12 papers
Reducing Class-Wise Performance Disparity via Margin Regularization
Beier Zhu, Kesen Zhao, Jiequan Cui +4
Deep neural networks often exhibit substantial disparities in class-wise accuracy, even when trained on class-balanced data, posing concerns for reliable deployment. While prior ef…
Hierarchical Semantic Alignment for Image Clustering
Xingyu Zhu, Beier Zhu, Yunfan Li +4
Image clustering is a classic problem in computer vision, which categorizes images into different groups. Recent studies utilize nouns as external semantic knowledge to improve clu…
DEPO: Dual-Efficiency Preference Optimization for LLM Agents
Sirui Chen, Mengshi Zhao, Lei Xu +5
Recent advances in large language models (LLMs) have greatly improved their reasoning and decision-making abilities when deployed as agents. Richer reasoning, however, often comes…
Adaptive Stochastic Coefficients for Accelerating Diffusion Sampling
Ruoyu Wang, Beier Zhu, Junzhi Li +2
Diffusion-based generative processes, formulated as differential equation solving, frequently balance computational speed with sample quality. Our theoretical investigation of ODE-…
Benchmarking and Bridging Emotion Conflicts for Multimodal Emotion Reasoning
Zhiyuan Han, Beier Zhu, Yanlong Xu +2
Despite their strong performance in multimodal emotion reasoning, existing Multimodal Large Language Models (MLLMs) often overlook the scenarios involving emotion conflicts, where…
Distilling Parallel Gradients for Fast ODE Solvers of Diffusion Models
Beier Zhu, Ruoyu Wang, Tong Zhao +2
Diffusion models (DMs) have achieved state-of-the-art generative performance but suffer from high sampling latency due to their sequential denoising nature. Existing solver-based a…