Zobrazeno 1 - 10
of 3 268
pro vyhledávání: '"Tang, Xu"'
Autor:
Yan, Cilin, Wang, Haochen, Jiang, Xiaolong, Hu, Yao, Tang, Xu, Kang, Guoliang, Gavves, Efstratios
Contrastive Vision-Language Pre-training(CLIP) demonstrates impressive zero-shot capability. The key to improve the adaptation of CLIP to downstream task with few exemplars lies in how to effectively model and transfer the useful knowledge embedded i
Externí odkaz:
http://arxiv.org/abs/2406.11252
Video-language pre-training is a typical and challenging problem that aims at learning visual and textual representations from large-scale data in a self-supervised way. Existing pre-training approaches either captured the correspondence of image-tex
Externí odkaz:
http://arxiv.org/abs/2405.07202
Predicting future human pose is a fundamental application for machine intelligence, which drives robots to plan their behavior and paths ahead of time to seamlessly accomplish human-robot collaboration in real-world 3D scenarios. Despite encouraging
Externí odkaz:
http://arxiv.org/abs/2405.02911
Autor:
Wang, Rui, Guo, Hailong, Liu, Jiaming, Li, Huaxia, Zhao, Haibo, Tang, Xu, Hu, Yao, Tang, Hao, Li, Peipei
In this paper, we introduce StableGarment, a unified framework to tackle garment-centric(GC) generation tasks, including GC text-to-image, controllable GC text-to-image, stylized GC text-to-image, and robust virtual try-on. The main challenge lies in
Externí odkaz:
http://arxiv.org/abs/2403.10783
Autor:
Zhang, Yuxuan, Wei, Lifu, Zhang, Qing, Song, Yiren, Liu, Jiaming, Li, Huaxia, Tang, Xu, Hu, Yao, Zhao, Haibo
Current makeup transfer methods are limited to simple makeup styles, making them difficult to apply in real-world scenarios. In this paper, we introduce Stable-Makeup, a novel diffusion-based makeup transfer method capable of robustly transferring a
Externí odkaz:
http://arxiv.org/abs/2403.07764
There has been significant progress in personalized image synthesis with methods such as Textual Inversion, DreamBooth, and LoRA. Yet, their real-world applicability is hindered by high storage demands, lengthy fine-tuning processes, and the need for
Externí odkaz:
http://arxiv.org/abs/2401.07519
Autor:
Li, Shanglin, Zeng, Bohan, Feng, Yutang, Gao, Sicheng, Liu, Xuhui, Liu, Jiaming, Lin, Li, Tang, Xu, Hu, Yao, Liu, Jianzhuang, Zhang, Baochang
Recent advances in vision-language models like Stable Diffusion have shown remarkable power in creative image synthesis and editing.However, most existing text-to-image editing methods encounter two obstacles: First, the text prompt needs to be caref
Externí odkaz:
http://arxiv.org/abs/2312.16794
Autor:
Zhang, Yuxuan, Song, Yiren, Liu, Jiaming, Wang, Rui, Yu, Jinpeng, Tang, Hao, Li, Huaxia, Tang, Xu, Hu, Yao, Pan, Han, Jing, Zhongliang
Recent advancements in subject-driven image generation have led to zero-shot generation, yet precise selection and focus on crucial subject representations remain challenging. Addressing this, we introduce the SSR-Encoder, a novel architecture design
Externí odkaz:
http://arxiv.org/abs/2312.16272
In this paper, we prove that the uniqueness of blowup at the maximum point of coincidence set of the superconductivity problem, mainly based on the Weiss-type and Monneau-type monotonicity formulas, and the proof of the main results in this paper is
Externí odkaz:
http://arxiv.org/abs/2309.07642
Autor:
Zhang, Xiangrong, Zhang, Tianyang, Wang, Guanchun, Zhu, Peng, Tang, Xu, Jia, Xiuping, Jiao, Licheng
Remote sensing object detection (RSOD), one of the most fundamental and challenging tasks in the remote sensing field, has received longstanding attention. In recent years, deep learning techniques have demonstrated robust feature representation capa
Externí odkaz:
http://arxiv.org/abs/2309.06751