Cross-Graph Domain Adaptation for Skeleton-based Human Action Recognition
Bibliographic record
Abstract
Recent research on human action recognition is largely facilitated by skeletal data, a compact graph representation composed of key joints of the human skeleton that is efficiently extracted by body tracking systems and that offers the merit of being robust to environmental variations. However, the skeleton resolution and joint connectivity of the extracted skeletons may vary with sensor devices, which results in different skeleton graph representations on collected data. This paper investigates a cross skeleton graph domain adaptation approach where a skeleton action recognition model is trained upon a source skeletal data domain but is expected to adapt onto a target domain configured with a different skeleton graph. It proposes an adversarial learning framework where a generation space is developed on which the model learns valid skeletal action knowledge from the source graph domain.Interaction with an embedded discrimination space is employed to extract heterogenous graph features from the target domain. Optimization of the generation space and the discrimination space is realized alternatively under adversarial learning which guarantees action-aware and domain-agnostic skeletal knowledge, thus forming a joint human action recognition model effectively functioning on both graph domains. In experiments, the paper evaluates the proposed method by incorporating graph convolutional networks into two skeleton action recognition benchmarks, NTU-RGB+D and Northwestern-UCLA, where comparisons are conducted to demonstrate the effectiveness of the proposed approach. Code will be available at https://github.com/tht106/CrossGraphDA .
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".