Publications (57)
Beyond Words: Augmenting Discriminative Richness via Diffusions in Unsupervised Prompt Learning
Hairui Ren, Fan Tang, He Zhao +3
Fine-tuning vision-language models (VLMs) with large amounts of unlabeled data has recently garnered significant interest. However, a key challenge remains the lack of high-quality…
U-VAP: User-specified Visual Appearance Personalization via Decoupled Self Augmentation
You Wu, Kean Liu, Xiaoyue Mi +3
Concept personalization methods enable large text-to-image models to learn specific subjects (e.g., objects/poses/3D models) and synthesize renditions in new contexts. Given that t…
: Zero-shot Style Transfer via Attention Rearrangement
Yingying Deng, Xiangyu He, Fan Tang +1
Despite the remarkable progress in image style transfer, formulating style in the context of art is inherently subjective and challenging. In contrast to existing learning/tuning m…
StyTr: Image Style Transfer with Transformers
Yingying Deng, Fan Tang, Weiming Dong +4
The goal of image style transfer is to render an image with artistic features guided by a style reference while maintaining the original content. Owing to the locality in convoluti…
Draw Your Art Dream: Diverse Digital Art Synthesis with Multimodal Guided Diffusion
Nisha Huang, Fan Tang, Weiming Dong +1
Digital art synthesis is receiving increasing attention in the multimedia community because of engaging the public with art effectively. Current digital art synthesis methods usual…
CrossRectify: Leveraging Disagreement for Semi-supervised Object Detection
Chengcheng Ma, Xingjia Pan, Qixiang Ye +3
Semi-supervised object detection has recently achieved substantial progress. As a mainstream solution, the self-labeling-based methods train the detector on both labeled data and u…