Chenyu Mu — accepted AI-conference papers

2025

Energy vs. Noise: Towards Robust Temporal Action Localization in Open-World

Chenyu Mu, Jiahua Li, Kun Wei, Cheng Deng

AAAI 2025technical

Temporal Action Localization (TAL) aims to accurately identify the start and end times of actions in untrimmed videos and classify them according to specific labels. However, the complexity and imbalance between target actions and background in video data make this task particularly challenging. Alt…

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2025

Meta-Learning Dynamic Center Distance: Hard Sample Mining for Learning with Noisy Labels

Chenyu Mu, Yijun Qu, Jiexi Yan, Erkun Yang, Cheng Deng

ICCV 2025poster

The sample selection approach is a widely adopted strategy for learning with noisy labels, where examples with lower losses are effectively treated as clean during training. However, this clean set often becomes dominated by easy examples, limiting the model's meaningful exposure to more challenging…

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