '355', '252', '541', '28'): [(0.258, 0.391), (0.374, 0.523)]

('cow', '63', '356', '161', '210'): [(0.103, 0.582), (0.263, 0.343)], ('cow', '63', '356', '161', '210'): [(0.252, 0.773), (0.404, 0.003)], ('cow', '63', '356', '161', '210'): [(0.248, 0.355), (0.243, 0.381)]

pixel_localization

pixel_level_perception

VAL

Please detect all instances of the following categories in this image: person, bicycle, car, motorcycle, airplane, bus, train, truck, boat, traffic light, fire hydrant, stop sign, parking meter, bench, bird, cat, dog, horse, sheep, cow, elephant, bear, zebra, giraffe, backpack, umbrella, handbag, tie, suitcase, frisbee, skis, snowboard, sports ball, kite, baseball bat, baseball glove, skateboard, surfboard, tennis racket, bottle, wine glass, cup, fork, knife, spoon, bowl, banana, apple, sandwich, orange, broccoli, carrot, hot dog, pizza, donut, cake, chair, couch, potted plant, bed, dining table, toilet, tv, laptop, mouse, remote, keyboard, cell phone, microwave, oven, toaster, sink, refrigerator, book, clock, vase, scissors, teddy bear, hair drier, toothbrush. For each detected object, provide the output in the format: category: ((x1, y1), (x2, y2)). The point (x1, y1) represents a coordinate on the detected object, and the point (x2, y2) represents a coordinate outside the detected object. Note that the width of the input image is given as 385 and the height as 640.

C

('tie', '228', '170', '248', '174'): [(0.592, 0.266), (0.644, 0.272)], ('tie', '228', '170', '248', '174'): [(1.018, 0.402), (1.501, 0.103)], ('tie', '228', '170', '248', '174'): [(1.101, 0.098), (1.358, 0.395)]

('tie', '13', '297', '423', '208'): [(0.034, 0.464), (1.099, 0.325)], ('tie', '13', '297', '423', '208'): [(0.27, 0.028), (0.735, 0.259)], ('tie', '13', '297', '423', '208'): [(1.021, 0.109), (0.766, 0.258)]

('tie', '208', '164', '2', '104'): [(0.54, 0.256), (0.005, 0.163)], ('tie', '208', '164', '2', '104'): [(1.018, 0.402), (1.501, 0.103)], ('tie', '208', '164', '2', '104'): [(1.101, 0.098), (1.358, 0.395)]

('tie', '344', '250', '213', '166'): [(0.894, 0.391), (0.553, 0.259)], ('tie', '344', '250', '213', '166'): [(0.87, 0.438), (0.621, 0.391)], ('tie', '344', '250', '213', '166'): [(0.091, 0.4), (1.512, 0.369)]

pixel_localization

pixel_level_perception

VAL

Please detect all instances of the following categories in this image: person, bicycle, car, motorcycle, airplane, bus, train, truck, boat, traffic light, fire hydrant, stop sign, parking meter, bench, bird, cat, dog, horse, sheep, cow, elephant, bear, zebra, giraffe, backpack, umbrella, handbag, tie, suitcase, frisbee, skis, snowboard, sports ball, kite, baseball bat, baseball glove, skateboard, surfboard, tennis racket, bottle, wine glass, cup, fork, knife, spoon, bowl, banana, apple, sandwich, orange, broccoli, carrot, hot dog, pizza, donut, cake, chair, couch, potted plant, bed, dining table, toilet, tv, laptop, mouse, remote, keyboard, cell phone, microwave, oven, toaster, sink, refrigerator, book, clock, vase, scissors, teddy bear, hair drier, toothbrush. For each detected object, provide the output in the format: category: ((x1, y1), (x2, y2)). The point (x1, y1) represents a coordinate on the detected object, and the point (x2, y2) represents a coordinate outside the detected object. Note that the width of the input image is given as 424 and the height as 640.

C

('clock', '607', '250', '620', '157'): [(1.432, 0.391), (1.462, 0.245)]

('clock', '345', '308', '242', '218'): [(0.814, 0.481), (0.571, 0.341)]

('clock', '241', '203', '201', '197'): [(0.568, 0.317), (0.474, 0.308)]

