Junsong Yuan, Yi Wu, Yuanhao Zhai, David Doermann, Ziyi Liu, Zhenyu Wu, Gang Hua, Chunluan Zhou
We lifted 12 functions out of this paper's own repositories and ran 10 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| yhZhai/SOAR | canonical | 10 of 12 |
| Function | Status | Where it lives |
|---|---|---|
| anchor | Ran | yhZhai/SOAR/docs_zh_CN/stat.py code served (permissive licence) · get_code("d0ddd50f31a39641") |
| get_indices | Ran | yhZhai/SOAR/experiments/draw_uncertainty_distribution.py code served (permissive licence) · get_code("49c3c12dc62fa96b") |
| get_ood_acc | Ran | yhZhai/SOAR/experiments/open_set_evaluation.py code served (permissive licence) · get_code("e06ec986367edd6c") |
| get_ood_datalist | Ran | yhZhai/SOAR/experiments/analyze_scene_bias.py code served (permissive licence) · get_code("ea1109a89f638356") |
| get_open_maf1 | Ran | yhZhai/SOAR/experiments/analyze_scene_bias.py code served (permissive licence) · get_code("8c4fbd5e5329d6c7") |
| get_stochastic_uncertainty_fn | Ran | yhZhai/SOAR/experiments/ood_detection.py code served (permissive licence) · get_code("03feb5deb14d8343") |
| get_video_name_from_datalist | Ran | yhZhai/SOAR/experiments/analyze_scene_bias.py code served (permissive licence) · get_code("227c51480967ab11") |
| gram_linear | Ran | yhZhai/SOAR/experiments/compare_feature_similarity.py code served (permissive licence) · get_code("65c16032c4c2e917") |
| gram_rbf | Ran | yhZhai/SOAR/experiments/compare_feature_similarity.py code served (permissive licence) · get_code("80314ffcc884fcc5") |
| to_numpy | Ran | yhZhai/SOAR/experiments/compare_feature_similarity.py code served (permissive licence) · get_code("3187403d89a3003a") |
| get_class_names | Not yet run | yhZhai/SOAR/experiments/draw_uncertainty_distribution.py code served (permissive licence) · get_code("6d45c7858760b1bb") |
| get_results | Not yet run | yhZhai/SOAR/experiments/analyze_recon_uncertainty.py code served (permissive licence) · get_code("8dd75826e2ff1516") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Deep learning models have a risk of utilizing spurious clues to make predictions, such as recognizing actions based on the background scene. This issue can severely degrade the open-set action recognition performance when the testing samples have different scene distributions from the training samples. To mitigate this problem, we propose a novel method, called Scene-debiasing Open-set Action Recognition (SOAR), which features an adversarial scene reconstruction module and an adaptive adversarial scene classification module. The former prevents the decoder from reconstructing the video background given video features, and thus helps reduce the background information in feature learning. The latter aims to confuse scene type classification given video features, with a specific emphasis on the action foreground, and helps to learn scene-invariant information. In addition, we design an experiment to quantify the scene bias. The results indicate that the current open-set action recognizers are biased toward the scene, and our proposed SOAR method better mitigates such bias. Furthermore, our extensive experiments demonstrate that our method outperforms state-of-the-art methods, and the ablation studies confirm the effectiveness of our proposed modules.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2309.01265")
get_code_for_paper("2309.01265")
have("2309.01265")
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