Zobrazeno 1 - 10
of 632
pro vyhledávání: '"Li, Changlin"'
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
Autor:
Ren, Pengzhen, Li, Changlin, Xu, Hang, Zhu, Yi, Wang, Guangrun, Liu, Jianzhuang, Chang, Xiaojun, Liang, Xiaodan
Recently, great success has been made in learning visual representations from text supervision, facilitating the emergence of text-supervised semantic segmentation. However, existing works focus on pixel grouping and cross-modal semantic alignment, w
Externí odkaz:
http://arxiv.org/abs/2302.10307
Capitalizing on the rapid development of neural networks, recent video frame interpolation (VFI) methods have achieved notable improvements. However, they still fall short for real-world videos containing large motions. Complex deformation and/or occ
Externí odkaz:
http://arxiv.org/abs/2211.11309
Automatic data augmentation (AutoAugment) strategies are indispensable in supervised data-efficient training protocols of vision transformers, and have led to state-of-the-art results in supervised learning. Despite the success, its development and a
Externí odkaz:
http://arxiv.org/abs/2210.08458