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Unleashing Text-to-Image Diffusion Prior for Zero-Shot Image Captioning
Jianjie Luo, Jingwen Chen, Yehao Li +4
Recently, zero-shot image captioning has gained increasing attention, where only text data is available for training. The remarkable progress in text-to-image diffusion model prese…
Improving Text-guided Object Inpainting with Semantic Pre-inpainting
Yifu Chen, Jingwen Chen, Yingwei Pan +4
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…
Improving Virtual Try-On with Garment-focused Diffusion Models
Siqi Wan, Yehao Li, Jingwen Chen +4
Diffusion models have led to the revolutionizing of generative modeling in numerous image synthesis tasks. Nevertheless, it is not trivial to directly apply diffusion models for sy…
Prompt Refinement with Image Pivot for Text-to-Image Generation
Jingtao Zhan, Qingyao Ai, Yiqun Liu +5
For text-to-image generation, automatically refining user-provided natural language prompts into the keyword-enriched prompts favored by systems is essential for the user experienc…
Boosting Diffusion Models with Moving Average Sampling in Frequency Domain
Yurui Qian, Qi Cai, Yingwei Pan +4
Diffusion models have recently brought a powerful revolution in image generation. Despite showing impressive generative capabilities, most of these models rely on the current sampl…
HIRI-ViT: Scaling Vision Transformer with High Resolution Inputs
Ting Yao, Yehao Li, Yingwei Pan +1
The hybrid deep models of Vision Transformer (ViT) and Convolution Neural Network (CNN) have emerged as a powerful class of backbones for vision tasks. Scaling up the input resolut…