('clock', '247', '216', '238', '205'): [(0.583, 0.338), (0.561, 0.32)]

pixel_localization

pixel_level_perception

VAL

Please detect all instances of the following categories in this image: person, bicycle, car, motorcycle, airplane, bus, train, truck, boat, traffic light, fire hydrant, stop sign, parking meter, bench, bird, cat, dog, horse, sheep, cow, elephant, bear, zebra, giraffe, backpack, umbrella, handbag, tie, suitcase, frisbee, skis, snowboard, sports ball, kite, baseball bat, baseball glove, skateboard, surfboard, tennis racket, bottle, wine glass, cup, fork, knife, spoon, bowl, banana, apple, sandwich, orange, broccoli, carrot, hot dog, pizza, donut, cake, chair, couch, potted plant, bed, dining table, toilet, tv, laptop, mouse, remote, keyboard, cell phone, microwave, oven, toaster, sink, refrigerator, book, clock, vase, scissors, teddy bear, hair drier, toothbrush. For each detected object, provide the output in the format: category: ((x1, y1), (x2, y2)). The point (x1, y1) represents a coordinate on the detected object, and the point (x2, y2) represents a coordinate outside the detected object. Note that the width of the input image is given as 500 and the height as 375.

A

('person', '261', '101', '152', '414'): [(0.522, 0.269), (0.304, 1.104)], ('person', '261', '101', '152', '414'): [(0.314, 1.168), (0.004, 0.285)]

('person', '212', '138', '298', '343'): [(0.424, 0.368), (0.596, 0.915)], ('person', '212', '138', '298', '343'): [(0.314, 1.168), (0.004, 0.285)]

('person', '97', '122', '160', '480'): [(0.194, 0.325), (0.32, 1.28)], ('person', '97', '122', '160', '480'): [(0.58, 1.072), (0.71, 1.091)]

('person', '348', '304', '299', '228'): [(0.696, 0.811), (0.598, 0.608)], ('person', '348', '304', '299', '228'): [(0.664, 0.864), (0.64, 1.235)]

pixel_localization

pixel_level_perception

VAL

Please detect all instances of the following categories in this image: person, bicycle, car, motorcycle, airplane, bus, train, truck, boat, traffic light, fire hydrant, stop sign, parking meter, bench, bird, cat, dog, horse, sheep, cow, elephant, bear, zebra, giraffe, backpack, umbrella, handbag, tie, suitcase, frisbee, skis, snowboard, sports ball, kite, baseball bat, baseball glove, skateboard, surfboard, tennis racket, bottle, wine glass, cup, fork, knife, spoon, bowl, banana, apple, sandwich, orange, broccoli, carrot, hot dog, pizza, donut, cake, chair, couch, potted plant, bed, dining table, toilet, tv, laptop, mouse, remote, keyboard, cell phone, microwave, oven, toaster, sink, refrigerator, book, clock, vase, scissors, teddy bear, hair drier, toothbrush. For each detected object, provide the output in the format: category: ((x1, y1), (x2, y2)). The point (x1, y1) represents a coordinate on the detected object, and the point (x2, y2) represents a coordinate outside the detected object. Note that the width of the input image is given as 428 and the height as 640.

A

('banana', '457', '222', '13', '110'): [(1.068, 0.347), (0.03, 0.172)]

('banana', '615', '99', '611', '98'): [(1.437, 0.155), (1.428, 0.153)]

('banana', '308', '426', '85', '303'): [(0.72, 0.666), (0.199, 0.473)]

('banana', '489', '399', '378', '404'): [(1.143, 0.623), (0.883, 0.631)]

pixel_localization

pixel_level_perception

VAL

Please detect all instances of the following categories in this image: person, bicycle, car, motorcycle, airplane, bus, train, truck, boat, traffic light, fire hydrant, stop sign, parking meter, bench, bird, cat, dog, horse, sheep, cow, elephant, bear, zebra, giraffe, backpack, umbrella, handbag, tie, suitcase, frisbee, skis, snowboard, sports ball, kite, baseball bat, baseball glove, skateboard, surfboard, tennis racket, bottle, wine glass, cup, fork, knife, spoon, bowl, banana, apple, sandwich, orange, broccoli, carrot, hot dog, pizza, donut, cake, chair, couch, potted plant, bed, dining table, toilet, tv, laptop, mouse, remote, keyboard, cell phone, microwave, oven, toaster, sink, refrigerator, book, clock, vase, scissors, teddy bear, hair drier, toothbrush. For each detected object, provide the output in the format: category: ((x1, y1), (x2, y2)). The point (x1, y1) represents a coordinate on the detected object, and the point (x2, y2) represents a coordinate outside the detected object. Note that the width of the input image is given as 640 and the height as 480.

