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Akademický článek
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Akademický článek
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Autor:
Wang, Dong, Qi, Weihui, Mao, Xinning, Zhang, Yujun, Miao, Zhimin, Zhu, Chengyue, Shao, Yinyan, Ge, Guofen, Zhang, Wei, Jin, HongTing, Zhu, Hang, Pan, Hao
Publikováno v:
In Phytomedicine October 2024 133
Visual tracking fundamentally involves regressing the state of the target in each frame of a video. Despite significant progress, existing regression-based trackers still tend to experience failures and inaccuracies. To enhance the precision of targe
Externí odkaz:
http://arxiv.org/abs/2406.00589
Federated learning benefits from cross-training strategies, which enables models to train on data from distinct sources to improve the generalization capability. However, the data heterogeneity between sources may lead models to gradually forget prev
Externí odkaz:
http://arxiv.org/abs/2405.20046
The neighbor-based method has become a powerful tool to handle the outlier detection problem, which aims to infer the abnormal degree of the sample based on the compactness of the sample and its neighbors. However, the existing methods commonly focus
Externí odkaz:
http://arxiv.org/abs/2405.19247
Learning with noisy labels has become an effective strategy for enhancing the robustness of models, which enables models to better tolerate inaccurate data. Existing methods either focus on optimizing the loss function to mitigate the interference fr
Externí odkaz:
http://arxiv.org/abs/2405.19606
Representation learning for images has been advanced by recent progress in more complex neural models such as the Vision Transformers and new learning theories such as the structural causal models. However, these models mainly rely on the classificat
Externí odkaz:
http://arxiv.org/abs/2308.04142
Federated Learning aims to learn a global model on the server side that generalizes to all clients in a privacy-preserving manner, by leveraging the local models from different clients. Existing solutions focus on either regularizing the objective fu
Externí odkaz:
http://arxiv.org/abs/2308.03457
Multimedia recommendation aims to fuse the multi-modal information of items for feature enrichment to improve the recommendation performance. However, existing methods typically introduce multi-modal information based on collaborative information to
Externí odkaz:
http://arxiv.org/abs/2307.02761