Model reuse is a longstanding challenge within the defense simulationmodeling community. While other disciplines successfully apply component-based approach to build systems, this approach has proven difficult to app...
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作者：
Xin ZhangHongzhi FengM. Shamim HossainYinzhuo ChenHongbo WangYuyu YinHangzhou Dianzi University
China Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education China Zhoushan Tongbo Marine Electronic Information Research Institute Hangzhou Dianzi University China and Yunnan Key Laboratory of Service Computing Yunnan University of Finance and Economics China Hangzhou Dianzi University
China Department of Software Engineering
College of Computer and Information Sciences King Saud University Saudi Arabia Hangzhou Dianzi University
China Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education China and Zhoushan Tongbo Marine Electronic Information Research Institute Hangzhou Dianzi University China
Action Quality Assessment (AQA) has become crucial in video analysis, finding wide applications in various domains, such as healthcare and sports. A significant challenge faced by AQA is the background bias due to the...
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Action Quality Assessment (AQA) has become crucial in video analysis, finding wide applications in various domains, such as healthcare and sports. A significant challenge faced by AQA is the background bias due to the dominance of the background in videos. Especially, the background bias tends to overshadow subtle foreground differences, which is crucial for precise action evaluation. To address the background bias issue, we propose a novel data augmentation method named Scaled Background Swap. Firstly, the background regions between different video samples are swapped to guide models focus toward the dynamic foreground regions and mitigate its sensitivity to the background during training. Secondly, the video’s foreground region is up-scaled to further enhance models’ attention to the critical foreground action information for AQA tasks. In particular, the proposed Scaled Background Swap method can effectively improve models’ accuracy and generalization by prioritizing foreground motion and swapping backgrounds. It can be flexibly applied with various video analysis models. Extensive experiments on AQA benchmarks demonstrate that Scaled Background Swap method achieves better performance than baselines. Specifically, the Spearman’s rank correlation on datasets AQA-7 and MTL-AQA reaches 0.8870 and 0.9526, respectively. The code is available at: https://***/Emy-cv/Scaled-Background Swap.
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