Few-Shot Learning of Video Action Recognition Only Based on Video Contents
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
The success of video action recognition based on Deep Neural Networks (DNNs) is highly dependent on a large number of manually labeled videos. In this paper, we introduce a supervised learning approach to recognize video actions with very few training videos. Specifically, we propose Temporal Attention Vectors (TAVs) which adapt various length videos to preserve the temporal information of the entire video. We evaluate the TAVs on UCF101 and HMDB51. Without training any deep 3D or 2D frame feature extractors on video datasets (only pre-trained on ImageNet), the TAVs only introduce 2.1M parameters but outperforms the state-of-the-art video action recognition benchmarks with very few labeled training videos (e.g. 92% on UCF101 and 59% on HMDB51, with 10 and 8 training videos per class, respectively). Furthermore, our approach can still achieve competitive results on full datasets (97.1% on UCF101 and 77% on HMDB51).
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it