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pro vyhledávání: '"Bai, Shuanghao"'
Source-free domain generalization (SFDG) tackles the challenge of adapting models to unseen target domains without access to source domain data. To deal with this challenging task, recent advances in SFDG have primarily focused on leveraging the text
Externí odkaz:
http://arxiv.org/abs/2409.14163
With the advancement of neural networks, diverse methods for neural Granger causality have emerged, which demonstrate proficiency in handling complex data, and nonlinear relationships. However, the existing framework of neural Granger causality has s
Externí odkaz:
http://arxiv.org/abs/2405.08779
Large pre-trained vision language models (VLMs) have shown impressive zero-shot ability on downstream tasks with manually designed prompt. To further adapt VLMs to downstream tasks, soft prompt is proposed to replace manually designed prompt, which u
Externí odkaz:
http://arxiv.org/abs/2404.19286
Pretrained vision-language models (VLMs) like CLIP exhibit exceptional generalization across diverse downstream tasks. While recent studies reveal their vulnerability to adversarial attacks, research to date has primarily focused on enhancing the rob
Externí odkaz:
http://arxiv.org/abs/2404.19287
Cross-domain few-shot classification (CDFSC) is a challenging and tough task due to the significant distribution discrepancies across different domains. To address this challenge, many approaches aim to learn transferable representations. Multilayer
Externí odkaz:
http://arxiv.org/abs/2312.09589
Autor:
Bai, Shuanghao, Zhang, Min, Zhou, Wanqi, Huang, Siteng, Luan, Zhirong, Wang, Donglin, Chen, Badong
Recently, despite the unprecedented success of large pre-trained visual-language models (VLMs) on a wide range of downstream tasks, the real-world unsupervised domain adaptation (UDA) problem is still not well explored. Therefore, in this paper, we f
Externí odkaz:
http://arxiv.org/abs/2312.09553
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