A Spatiotemporal Heterogeneous Two-Stream Network for Action Recognition
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The method based on the two-stream networks has achieved great success in video action recognition. However, most existing methods employ the same structure for both spatial and temporal networks, leading to unsatisfied performance. In this paper, we propose a spatiotemporal heterogeneous two-stream network, which employs two different network structures for spatial and temporal information, respectively. Specifically, the Residual network (ResNet) and BN-Inception are utilized as the base networks to present the spatiotemporal characteristics of different human actions. In addition, a segmental architecture is employed to model long-range temporal structure over video sequences to better distinguish the similar actions owning sub-action sharing phenomenon. Moreover, combined with the strategy of data augment, a modified cross-modal pre-training strategy is proposed and applied to the spatiotemporal heterogeneous network to improve the final performance of human actions recognition. The experiments on UCF101 and HMDB51 datasets demonstrate the proposed spatiotemporal heterogeneous two-stream network outperforms the spatiotemporal isomorphic networks and other related methods.
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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 | 0.000 | 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 | 0.000 |
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 it