3 citations · 3 across the 7 of their papers we have counts for
6 papers · 1 filter
Calibrated Multi-Preference Optimization for Aligning Diffusion Models
Kyungmin Lee, Xiaohang Li, Qifei Wang +7
Aligning text-to-image (T2I) diffusion models with preference optimization is valuable for human-annotated datasets, but the heavy cost of manual data collection limits scalability…
Focus-N-Fix: Region-Aware Fine-Tuning for Text-to-Image Generation
Xiaoying Xing, Avinab Saha, Junfeng He +9
Text-to-image (T2I) generation has made significant advances in recent years, but challenges still remain in the generation of perceptual artifacts, misalignment with complex promp…
Cropper: Vision-Language Model for Image Cropping through In-Context Learning
Seung Hyun Lee, Jijun Jiang, Yiran Xu +10
The goal of image cropping is to identify visually appealing crops in an image. Conventional methods are trained on specific datasets and fail to adapt to new requirements. Recent…
Parrot: Pareto-optimal Multi-Reward Reinforcement Learning Framework for Text-to-Image Generation
Seung Hyun Lee, Yinxiao Li, Junjie Ke +11
Recent works have demonstrated that using reinforcement learning (RL) with multiple quality rewards can improve the quality of generated images in text-to-image (T2I) generation. H…
Rich Human Feedback for Text-to-Image Generation
Youwei Liang, Junfeng He, Gang Li +15
Recent Text-to-Image (T2I) generation models such as Stable Diffusion and Imagen have made significant progress in generating high-resolution images based on text descriptions. How…
UniAR: A Unified model for predicting human Attention and Responses on visual content
Peizhao Li, Junfeng He, Gang Li +10
Progress in human behavior modeling involves understanding both implicit, early-stage perceptual behavior, such as human attention, and explicit, later-stage behavior, such as subj…