most citedHolmes-VAD: Towards Unbiased and Explainable Video Anomaly Detection via Multi-modal LLM

3 citations · 5 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV2025

HBridge: H-Shape Bridging of Heterogeneous Experts for Unified Multimodal Understanding and Generation

Xiang Wang, Zhifei Zhang, He Zhang +11

Recent unified models integrate understanding experts (e.g., LLMs) with generative experts (e.g., diffusion models), achieving strong multimodal performance. However, recent advanc…

cs.CV2025

UniAnimate-DiT: Human Image Animation with Large-Scale Video Diffusion Transformer

Xiang Wang, Shiwei Zhang, Longxiang Tang +4

This report presents UniAnimate-DiT, an advanced project that leverages the cutting-edge and powerful capabilities of the open-source Wan2.1 model for consistent human image animat…

cs.CV2025

Taming Consistency Distillation for Accelerated Human Image Animation

Xiang Wang, Shiwei Zhang, Hangjie Yuan +5

Recent advancements in human image animation have been propelled by video diffusion models, yet their reliance on numerous iterative denoising steps results in high inference costs…

cs.CV2024

Replace Anyone in Videos

Xiang Wang, Shiwei Zhang, Haonan Qiu +7

The field of controllable human-centric video generation has witnessed remarkable progress, particularly with the advent of diffusion models. However, achieving precise and localiz…

cs.CV2024★ 3 cited

Holmes-VAD: Towards Unbiased and Explainable Video Anomaly Detection via Multi-modal LLM

Huaxin Zhang, Xiaohao Xu, Xiang Wang +6

Towards open-ended Video Anomaly Detection (VAD), existing methods often exhibit biased detection when faced with challenging or unseen events and lack interpretability. To address…

cs.CV2024★ 2 cited

GlanceVAD: Exploring Glance Supervision for Label-efficient Video Anomaly Detection

Huaxin Zhang, Xiang Wang, Xiaohao Xu +6

In recent years, video anomaly detection has been extensively investigated in both unsupervised and weakly supervised settings to alleviate costly temporal labeling. Despite signif…