C

('person', '235', '101', '271', '86'): [(0.367, 0.21), (0.423, 0.179)], ('person', '235', '101', '271', '86'): [(0.386, 0.108), (0.333, 0.173)], ('person', '235', '101', '271', '86'): [(0.498, 1.242), (0.287, 0.992)]

('person', '248', '80', '299', '94'): [(0.388, 0.167), (0.467, 0.196)], ('person', '248', '80', '299', '94'): [(0.344, 0.629), (0.333, 0.61)], ('person', '248', '80', '299', '94'): [(0.566, 1.219), (0.602, 0.071)]

('person', '215', '94', '343', '529'): [(0.336, 0.196), (0.536, 1.102)], ('person', '215', '94', '343', '529'): [(0.339, 0.617), (0.147, 0.006)], ('person', '215', '94', '343', '529'): [(0.498, 1.242), (0.287, 0.992)]

('person', '225', '74', '272', '109'): [(0.352, 0.154), (0.425, 0.227)], ('person', '225', '74', '272', '109'): [(0.336, 0.61), (0.334, 0.631)], ('person', '225', '74', '272', '109'): [(0.659, 0.577), (0.328, 0.627)]

pixel_localization

pixel_level_perception

VAL

Please detect all instances of the following categories in this image: person, bicycle, car, motorcycle, airplane, bus, train, truck, boat, traffic light, fire hydrant, stop sign, parking meter, bench, bird, cat, dog, horse, sheep, cow, elephant, bear, zebra, giraffe, backpack, umbrella, handbag, tie, suitcase, frisbee, skis, snowboard, sports ball, kite, baseball bat, baseball glove, skateboard, surfboard, tennis racket, bottle, wine glass, cup, fork, knife, spoon, bowl, banana, apple, sandwich, orange, broccoli, carrot, hot dog, pizza, donut, cake, chair, couch, potted plant, bed, dining table, toilet, tv, laptop, mouse, remote, keyboard, cell phone, microwave, oven, toaster, sink, refrigerator, book, clock, vase, scissors, teddy bear, hair drier, toothbrush. For each detected object, provide the output in the format: category: ((x1, y1), (x2, y2)). The point (x1, y1) represents a coordinate on the detected object, and the point (x2, y2) represents a coordinate outside the detected object. Note that the width of the input image is given as 640 and the height as 480.

B

('dog', '341', '181', '279', '247'): [(0.533, 0.377), (0.436, 0.515)], ('dog', '341', '181', '279', '247'): [(0.444, 0.287), (0.398, 0.34)], ('dog', '341', '181', '279', '247'): [(0.286, 0.456), (0.209, 1.137)]

('dog', '195', '504', '212', '113'): [(0.305, 1.05), (0.331, 0.235)], ('dog', '195', '504', '212', '113'): [(0.397, 0.315), (0.523, 1.265)], ('dog', '195', '504', '212', '113'): [(0.286, 0.456), (0.209, 1.137)]

('dog', '355', '108', '229', '274'): [(0.555, 0.225), (0.358, 0.571)], ('dog', '355', '108', '229', '274'): [(0.503, 0.6), (0.289, 0.444)], ('dog', '355', '108', '229', '274'): [(0.733, 0.106), (0.27, 0.433)]

('dog', '148', '626', '35', '558'): [(0.231, 1.304), (0.055, 1.163)], ('dog', '148', '626', '35', '558'): [(0.303, 0.254), (0.372, 0.26)], ('dog', '148', '626', '35', '558'): [(0.286, 0.456), (0.209, 1.137)]

pixel_localization

pixel_level_perception

VAL

Please detect all instances of the following categories in this image: person, bicycle, car, motorcycle, airplane, bus, train, truck, boat, traffic light, fire hydrant, stop sign, parking meter, bench, bird, cat, dog, horse, sheep, cow, elephant, bear, zebra, giraffe, backpack, umbrella, handbag, tie, suitcase, frisbee, skis, snowboard, sports ball, kite, baseball bat, baseball glove, skateboard, surfboard, tennis racket, bottle, wine glass, cup, fork, knife, spoon, bowl, banana, apple, sandwich, orange, broccoli, carrot, hot dog, pizza, donut, cake, chair, couch, potted plant, bed, dining table, toilet, tv, laptop, mouse, remote, keyboard, cell phone, microwave, oven, toaster, sink, refrigerator, book, clock, vase, scissors, teddy bear, hair drier, toothbrush. For each detected object, provide the output in the format: category: ((x1, y1), (x2, y2)). The point (x1, y1) represents a coordinate on the detected object, and the point (x2, y2) represents a coordinate outside the detected object. Note that the width of the input image is given as 480 and the height as 640.

