Heterogeneous Features Fusion with Collaborative Representation Learning for 3D Action Recognition
Bibliographic record
Abstract
Human action recognition of depth sensors has drawn wide attentions in computer vision and multimedia processing areas. In contrast to simple periodic actions, irrelevant actions or sharing sub-actions between different classes of two-person non-periodic interactions make this task challenging. This paper presents heterogeneous features fusion with Collaborative Representation (CR) to address this challenge. Two effective high dimensional low-level features are developed from depth image sequence and skeleton pose sequence respectively. In the Canonical Correlations Analysis (CCA) feature space of these two features, Collaborative Representation (CR) is learned and adopted as the final high-level discriminative representation. Experiments on two depth action datasets (SBU Kinect-Interaction and MSR Action 3D) show that the proposed method is superior to the state-of-the-art methods compared, including some recent deep learning based methods.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".