Global context-aware attention model for weakly-supervised temporal action localization
Bibliographic record
Abstract
Temporal action localization (TAL) is a significant and challenging task in the field of video understanding. It aims to locate the start and end timestamps of the actions in a video and recognize their categories. However, efficient action localization often requires extensive precise annotations. Therefore, the researchers propose weakly-supervised temporal action localization (WTAL), which aims to locate action instances in a video using only video level annotations. The existing WTAL methods lack the ability to distinguish the action context information effectively, including the pre-action and post-action scenes, which blur the action boundary and lead to the inaccurate action location. To solve the above problems, this paper proposes a global context-aware attention model (GCAM). Firstly, GCAM designs the mask attention module (MAM) to restrict the model's receptive field and make the model focus on localized features related to the action context. It enhances the ability to distinguish the action context information and clearly locate the start and end timestamps of the actions. Secondly, GCAM introduces the context broadcasting module (CBM), which supplements the global context information to keep the features intact in temporal dimension. This module solves the issue that the model overemphasizes the localized features due to the addition of the MAM. Extensive experiments on the THUMOS14 and ActivityNet1.2 datasets demonstrate the effectiveness of GCAM. On the THUMOS14 dataset, GCAM achieves an average mean average precision (mAP) of 49.5 %, representing a 2.2 % improvement over existing WTAL methods. On the ActivityNet1.2 dataset, GCAM achieves an average mAP of 27.2 %, representing a 0.3 % improvement over existing WTAL methods. These results highlight the superior performance of GCAM in accurately localizing actions in videos.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".