Bibliographic record
Abstract
Advances in Deep Learning and Computer Vision enabled sophisticated information extraction out of images and video frames. Recent research aims to make objects, their types and relative locations as the video evolves, first class citizens for query processing purposes.In this paper, we initiate research to explore declarative style of querying for real time video streams involving objects and their interactions. We seek to efficiently identify frames in a streaming video in which an object is interacting with another in a specific way, such as for example a human kicking a ball. We first propose an algorithm called progressive filters (PF) that deploys a sequence of inexpensive and less accurate models (filters) to detect the presence of the query specified objects on frames. We demonstrate that PF derives a least cost sequence of filters given the current selectivities of query objects. Since selectivities may vary as the video evolves, we present a dynamic statistical test to determine when to trigger re-optimization of the filters. Finally, we present a filtering approach called Interaction Sheave (IS) that utilizes learned spatial information about objects and interactions to effectively prune frames that are unlikely to involve the query specified action between them, thus improving the frame processing rate further.We present the results of a thorough experimental evaluation involving real data sets, demonstrating the performance benefits of each of our proposals. In particular we experimentally demonstrate that our techniques can improve query performance substantially (up to an order of magnitude in our experiments) while maintaining essentially the same F1-score as alternatives.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".