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pro vyhledávání: '"Zeng, Fanhu"'
Continual learning aims to equip models with the ability to retain previously learned knowledge like a human. Recent work incorporating Parameter-Efficient Fine-Tuning has revitalized the field by introducing lightweight extension modules. However, e
Externí odkaz:
http://arxiv.org/abs/2411.19154
Large Multimodal Models (LMMs) exhibit remarkable multi-tasking ability by learning mixed datasets jointly. However, novel tasks would be encountered sequentially in dynamic world, and continually fine-tuning LMMs often leads to performance degrades.
Externí odkaz:
http://arxiv.org/abs/2410.05849
Enhancing Outlier Knowledge for Few-Shot Out-of-Distribution Detection with Extensible Local Prompts
Out-of-Distribution (OOD) detection, aiming to distinguish outliers from known categories, has gained prominence in practical scenarios. Recently, the advent of vision-language models (VLM) has heightened interest in enhancing OOD detection for VLM t
Externí odkaz:
http://arxiv.org/abs/2409.04796
Vision Transformers (ViTs) have emerged as powerful models in the field of computer vision, delivering superior performance across various vision tasks. However, the high computational complexity poses a significant barrier to their practical applica
Externí odkaz:
http://arxiv.org/abs/2310.01812
Autor:
Zeng, Fanhu, Xie, Yu, Guo, Yuping, Li, Qigao, Tan, Bin, Huang, Fuyao, Huang, Yongbing, Ni, Shang, Xu, Jiefei, Jia, Junzuo
Publikováno v:
Water Science and Technology; October 2022, Vol. 86 Issue: 7 p1745-1758, 14p
