Zobrazeno 1 - 10
of 689
pro vyhledávání: '"Li Changlin"'
Autor:
Kong, Weijie, Tian, Qi, Zhang, Zijian, Min, Rox, Dai, Zuozhuo, Zhou, Jin, Xiong, Jiangfeng, Li, Xin, Wu, Bo, Zhang, Jianwei, Wu, Kathrina, Lin, Qin, Yuan, Junkun, Long, Yanxin, Wang, Aladdin, Wang, Andong, Li, Changlin, Huang, Duojun, Yang, Fang, Tan, Hao, Wang, Hongmei, Song, Jacob, Bai, Jiawang, Wu, Jianbing, Xue, Jinbao, Wang, Joey, Wang, Kai, Liu, Mengyang, Li, Pengyu, Li, Shuai, Wang, Weiyan, Yu, Wenqing, Deng, Xinchi, Li, Yang, Chen, Yi, Cui, Yutao, Peng, Yuanbo, Yu, Zhentao, He, Zhiyu, Xu, Zhiyong, Zhou, Zixiang, Xu, Zunnan, Tao, Yangyu, Lu, Qinglin, Liu, Songtao, Zhou, Daquan, Wang, Hongfa, Yang, Yong, Wang, Di, Liu, Yuhong, Jiang, Jie, Zhong, Caesar
Recent advancements in video generation have significantly impacted daily life for both individuals and industries. However, the leading video generation models remain closed-source, resulting in a notable performance gap between industry capabilitie
Externí odkaz:
http://arxiv.org/abs/2412.03603
Maritime vessel maneuvers, characterized by their inherent complexity and indeterminacy, requires vessel trajectory prediction system capable of modeling the multi-modality nature of future motion states. Conventional stochastic trajectory prediction
Externí odkaz:
http://arxiv.org/abs/2410.09550
Autor:
Li, Changlin, Zhang, Jiawei, Lin, Sihao, Yang, Zongxin, Liang, Junwei, Liang, Xiaodan, Chang, Xiaojun
The rapid advancements in Large Vision Models (LVMs), such as Vision Transformers (ViTs) and diffusion models, have led to an increasing demand for computational resources, resulting in substantial financial and environmental costs. This growing chal
Externí odkaz:
http://arxiv.org/abs/2410.00350
Autor:
Liu, Jia, Li, Changlin, Sun, Qirui, Ming, Jiahui, Fang, Chen, Wang, Jue, Zeng, Bing, Liu, Shuaicheng
Fine-tuning advanced diffusion models for high-quality image stylization usually requires large training datasets and substantial computational resources, hindering their practical applicability. We propose Ada-Adapter, a novel framework for few-shot
Externí odkaz:
http://arxiv.org/abs/2407.05552
Previous methods for Video Frame Interpolation (VFI) have encountered challenges, notably the manifestation of blur and ghosting effects. These issues can be traced back to two pivotal factors: unavoidable motion errors and misalignment in supervisio
Externí odkaz:
http://arxiv.org/abs/2404.06692
Autor:
Wang, Guangrun, Li, Changlin, Yuan, Liuchun, Peng, Jiefeng, Xian, Xiaoyu, Liang, Xiaodan, Chang, Xiaojun, Lin, Liang
Neural Architecture Search (NAS), aiming at automatically designing neural architectures by machines, has been considered a key step toward automatic machine learning. One notable NAS branch is the weight-sharing NAS, which significantly improves sea
Externí odkaz:
http://arxiv.org/abs/2403.01326
Vision Transformers (ViTs) have demonstrated outstanding performance in computer vision tasks, yet their high computational complexity prevents their deployment in computing resource-constrained environments. Various token pruning techniques have bee
Externí odkaz:
http://arxiv.org/abs/2310.05654
Aspect-based sentiment classification is a crucial problem in fine-grained sentiment analysis, which aims to predict the sentiment polarity of the given aspect according to its context. Previous works have made remarkable progress in leveraging atten
Externí odkaz:
http://arxiv.org/abs/2308.11447
GrowCLIP: Data-aware Automatic Model Growing for Large-scale Contrastive Language-Image Pre-training
Autor:
Deng, Xinchi, Shi, Han, Huang, Runhui, Li, Changlin, Xu, Hang, Han, Jianhua, Kwok, James, Zhao, Shen, Zhang, Wei, Liang, Xiaodan
Cross-modal pre-training has shown impressive performance on a wide range of downstream tasks, benefiting from massive image-text pairs collected from the Internet. In practice, online data are growing constantly, highlighting the importance of the a
Externí odkaz:
http://arxiv.org/abs/2308.11331
Autor:
Cai, Kaixin, Ren, Pengzhen, Zhu, Yi, Xu, Hang, Liu, Jianzhuang, Li, Changlin, Wang, Guangrun, Liang, Xiaodan
Recently, semantic segmentation models trained with image-level text supervision have shown promising results in challenging open-world scenarios. However, these models still face difficulties in learning fine-grained semantic alignment at the pixel
Externí odkaz:
http://arxiv.org/abs/2308.04829