for example , this QA pair:{'id': '1d235502-7047-4728-832a-fa47f6e622f0', 'data_source': 'arkitscenes', 'scene_name': '41048124', 'question_type': 'obj_appearance_order', 'conversations': [{'from': 'human', 'value': '![]()
\nThe video captures 3D spatial information of a scene. Your task is to focus on the spatial relationships in the video.\n\n1. Question Analysis\n- Carefully analyze the given question.\n- Identify relevant objects only if bounding box information would help in answering the question.\n- If bounding boxes are not useful for answering the question, skip object identification entirely.\n\n2. Bounding Box Output (conditional)\n- Output bounding boxes only if they are useful for answering the question.\n- For each relevant object, output its name, counts, and corresponding 3D bounding boxes in the format: (x1, y1, z1, x2, y2, z2)\n- If an object has multiple instances, list only distinct boxes, ensuring no duplicates, one by one.\n- If an object has too many instances, select and output up to 5 bounding boxes that are most relevant to the question.\n- If bounding boxes are not needed, skip this step entirely.\n\n3. Reasoning\n- Base your reasoning on all available information.\n- Derive the final answer strictly based on this reasoning.\n\n4. Output Format (STRICT)\nYour response must follow exactly this structure:\n\n[Analysing the question]\nIf bounding boxes are useful for answering the question, output them in the following format: object_name object_count (x1, y1, z1, x2, y2, z2)..., object_name object_count (x1, y1, z1, x2, y2, z2)(x1, y1, z1, x2, y2, z2)...\nIf bounding boxes are not useful for answering the question, skip this step entirely.\n[Reasoning grounded in all available information][Final concise answer here]\n\nQuestion: \nWhat will be the initial order of appearance of these items in the video: chair, table, tv_monitor, sofa?\nOptions:\nA. chair, table, tv_monitor, sofa\nB. sofa, chair, table, tv_monitor\nC. table, tv_monitor, sofa, chair\nD. tv_monitor, sofa, chair, table\n'}, {'from': 'gpt', 'value': 'The question is asking for the first-time appearance order of chair, table, tv_monitor, sofa. To solve this, I only need to focus on the temporal information in the video without identifying any object bounding boxes.\nCombined with video and the given options, the first-time appearance order of mentioned objects in the options is B. sofa, chair, table, tv_monitor.B'}], 'metadata': {'dataset': 'vsibench_cot', 'data_augmentation': {'rescale': {'flag': True, 'scale': 1.073053240776062}, 'rot_z': {'flag': True, 'rot': 1.0}, 'trans_x': {'flag': False, 'trans': 0}, 'trans_y': {'flag': True, 'trans': -0.025245095843190235}, 'trans_z': {'flag': True, 'trans': 0.8749931584187944}}}, 'video': 'arkitscenes/41048124'}
can you explain what objects have been applied the data augmentation techique? Are you try to do rotation, translation for all three objects' bounding boxes stated in the question? Thanks!
