3 citations · 3 across the 9 of their papers we have counts for
9 papers
CDRRM: Contrast-Driven Rubric Generation for Reliable and Interpretable Reward Modeling
Dengcan Liu, Fengkai Yang, Xiaohan Wang +7
Reward modeling is essential for aligning Large Language Models(LLMs) with human preferences, yet conventional reward models suffer from poor interpretability and heavy reliance on…
CFG: Control Classifier-Free Guidance via Score Discrepancy Analysis
Jiayang Gao, Tianyi Zheng, Jiayang Zou +9
Classifier-Free Guidance (CFG) is a cornerstone of modern conditional diffusion models, yet its reliance on the fixed or heuristic dynamic guidance weight is predominantly empirica…
HALO: Human-Aligned End-to-end Image Retargeting with Layered Transformations
Yiran Xu, Siqi Xie, Zhuofang Li +13
Image retargeting aims to change the aspect-ratio of an image while maintaining its content and structure with less visual artifacts. Existing methods still generate many artifacts…
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…
ArtVLM: Attribute Recognition Through Vision-Based Prefix Language Modeling
William Yicheng Zhu, Keren Ye, Junjie Ke +4
Recognizing and disentangling visual attributes from objects is a foundation to many computer vision applications. While large vision language representations like CLIP had largely…
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…