2 citations · 2 across the 32 of their papers we have counts for
15 papers · 1 filter
Parallel Diffusion Solver via Residual Dirichlet Policy Optimization
Ruoyu Wang, Ziyu Li, Beier Zhu +5
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…
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-…
Enhancing CLIP Robustness via Cross-Modality Alignment
Xingyu Zhu, Beier Zhu, Shuo Wang +2
Vision-language models (VLMs) such as CLIP demonstrate strong generalization in zero-shot classification but remain highly vulnerable to adversarial perturbations. Existing methods…
Real-Time Motion-Controllable Autoregressive Video Diffusion
Kesen Zhao, Jiaxin Shi, Beier Zhu +5
Real-time motion-controllable video generation remains challenging due to the inherent latency of bidirectional diffusion models and the lack of effective autoregressive (AR) appro…
Streaming Drag-Oriented Interactive Video Manipulation: Drag Anything, Anytime!
Junbao Zhou, Yuan Zhou, Kesen Zhao +4
Achieving streaming, fine-grained control over the outputs of autoregressive video diffusion models remains challenging, making it difficult to ensure that they consistently align…
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…
Generative Distribution Distillation
Jiequan Cui, Beier Zhu, Qingshan Xu +6
In this paper, we formulate the knowledge distillation (KD) as a conditional generative problem and propose the \textit{Generative Distribution Distillation (GenDD)} framework. A n…
Detail++: Training-Free Detail Enhancer for T2I Diffusion Models
Lifeng Chen, Jiner Wang, Zihao Pan +3
Recent advances in text-to-image (T2I) generation have led to impressive visual results. However, these models still face significant challenges when handling complex prompt, parti…
CoDi: Subject-Consistent and Pose-Diverse Text-to-Image Generation
Zhanxin Gao, Beier Zhu, Liang Yao +2
Subject-consistent generation (SCG)-aiming to maintain a consistent subject identity across diverse scenes-remains a challenge for text-to-image (T2I) models. Existing training-fre…
Dynamic Multimodal Prototype Learning in Vision-Language Models
Xingyu Zhu, Shuo Wang, Beier Zhu +6
With the increasing attention to pre-trained vision-language models (VLMs), \eg, CLIP, substantial efforts have been devoted to many downstream tasks, especially in test-time adapt…
Project-Probe-Aggregate: Efficient Fine-Tuning for Group Robustness
Beier Zhu, Jiequan Cui, Hanwang Zhang +1
While image-text foundation models have succeeded across diverse downstream tasks, they still face challenges in the presence of spurious correlations between the input and label.…
Generalized Kullback-Leibler Divergence Loss
Jiequan Cui, Beier Zhu, Qingshan Xu +5
In this paper, we delve deeper into the Kullback-Leibler (KL) Divergence loss and mathematically prove that it is equivalent to the Decoupled Kullback-Leibler (DKL) Divergence loss…