Multiobject behavior recognition by event driven selective attention method
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper presents a multiobject behaviour recognition approach based on assumption generation and verification, i.e., feasible assumptions about the present behaviors consistent with the input image and behavior models are dynamically generated and verified by finding their supporting evidence in input images. This can be realized by an architecture called the selective attention model, which consists of a state-dependent event detector and an event sequence analyzer. The former detects image variation (event) in a limited image region (focusing region), which is not affected by occlusions and outliers. The latter analyzes sequences of detected events and activates all feasible states representing assumptions about multiobject behaviors. We further extend the system by introducing colored-token propagation to discriminate different objects in state space, and integration of multiviewpoint image sequences to disambiguate the single-view recognition results. Extensive experiments of human behavior recognition in real world environments demonstrate the soundness and robustness of our architecture.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it