Zobrazeno 1 - 10
of 275
pro vyhledávání: '"Chen, Zhineng"'
Recent years have witnessed the success of large text-to-image diffusion models and their remarkable potential to generate high-quality images. The further pursuit of enhancing the editability of images has sparked significant interest in the downstr
Externí odkaz:
http://arxiv.org/abs/2409.08260
Autor:
Yang, Haibo, Chen, Yang, Pan, Yingwei, Yao, Ting, Chen, Zhineng, Wu, Zuxuan, Jiang, Yu-Gang, Mei, Tao
Learning radiance fields (NeRF) with powerful 2D diffusion models has garnered popularity for text-to-3D generation. Nevertheless, the implicit 3D representations of NeRF lack explicit modeling of meshes and textures over surfaces, and such surface-u
Externí odkaz:
http://arxiv.org/abs/2409.07454
The emergence of text-to-image generation models has led to the recognition that image enhancement, performed as post-processing, would significantly improve the visual quality of the generated images. Exploring diffusion models to enhance the genera
Externí odkaz:
http://arxiv.org/abs/2409.07451
Despite having tremendous progress in image-to-3D generation, existing methods still struggle to produce multi-view consistent images with high-resolution textures in detail, especially in the paradigm of 2D diffusion that lacks 3D awareness. In this
Externí odkaz:
http://arxiv.org/abs/2409.07452
Super-resolution (SR) aims to enhance the quality of low-resolution images and has been widely applied in medical imaging. We found that the design principles of most existing methods are influenced by SR tasks based on real-world images and do not t
Externí odkaz:
http://arxiv.org/abs/2409.07092
Scene text recognition (STR) pre-training methods have achieved remarkable progress, primarily relying on synthetic datasets. However, the domain gap between synthetic and real images poses a challenge in acquiring feature representations that align
Externí odkaz:
http://arxiv.org/abs/2408.05706
Deep neural networks (DNNs) are known to be vulnerable to adversarial attacks even under a black-box setting where the adversary can only query the model. Particularly, query-based black-box adversarial attacks estimate adversarial gradients based on
Externí odkaz:
http://arxiv.org/abs/2408.01978
Scene Text Recognition (STR) methods have demonstrated robust performance in word-level text recognition. However, in real applications the text image is sometimes long due to detected with multiple horizontal words. It triggers the requirement to bu
Externí odkaz:
http://arxiv.org/abs/2407.12317
Autor:
Wang, Jiaqi, Zang, Yuhang, Zhang, Pan, Chu, Tao, Cao, Yuhang, Sun, Zeyi, Liu, Ziyu, Dong, Xiaoyi, Wu, Tong, Lin, Dahua, Chen, Zeming, Wang, Zhi, Meng, Lingchen, Yao, Wenhao, Yang, Jianwei, Wu, Sihong, Chen, Zhineng, Wu, Zuxuan, Jiang, Yu-Gang, Wu, Peixi, Chai, Bosong, Nie, Xuan, Yan, Longquan, Wang, Zeyu, Zhou, Qifan, Wang, Boning, Huang, Jiaqi, Xu, Zunnan, Li, Xiu, Yuan, Kehong, Zu, Yanyan, Ha, Jiayao, Gao, Qiong, Jiao, Licheng
Detecting objects in real-world scenes is a complex task due to various challenges, including the vast range of object categories, and potential encounters with previously unknown or unseen objects. The challenges necessitate the development of publi
Externí odkaz:
http://arxiv.org/abs/2406.11739
Images suffer from heavy spatial redundancy because pixels in neighboring regions are spatially correlated. Existing approaches strive to overcome this limitation by reducing less meaningful image regions. However, current leading methods rely on sup
Externí odkaz:
http://arxiv.org/abs/2404.00680