Unlabelled 3D Motion Examples Improve Cross-View Action Recognition
Bibliographic record
Abstract
We demonstrate a novel strategy for unsupervised cross-view action recognition using multi-view feature synthesis. We do not rely on cross-view video annotations to transfer knowledge across views, but use local features generated using motion capture data to learn the feature transformation. Motion capture data allows us to build a feature level correspondence between two synthesized views. We learn a feature mapping scheme for each view change by making a naive assumption that all features transform independently. This assumption along with the exact feature correspondences dramatically simplifies learning. With this learned mapping we are able to “hallucinate” action descriptors corresponding to different viewpoints. This simple approach effectively models the transformation of BoW based action descriptors under viewpoint change and outperforms the state of the art on the INRIA IXMAS dataset.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".