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--- |
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language: |
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- en |
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license: mit |
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pretty_name: False Absence Detection |
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tags: |
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- clarusc64 |
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- world-models |
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- spatial-grounding |
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- absence |
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- persistence |
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- video |
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task_categories: |
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- video-classification |
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- image-classification |
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size_categories: |
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- n<1K |
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source_datasets: |
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- original |
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--- |
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ClarusC64/false_absence_detection_v01 |
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Dataset summary |
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This dataset tests whether models treat **invisible entities** as gone or still present. |
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In each sequence, a target leaves the camera view. |
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Some exits are real. |
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Some are false absences with clear evidence that the target remains in the container. |
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Goal |
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- check if models infer continued presence from indirect cues |
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- avoid treating every disappearance as an exit |
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- keep spatial grounding under occlusion and clutter |
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Key signals |
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- `absence_tag`: present, still_present, left_scene |
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- `evidence_type`: shadow, reflection, audio cue, motion trail, interaction trace, door state, physical constraint, none |
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- `false_absence_target`: true when the model should infer “still present” |
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- `trap_flag`: true when the sample is designed to lure models into assuming exit |
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Columns |
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- `sample_id` – unique id per frame sample |
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- `split` – train, valid, eval |
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- `modality` – video |
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- `scene_type` – indoor_room, factory_line, corridor, sports_pitch, warehouse |
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- `sequence_id` – id for a temporal sequence |
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- `frame_index` – index within the sequence |
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- `time_gap` – not used here (fixed gaps inside raw video) |
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- `container_id` – id of the main container |
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- `container_bounds` – "x_min y_min x_max y_max" |
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- `boundary_type` – hard, soft, porous |
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- `zone_id` – region id inside the container |
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- `zone_type` – sofa, door, conveyor, chute, passage, turn_left, exit_door, left_flank, stands, robot_lane, intersection, dock_exit |
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- `target_entity_id` – tracked entity such as cat_01, crate_05, cart_02, ball_07, robot_11 |
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- `target_visibility` – visible, partial, not_visible |
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- `absence_tag` – present, still_present, left_scene |
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- `evidence_type` – none, shadow, motion_trail, interaction_trace, audio_cue, door_state, reflection, crowd_reaction, physical_constraint |
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- `false_absence_target` – true if the model should infer continued presence from evidence |
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- `trap_flag` – true if the scene is designed as a false-absence trap |
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- `label_type` – baseline, false_absence_case, true_exit |
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- `drift_risk` – low, medium, high |
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- `comment` – short human note |
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Example loading code |
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```python |
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from datasets import load_dataset |
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ds = load_dataset("ClarusC64/false_absence_detection_v01") |
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row = ds["train"][1] |
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print(row["target_entity_id"], row["target_visibility"], |
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row["absence_tag"], row["evidence_type"], row["false_absence_target"]) |
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