activity
20222026
most citedA Unified Model for Multi-class Anomaly Detection

87 citations · 89 across the 5 of their papers we have counts for

collaborators

6 papers

cs.CV2026

InspectorGPT: A Comparative Reasoning Enhanced VLM for Comprehensive Industrial Anomaly Detection

Weifei Chen, Honghao Zhang, Zhiyuan You +1

Industrial anomaly detection is a critical component of modern manufacturing. Most traditional unsupervised methods rely on modelling normal feature distributions, inherently limit…

cs.AI2026

RouteMoA: Dynamic Routing without Pre-Inference Boosts Efficient Mixture-of-Agents

Jize Wang, Han Wu, Zhiyuan You +9

Mixture-of-Agents (MoA) improves LLM performance through layered collaboration, but its dense topology raises costs and latency. Existing methods employ LLM judges to filter respon…

cs.CL2024

SAIL: Sample-Centric In-Context Learning for Document Information Extraction

Jinyu Zhang, Zhiyuan You, Jize Wang +1

Document Information Extraction (DIE) aims to extract structured information from Visually Rich Documents (VRDs). Previous full-training approaches have demonstrated strong perform…

cs.CV2022★ 2 cited

ADTR: Anomaly Detection Transformer with Feature Reconstruction

Zhiyuan You, Kai Yang, Wenhan Luo +3

Anomaly detection with only prior knowledge from normal samples attracts more attention because of the lack of anomaly samples. Existing CNN-based pixel reconstruction approaches s…

cs.CV2022★ 87 cited

A Unified Model for Multi-class Anomaly Detection

Zhiyuan You, Lei Cui, Yujun Shen +4

Despite the rapid advance of unsupervised anomaly detection, existing methods require to train separate models for different objects. In this work, we present UniAD that accomplish…

cs.CV2022

Few-shot Object Counting with Similarity-Aware Feature Enhancement

Zhiyuan You, Kai Yang, Wenhan Luo +3

This work studies the problem of few-shot object counting, which counts the number of exemplar objects (i.e., described by one or several support images) occurring in the query ima…