A Multi-Stream Graph Convolutional Networks-Hidden Conditional Random Field Model for Skeleton-Based Action Recognition
Bibliographic record
Abstract
Recently, Graph Convolutional Network(GCN) methods for skeleton-based action recognition have achieved great success due to their ability to preserve structural information of the skeleton. However, these methods abandon the structural information in the classification stage by employing traditional fully-connected layers and softmax classifier, leading to sub-optimal performance. In this work, a novel Graph Convolutional Networks-Hidden conditional Random Field (GCN-HCRF) model is proposed to solve this problem. The proposed method combines GCN with HCRF to retain the human skeleton structure information even during the classification stage. Our model is trained end-to-end by utilizing the message passing from the belief propagation algorithm on the human structure graph. To further capture spatial and temporal information, we propose a multi-stream framework which takes the relative coordinate of the joints and bone direction as two static feature streams, and the temporal displacements between two consecutive frames as the dynamic feature stream. Experimental results on three challenging benchmarks (NTU RGB+D, N-UCLA, SYSU) show the superior performance of the proposed model over state-of-the-art models.
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".