Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Zhong, Jike"'
Recent studies on large language models (LLMs) and large multimodal models (LMMs) have demonstrated promising skills in various domains including science and mathematics. However, their capability in more challenging and real-world related scenarios
Externí odkaz:
http://arxiv.org/abs/2411.01492
Recent advances in fine-tuning Vision-Language Models (VLMs) have witnessed the success of prompt tuning and adapter tuning, while the classic model fine-tuning on inherent parameters seems to be overlooked. It is believed that fine-tuning the parame
Externí odkaz:
http://arxiv.org/abs/2409.16718
Autor:
Tu, Cheng-Hao, Chen, Hong-You, Mai, Zheda, Zhong, Jike, Pahuja, Vardaan, Berger-Wolf, Tanya, Gao, Song, Stewart, Charles, Su, Yu, Chao, Wei-Lun
We propose a learning problem involving adapting a pre-trained source model to the target domain for classifying all classes that appeared in the source data, using target data that covers only a partial label space. This problem is practical, as it
Externí odkaz:
http://arxiv.org/abs/2311.01420
Autor:
Chen, Hong-You, Zhong, Jike, Zhang, Mingda, Jia, Xuhui, Qi, Hang, Gong, Boqing, Chao, Wei-Lun, Zhang, Li
Personalized federated learning (PFL) aims to harness the collective wisdom of clients' data while building personalized models tailored to individual clients' data distributions. Existing works offer personalization primarily to clients who particip
Externí odkaz:
http://arxiv.org/abs/2304.07882
Batch Normalization (BN) is widely used in {centralized} deep learning to improve convergence and generalization. However, in {federated} learning (FL) with decentralized data, prior work has observed that training with BN could hinder performance an
Externí odkaz:
http://arxiv.org/abs/2303.06530
One fundamental challenge in building an instance segmentation model for a large number of classes in complex scenes is the lack of training examples, especially for rare objects. In this paper, we explore the possibility to increase the training exa
Externí odkaz:
http://arxiv.org/abs/2202.11124