Improving the Multi-label Atomic Activity Recognition by Robust Visual Feature and Advanced Attention @ ROAD++ Atomic Activity Recognition 2024

• URL: http://arxiv.org/abs/2410.16037v1
• Date: Mon, 21 Oct 2024 14:10:14 GMT
• Title: Improving the Multi-label Atomic Activity Recognition by Robust Visual Feature and Advanced Attention @ ROAD++ Atomic Activity Recognition 2024
• Authors: Jiamin Cao, Lingqi Wang, Kexin Zhang, Yuting Yang, Licheng Jiao, Yuwei Guo,
• Abstract summary: Road++ Track3 proposes a multi-label atomic activity recognition task in traffic scenarios. The robustness of visual feature extraction remains a key challenge. The final mAP on the test set was 58%, which is 4% higher than the challenge baseline.
• Score: 34.921509504848025
• License:
• Abstract: Road++ Track3 proposes a multi-label atomic activity recognition task in traffic scenarios, which can be standardized as a 64-class multi-label video action recognition task. In the multi-label atomic activity recognition task, the robustness of visual feature extraction remains a key challenge, which directly affects the model performance and generalization ability. To cope with these issues, our team optimized three aspects: data processing, model and post-processing. Firstly, the appropriate resolution and video sampling strategy are selected, and a fixed sampling strategy is set on the validation and test sets. Secondly, in terms of model training, the team selects a variety of visual backbone networks for feature extraction, and then introduces the action-slot model, which is trained on the training and validation sets, and reasoned on the test set. Finally, for post-processing, the team combined the strengths and weaknesses of different models for weighted fusion, and the final mAP on the test set was 58%, which is 4% higher than the challenge baseline.

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