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