Zobrazeno 1 - 10
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pro vyhledávání: '"Liu, Yuyang"'
Autor:
Cao, Meng, Liu, Yuyang, Liu, Yingfei, Wang, Tiancai, Dong, Jiahua, Ding, Henghui, Zhang, Xiangyu, Reid, Ian, Liang, Xiaodan
Instruction tuning constitutes a prevalent technique for tailoring Large Vision Language Models (LVLMs) to meet individual task requirements. To date, most of the existing approaches are confined to single-task adaptation, whereas the requirements in
Externí odkaz:
http://arxiv.org/abs/2411.02564
Incremental object detection (IOD) is challenged by background shift, where background categories in sequential data may include previously learned or future classes. Inspired by the vision-language foundation models such as CLIP, these models captur
Externí odkaz:
http://arxiv.org/abs/2410.05804
Incremental semantic segmentation endeavors to segment newly encountered classes while maintaining knowledge of old classes. However, existing methods either 1) lack guidance from class-specific knowledge (i.e., old class prototypes), leading to a bi
Externí odkaz:
http://arxiv.org/abs/2407.09047
Autor:
Po, Lai-Man, Liu, Yuyang, Wu, Haoxuan, Zhang, Tianqi, Yu, Wing-Yin, Wang, Zhuohan, Jiang, Zeyu, Li, Kun
This paper introduces Standard Basis LoRA (SBoRA), a novel parameter-efficient fine-tuning approach for Large Language Models that builds upon the pioneering works of Low-Rank Adaptation (LoRA) and Orthogonal Adaptation. SBoRA reduces the number of t
Externí odkaz:
http://arxiv.org/abs/2407.05413
Autor:
Ershov, Egor, Panshin, Artyom, Karasev, Oleg, Korchagin, Sergey, Lev, Shepelev, Startsev, Alexandr, Vladimirov, Daniil, Zaychenkova, Ekaterina, Banić, Nikola, Iarchuk, Dmitrii, Efimova, Maria, Timofte, Radu, Terekhin, Arseniy, Yue, Shuwei, Liu, Yuyang, Wei, Minchen, Xu, Lu, Zhang, Chao, Wang, Yasi, Kınlı, Furkan, Yılmaz, Doğa, Özcan, Barış, Kıraç, Furkan, Liu, Shuai, Xiao, Jingyuan, Feng, Chaoyu, Wang, Hao, Shao, Guangqi, Zhang, Yuqian, Huang, Yibin, Luo, Wei, Wang, Liming, Wang, Xiaotao, Lei, Lei, Zini, Simone, Rota, Claudio, Buzzelli, Marco, Bianco, Simone, Schettini, Raimondo, Guo, Jin, Liu, Tianli, Wu, Mohao, Shao, Ben, Yang, Qirui, Li, Xianghui, Cheng, Qihua, Zhang, Fangpu, Xu, Zhiqiang, Yang, Jingyu, Yue, Huanjing
This paper presents a review of the NTIRE 2024 challenge on night photography rendering. The goal of the challenge was to find solutions that process raw camera images taken in nighttime conditions, and thereby produce a photo-quality output images i
Externí odkaz:
http://arxiv.org/abs/2406.13007
Autor:
Goswami, Dipam, Soutif--Cormerais, Albin, Liu, Yuyang, Kamath, Sandesh, Twardowski, Bartłomiej, van de Weijer, Joost
Continual learning methods are known to suffer from catastrophic forgetting, a phenomenon that is particularly hard to counter for methods that do not store exemplars of previous tasks. Therefore, to reduce potential drift in the feature extractor, e
Externí odkaz:
http://arxiv.org/abs/2405.19074
Autor:
Lv, Liuzhenghao, Lin, Zongying, Li, Hao, Liu, Yuyang, Cui, Jiaxi, Chen, Calvin Yu-Chian, Yuan, Li, Tian, Yonghong
Large Language Models (LLMs) have achieved remarkable performance in multiple Natural Language Processing (NLP) tasks. Under the premise that protein sequences constitute the protein language, Protein Language Models(PLMs) have advanced the field of
Externí odkaz:
http://arxiv.org/abs/2402.16445
Autor:
Sójka, Damian, Liu, Yuyang, Goswami, Dipam, Cygert, Sebastian, Twardowski, Bartłomiej, van de Weijer, Joost
The goal of the challenge is to develop a test-time adaptation (TTA) method, which could adapt the model to gradually changing domains in video sequences for semantic segmentation task. It is based on a synthetic driving video dataset - SHIFT. The so
Externí odkaz:
http://arxiv.org/abs/2310.13533
Autor:
Huang, Yufei, Li, Siyuan, Su, Jin, Wu, Lirong, Zhang, Odin, Lin, Haitao, Qi, Jingqi, Liu, Zihan, Gao, Zhangyang, Liu, Yuyang, Zheng, Jiangbin, Li, Stan. ZQ.
Protein structure-based property prediction has emerged as a promising approach for various biological tasks, such as protein function prediction and sub-cellular location estimation. The existing methods highly rely on experimental protein structure
Externí odkaz:
http://arxiv.org/abs/2310.11466
Autor:
Liu, Yuyang, Dong, Weijun, Hu, Yingdong, Wen, Chuan, Yin, Zhao-Heng, Zhang, Chongjie, Gao, Yang
Humans often acquire new skills through observation and imitation. For robotic agents, learning from the plethora of unlabeled video demonstration data available on the Internet necessitates imitating the expert without access to its action, presenti
Externí odkaz:
http://arxiv.org/abs/2310.07433