Learning Representations From Skeletal Self-Similarities for Cross-View Action Recognition
Bibliographic record
Abstract
Existing research attention in vision-based action recognition is generally paid on recognizing actions from the same views seen in the training data. One of the big challenges in action recognition lies in the large variations of action representations as actions are captured from totally different viewpoints. This paper addresses this problem by learning view-invariant representations from skeletal self-similarities of varying scales with a very light multi-stream neural network (MSNN). As human skeletons have been proved to be an effective feature modality used for action recognition and are easy to obtain, we first create a view-invariant action description by formulating skeletal self-similarities at each frame as an image (SSI), which can show a high structural stability under view changes. Accordingly, a MSNN is designed based on 3D CNN and LSTM units to learn representations from SSIs of multiple scales, where the scheme of multiple scales provides our method with a good robustness to view changes. In addition, we integrate the computation of SSIs into the MSNN by wrapping it as a custom learnable layer thanks to its simplicity, instead of normalizing and transforming skeletons using a hand-crafted preprocessing. Extensive experimental evaluations on three challenging cross-view datasets demonstrate the effectiveness of our proposed method, which achieves superior performance to the state-of-the-art algorithms on cross-view recognition. The source code of this work will be released shortly to facilitate future studies in this field.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 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".