5 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…
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…