Bibliographic record
Abstract
We present SVQ-ACT, a system capable of evaluating declarative action and object queries over input videos. Our approach is independent of the underlying object and action detection models utilized. Users may issue queries involving action and specific objects (e.g., a human riding a bicycle, close to a traffic light and a car left of the bicycle) and identify video clips that satisfy query constraints. Our system is capable of operating in two main settings, namely online and offline. In the online setting, the user specifies a video source (e.g., a surveillance video) and a declarative query containing an action and object predicates. Our system will identify and label in real-time all frame sequences that match the query. In the offline mode, the system accepts a video repository as input, preprocesses all the video in an offline manner and extracts suitable metadata. Following this step, users can execute any query they wish interactively on the video repository (containing actions and objects supported by the underlying detection models) to identify sequences of frames from videos that satisfy the query. In this case, to limit the number of results produced, we introduce novel result ranking algorithms that can produce the k most relevant results efficiently.We demonstrate that SVQ-ACT can correctly capture the desired query semantics and execute queries efficiently and correctly, delivering a high degree of accuracy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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 source (direct Gemma or distilled Codex), 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".