Anomaly Detection in Video Sequence with Appearance-Motion\n Correspondence
Bibliographic record
Abstract
Anomaly detection in surveillance videos is currently a challenge because of\nthe diversity of possible events. We propose a deep convolutional neural\nnetwork (CNN) that addresses this problem by learning a correspondence between\ncommon object appearances (e.g. pedestrian, background, tree, etc.) and their\nassociated motions. Our model is designed as a combination of a reconstruction\nnetwork and an image translation model that share the same encoder. The former\nsub-network determines the most significant structures that appear in video\nframes and the latter one attempts to associate motion templates to such\nstructures. The training stage is performed using only videos of normal events\nand the model is then capable to estimate frame-level scores for an unknown\ninput. The experiments on 6 benchmark datasets demonstrate the competitive\nperformance of the proposed approach with respect to state-of-the-art methods.\n
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".