Human action recognition from a single clip per action
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
Learning-based approaches for human action recognition often rely on large training sets. Most of these approaches do not perform well when only a few training samples are available. In this paper, we consider the problem of human action recognition from a single clip per action. Each clip contains at most 25 frames. Using a patch based motion descriptor and matching scheme, we can achieve promising results on three different action datasets with a single clip as the template. Our results are comparable to previously published results using much larger training sets. We also present a method for learning a transferable distance function for these patches. The transferable distance function learning extracts generic knowledge of patch weighting from previous training sets, and can be applied to videos of new actions without further learning. Our experimental results show that the transferable distance function learning not only improves the recognition accuracy of the single clip action recognition, but also significantly enhances the efficiency of the matching scheme.
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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 | 0.002 |
| 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