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Datasets:
WanyueZhang
/
MulSeT

Modalities:
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Languages:
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Tags:
computer-vision
visual-question-answering
multi-modal
spatial-reasoning
synthetic-data
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Upload dataset files (batch 9/552)

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  1. .gitattributes +258 -0
  2. train/train1026/0/0.json +0 -0
  3. train/train1026/0/0_depth_npy.npy +3 -0
  4. train/train1026/0/0_semantic_sensor_npy.npy +3 -0
  5. train/train1026/0/1.json +1063 -0
  6. train/train1026/0/1_depth_npy.npy +3 -0
  7. train/train1026/0/1_semantic_sensor_npy.npy +3 -0
  8. train/train1026/0/depth_0.png +0 -0
  9. train/train1026/0/depth_1.png +0 -0
  10. train/train1026/0/mask_questions.jsonl +5 -0
  11. train/train1026/0/questions_distance.jsonl +2 -0
  12. train/train1026/0/rgb_0.png +3 -0
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  17. train/train1026/0/rgb_1_mask_DiningTable|4|1|0.png +3 -0
  18. train/train1026/0/rgb_1_mask_Fridge|2|1.png +3 -0
  19. train/train1026/0/semantic_0.png +0 -0
  20. train/train1026/0/semantic_1.png +0 -0
  21. train/train1026/1/0.json +1579 -0
  22. train/train1026/1/0_depth_npy.npy +3 -0
  23. train/train1026/1/0_semantic_sensor_npy.npy +3 -0
  24. train/train1026/1/1.json +1652 -0
  25. train/train1026/1/1_depth_npy.npy +3 -0
  26. train/train1026/1/1_semantic_sensor_npy.npy +3 -0
  27. train/train1026/1/depth_0.png +0 -0
  28. train/train1026/1/depth_1.png +0 -0
  29. train/train1026/1/mask_questions.jsonl +0 -0
  30. train/train1026/1/rgb_0.png +3 -0
  31. train/train1026/1/rgb_1.png +3 -0
  32. train/train1026/1/semantic_0.png +0 -0
  33. train/train1026/1/semantic_1.png +0 -0
  34. train/train1026/2/0.json +1616 -0
  35. train/train1026/2/0_depth_npy.npy +3 -0
  36. train/train1026/2/0_semantic_sensor_npy.npy +3 -0
  37. train/train1026/2/1.json +1213 -0
  38. train/train1026/2/1_depth_npy.npy +3 -0
  39. train/train1026/2/1_semantic_sensor_npy.npy +3 -0
  40. train/train1026/2/depth_0.png +0 -0
  41. train/train1026/2/depth_1.png +0 -0
  42. train/train1026/2/mask_questions.jsonl +1 -0
  43. train/train1026/2/questions_distance.jsonl +1 -0
  44. train/train1026/2/rgb_0.png +3 -0
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  46. train/train1026/2/rgb_1.png +3 -0
  47. train/train1026/2/semantic_0.png +0 -0
  48. train/train1026/2/semantic_1.png +0 -0
  49. train/train1026/3/0.json +634 -0
  50. train/train1026/3/0_depth_npy.npy +3 -0
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+ {"id": 316, "folder_dir": "train1026/0", "pair_name": "0-1:Chair|4|1|1", "query": "Please combine the information from the two images and answer the following question:\nWhat is the object being masked in the second image?\nNote: The masked region is indicated by the black rectangle outlined in red in the second image.\nChoose the correct option from the list below. Only respond with the corresponding letter.\n\nA. Television\nB. Fridge\nC. Chair\nD. Dresser", "answer": "C", "object_name": "Chair", "image_1": "train1026/0/rgb_0.png", "image_2": "train1026/0/rgb_1.png", "mask_image": "train1026/0/rgb_1_mask_Chair|4|1|1.png", "options": ["Television", "Fridge", "Chair", "Dresser"]}
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+ {"id": 317, "folder_dir": "train1026/0", "pair_name": "0-1:DiningTable|4|1|0", "query": "Please combine the information from the two images and answer the following question:\nWhat is the object being masked in the second image?\nNote: The masked region is indicated by the black rectangle outlined in red in the second image.\nChoose the correct option from the list below. Only respond with the corresponding letter.\n\nA. Fridge\nB. DiningTable\nC. Laptop\nD. Bowl", "answer": "B", "object_name": "DiningTable", "image_1": "train1026/0/rgb_0.png", "image_2": "train1026/0/rgb_1.png", "mask_image": "train1026/0/rgb_1_mask_DiningTable|4|1|0.png", "options": ["Fridge", "DiningTable", "Laptop", "Bowl"]}
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+ {"id": 318, "folder_dir": "train1026/0", "pair_name": "1-0:Fridge|2|1", "query": "Please combine the information from the two images and answer the following question:\nWhat is the object being masked in the second image?\nNote: The masked region is indicated by the black rectangle outlined in red in the second image.\nChoose the correct option from the list below. Only respond with the corresponding letter.\n\nA. Fridge\nB. Chair\nC. Painting\nD. FloorLamp", "answer": "A", "object_name": "Fridge", "image_1": "train1026/0/rgb_1.png", "image_2": "train1026/0/rgb_0.png", "mask_image": "train1026/0/rgb_0_mask_Fridge|2|1.png", "options": ["Fridge", "Chair", "Painting", "FloorLamp"]}
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+ {"id": 319, "folder_dir": "train1026/0", "pair_name": "1-0:Chair|4|1|1", "query": "Please combine the information from the two images and answer the following question:\nWhat is the object being masked in the second image?\nNote: The masked region is indicated by the black rectangle outlined in red in the second image.\nChoose the correct option from the list below. Only respond with the corresponding letter.\n\nA. Fridge\nB. Painting\nC. Chair\nD. FloorLamp", "answer": "C", "object_name": "Chair", "image_1": "train1026/0/rgb_1.png", "image_2": "train1026/0/rgb_0.png", "mask_image": "train1026/0/rgb_0_mask_Chair|4|1|1.png", "options": ["Fridge", "Painting", "Chair", "FloorLamp"]}
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