arXiv Computer Vision

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

arXiv AI

Jul 22

OPD-IAD: From Language Judgment to Industrial Anomaly Detection via On-Policy Self-Distillation

arXiv:2607. 18850v1 Announce Type: cross Abstract: Large vision-language models (LVLMs) have recently shown strong potential for industrial anomaly detection (IAD) by providing image-level anomaly judgments and interpretable defect reasoning.

By Shuimu Chen, Jing Jin, Nan Su, Hongbo Xu, Zebang Cheng, Wenming Yang, Fei Ma, Guijin Wang

llms efficiency multimodal

arXiv AI

Aug 13

Glance, Scrutinize, and Think: Advancing Video Anomaly Detection from Training-Free to Agentic Reasoning

arXiv:2608. 11260v1 Announce Type: new Abstract: Video Anomaly Detection (VAD) aims to identify anomalous events and localize their temporal intervals.

By Shibo Gao, Peipei Yang, Xu-Yao Zhang, Linlin Huang

llms agents reinforcement-learning fine-tuning multimodal benchmarks safety

arXiv AI

Jul 21

O-VAD: Industrial Video Anomaly Detection through Object-Centric Tracking and Reasoning

arXiv:2607. 18142v1 Announce Type: cross Abstract: Industrial Video Anomaly Detection (IVAD) aims to identify anomalous objects and events in an industrial process, which is crucial for modern manufacturing and quality control systems.

By Mei Yuan, Qi Long, Qifeng Wu, Zhenyang Li, Yizhou Zhao, Lei Wang, Yang Liu, Min Xu

agents fine-tuning multimodal

arXiv AI

Jun 9

Unification of Closed-Open Industrial Detection Scenarios: New Large-Scale Benchmarks,Challenges and Baselines

arXiv:2606. 07953v1 Announce Type: new Abstract: Large-scale Visual-Language Models (LVLMs) have achieved remarkable success in natural visual tasks, yet their application to industrial defect detection remains challenging due to two fundamental limitations: (i) the scarcity of large-scale industrial datasets that cover diverse defect categories across multiple domains, and (ii) the reliance on manual prompts (points, boxes, masks) that introduce subjective noise and lack text-visual interaction for fine-grained understanding.

By Zekai Zhang, Jinglin Zhang, Qinghui Chen, Gang Li, Da Chen, Shuainan Jing, He Wang, Dagang Li, Cong Liu, Cong Bai, Shengyong Chen

llms multimodal benchmarks safety

arXiv AI

Jun 2

A Structured Benchmark for Text-Guided Anomaly Detection: When Language Stops Conditioning the Decision

arXiv:2606. 01992v1 Announce Type: cross Abstract: Industrial anomaly detection has historically been a unimodal task.

By Stefano Samele, Eugenio Lomurno, Teodora Jovanovic, Sanjay Shivakumar Manohar, Alberto Crivellaro, Matteo Matteucci

llms rag multimodal benchmarks

arXiv AI

Jul 14

Answer-Conditioned Chain-of-Thought Distillation for Few-Shot Industrial Vision with Small VLMs

arXiv:2607. 10666v1 Announce Type: cross Abstract: Deploying AI-based visual inspection in manufacturing is hard because requirements change often, new defect types appear, and large labeled datasets are rarely available.

By Shubham Rao

llms fine-tuning efficiency multimodal

arXiv AI

Jun 9

Zero-Shot Learning in Industrial Scenarios: New Large-Scale Benchmark, Challenges and Baseline

arXiv:2606. 07965v1 Announce Type: new Abstract: Large Visual Language Models (LVLMs) have achieved remarkable success in vision tasks.

By Zekai Zhang, Qinghui Chen, Maomao Xiong, Shijiao Ding, Zhanzhi Su, Xinjie Yao, Yiming Sun, Cong Bai, Jinglin Zhang

llms fine-tuning multimodal benchmarks

arXiv Machine Learning

Aug 11

From Benchmark Performance to Tool Deployment: Human-in-the-Loop Anomaly Detection

arXiv:2608. 07770v1 Announce Type: new Abstract: Automated anomaly detection methods often report strong performance on curated academic benchmarks, but their behavior under real-world industrial conditions is less clear.

By Mike Szklarzewski, CJ George, Gavin Smithson, Christopher Stokes, Dakota Fulp, William M. Jones, Benjamin Wynn, Alexander Ur, Agit Yesiloz, Clint Kallenbach, Mark Swartz, Nathan DeBardeleben, Sharmistha Chakrabarti

benchmarks data-engineering

arXiv AI

Aug 11

VTO: Visual Tool Orchestration for Video Anomaly Detection

arXiv:2608. 08219v1 Announce Type: cross Abstract: Video anomaly detection (VAD) is a critical yet challenging task due to the complex and diverse nature of real-world scenarios.

By Rui Wang, Yeteng Wu, Xianling Zhang, Mengshi Qi

agents reinforcement-learning fine-tuning multimodal benchmarks safety

arXiv AI

Jul 21

RobustMAD: Evaluating Real-World Robustness of Multimodal Small Language Models for Deployable Anomaly Detection Assistants

arXiv:2607. 16243v1 Announce Type: cross Abstract: Multimodal industrial anomaly inspection assistants are a critical component of next-generation smart factories, enabling interactive vision-language-based querying.

By Anushiya Arunan, Xin Li, Yan Qin, U-Xuan Tan, Nhu Khue Vuong, Xiaoli Li, Chau Yuen

llms multimodal benchmarks safety

Hugging Face Trending Papers

Aug 10

ADOPD: Reference-Privileged On-Policy Distillation for MLLM-Based Industrial Anomaly Detection

Industrial anomaly detection (IAD) requires identifying fine-grained deviations from normal visual patterns. Multimodal large language models (MLLMs) can improve recognition accuracy by comparing query images with references at inference time, but these benefits rely on additional retrieval and processing.

llms efficiency multimodal benchmarks

arXiv AI

Jul 28

DuoAD: Leveraging [CLS] Dual Characteristics for Training-Free Few-Shot Anomaly Detection

arXiv:2607. 23924v1 Announce Type: cross Abstract: Vision foundation models have enabled strong training-free anomaly detection (AD).

By Jyun-Ze Tang, Po-Han Huang, Ming-Ching Chang, Chih-Fan Hsu, Jeng-Lin Li

llms rag benchmarks

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