---
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"])