A Graph Learning Based Multi-Modal Video Action Recognition
Bibliographic record
Abstract
Due to the robustness to variations of viewpoints and environment, 3D skeleton-based action recognition has drawn considerable interests in academic and industrial sectors. Recently, deep neural networks (DNNs)-based algorithms have been applied to skeleton-based action recognition extensively. Amongst many contemporary approaches, graph learning plays a significant role in 3D skeleton-based action recognition. In addition, with the advancement of multi-sensory technology, multi-modal action recognition has grown at an extremely rapid pace. In this work, a graph learning based multi-modal framework is proposed with application to action recognition. Specifically, a two-stream heterogeneous network is designed to extract the complementary features from 3D skeleton and RGB modalities jointly. Then, a discriminative adaptation model (DAM) is presented and then applied to the designed heterogeneous network for multi-modal action recognition. To validate the effectiveness of the proposed model, experiments are conducted on two multi-modal action recognition database with different scales: NTU RGB+D 120, and SYSU. Experimental results show the power of the generated features and the DAM model on multi-modal action recognition.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
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".