22 citations · 35 across the 10 of their papers we have counts for
8 papers · 1 filter
Revealing the Two Sides of Data Augmentation: An Asymmetric Distillation-based Win-Win Solution for Open-Set Recognition
Yunbing Jia, Xiaoyu Kong, Fan Tang +3
In this paper, we reveal the two sides of data augmentation: enhancements in closed-set recognition correlate with a significant decrease in open-set recognition. Through empirical…
Break-for-Make: Modular Low-Rank Adaptations for Composable Content-Style Customization
Yu Xu, Fan Tang, Juan Cao +5
Personalized generation paradigms empower designers to customize visual intellectual properties with the help of textual descriptions by tuning or adapting pre-trained text-to-imag…
MotionCrafter: One-Shot Motion Customization of Diffusion Models
Yuxin Zhang, Fan Tang, Nisha Huang +4
The essence of a video lies in its dynamic motions, including character actions, object movements, and camera movements. While text-to-video generative diffusion models have recent…
Style-A-Video: Agile Diffusion for Arbitrary Text-based Video Style Transfer
Nisha Huang, Yuxin Zhang, Weiming Dong
Large-scale text-to-video diffusion models have demonstrated an exceptional ability to synthesize diverse videos. However, due to the lack of extensive text-to-video datasets and t…
A Unified Arbitrary Style Transfer Framework via Adaptive Contrastive Learning
Yuxin Zhang, Fan Tang, Weiming Dong +4
We present Unified Contrastive Arbitrary Style Transfer (UCAST), a novel style representation learning and transfer framework, which can fit in most existing arbitrary image style…
Region-Aware Diffusion for Zero-shot Text-driven Image Editing
Nisha Huang, Fan Tang, Weiming Dong +2
Image manipulation under the guidance of textual descriptions has recently received a broad range of attention. In this study, we focus on the regional editing of images with the g…