D

('vase', '537', '142', '469', '258'): [(1.119, 0.222), (0.977, 0.403)], ('vase', '537', '142', '469', '258'): [(0.696, 0.042), (1.171, 0.15)]

('vase', '286', '237', '493', '84'): [(0.596, 0.37), (1.027, 0.131)], ('vase', '286', '237', '493', '84'): [(0.802, 0.359), (0.854, 0.403)]

('vase', '234', '341', '44', '145'): [(0.487, 0.533), (0.092, 0.227)], ('vase', '234', '341', '44', '145'): [(0.863, 0.256), (0.521, 0.191)]

('vase', '286', '237', '493', '84'): [(0.596, 0.37), (1.027, 0.131)], ('vase', '286', '237', '493', '84'): [(0.746, 0.38), (0.438, 0.128)]

pixel_localization

pixel_level_perception

VAL

Please detect all instances of the following categories in this image: person, bicycle, car, motorcycle, airplane, bus, train, truck, boat, traffic light, fire hydrant, stop sign, parking meter, bench, bird, cat, dog, horse, sheep, cow, elephant, bear, zebra, giraffe, backpack, umbrella, handbag, tie, suitcase, frisbee, skis, snowboard, sports ball, kite, baseball bat, baseball glove, skateboard, surfboard, tennis racket, bottle, wine glass, cup, fork, knife, spoon, bowl, banana, apple, sandwich, orange, broccoli, carrot, hot dog, pizza, donut, cake, chair, couch, potted plant, bed, dining table, toilet, tv, laptop, mouse, remote, keyboard, cell phone, microwave, oven, toaster, sink, refrigerator, book, clock, vase, scissors, teddy bear, hair drier, toothbrush. For each detected object, provide the output in the format: category: ((x1, y1), (x2, y2)). The point (x1, y1) represents a coordinate on the detected object, and the point (x2, y2) represents a coordinate outside the detected object. Note that the width of the input image is given as 500 and the height as 375.

B

('person', '28', '10', '158', '170'): [(0.056, 0.027), (0.316, 0.453)], ('person', '28', '10', '158', '170'): [(0.558, 0.597), (0.324, 0.352)]

('person', '56', '153', '212', '353'): [(0.112, 0.408), (0.424, 0.941)], ('person', '56', '153', '212', '353'): [(0.316, 0.405), (0.322, 0.293)]

('person', '162', '371', '112', '316'): [(0.324, 0.989), (0.224, 0.843)], ('person', '162', '371', '112', '316'): [(0.714, 0.621), (0.158, 0.373)]

('person', '56', '153', '212', '353'): [(0.112, 0.408), (0.424, 0.941)], ('person', '56', '153', '212', '353'): [(0.008, 1.256), (0.328, 0.424)]

pixel_localization

pixel_level_perception

VAL

Please detect all instances of the following categories in this image: person, bicycle, car, motorcycle, airplane, bus, train, truck, boat, traffic light, fire hydrant, stop sign, parking meter, bench, bird, cat, dog, horse, sheep, cow, elephant, bear, zebra, giraffe, backpack, umbrella, handbag, tie, suitcase, frisbee, skis, snowboard, sports ball, kite, baseball bat, baseball glove, skateboard, surfboard, tennis racket, bottle, wine glass, cup, fork, knife, spoon, bowl, banana, apple, sandwich, orange, broccoli, carrot, hot dog, pizza, donut, cake, chair, couch, potted plant, bed, dining table, toilet, tv, laptop, mouse, remote, keyboard, cell phone, microwave, oven, toaster, sink, refrigerator, book, clock, vase, scissors, teddy bear, hair drier, toothbrush. For each detected object, provide the output in the format: category: ((x1, y1), (x2, y2)). The point (x1, y1) represents a coordinate on the detected object, and the point (x2, y2) represents a coordinate outside the detected object. Note that the width of the input image is given as 500 and the height as 341.

C

('vase', '230', '240', '237', '265'): [(0.46, 0.704), (0.474, 0.777)], ('vase', '230', '240', '237', '265'): [(0.098, 1.085), (0.596, 0.587)]

('vase', '327', '248', '318', '204'): [(0.654, 0.727), (0.636, 0.598)], ('vase', '327', '248', '318', '204'): [(0.618, 0.211), (0.53, 0.613)]

('vase', '310', '238', '128', '26'): [(0.62, 0.698), (0.256, 0.076)], ('vase', '310', '238', '128', '26'): [(0.076, 0.584), (0.448, 0.223)]

('vase', '310', '238', '128', '26'): [(0.62, 0.698), (0.256, 0.076)], ('vase', '310', '238', '128', '26'): [(0.272, 1.062), (0.136, 0.32)]

pixel_localization

pixel_level_perception

VAL

What is the semantic category of the pixel point at coordinates (0.473, 0.222) in the image? Note that the width of the input image is given as 640 and the height as 427. The coordinates of the top left corner of the image are (0, 0), and the coordinates of the bottom right corner are (640, 427).

D

motorcycle

bus

car

truck

pixel_recognition

pixel_level_perception

VAL