Analyzing VLM-Based Approaches for Anomaly Classification and Segmentation

• URL: http://arxiv.org/abs/2601.13440v1
• Date: Mon, 19 Jan 2026 22:55:30 GMT
• Title: Analyzing VLM-Based Approaches for Anomaly Classification and Segmentation
• Authors: Mohit Kakda, Mirudula Shri Muthukumaran, Uttapreksha Patel, Lawrence Swaminathan Xavier Prince,
• Abstract summary: Vision-Language Models (VLMs) have revolutionized anomaly detection by enabling zero-shot and few-shot defect identification without extensive labeled datasets.<n>This project presents a comprehensive analysis of VLM-based approaches for anomaly classification (AC) and anomaly segmentation (AS)
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision-Language Models (VLMs), particularly CLIP, have revolutionized anomaly detection by enabling zero-shot and few-shot defect identification without extensive labeled datasets. By learning aligned representations of images and text, VLMs facilitate anomaly classification and segmentation through natural language descriptions of normal and abnormal states, eliminating traditional requirements for task-specific training or defect examples. This project presents a comprehensive analysis of VLM-based approaches for anomaly classification (AC) and anomaly segmentation (AS). We systematically investigate key architectural paradigms including sliding window-based dense feature extraction (WinCLIP), multi-stage feature alignment with learnable projections (AprilLab framework), and compositional prompt ensemble strategies. Our analysis evaluates these methods across critical dimensions: feature extraction mechanisms, text-visual alignment strategies, prompt engineering techniques, zero-shot versus few-shot trade-offs, computational efficiency, and cross-domain generalization. Through rigorous experimentation on benchmarks such as MVTec AD and VisA, we compare classification accuracy, segmentation precision, and inference efficiency. The primary contribution is a foundational understanding of how and why VLMs succeed in anomaly detection, synthesizing practical insights for method selection and identifying current limitations. This work aims to facilitate informed adoption of VLM-based methods in industrial quality control and guide future research directions.

Related papers

• Advancing Adaptive Multi-Stage Video Anomaly Reasoning: A Benchmark Dataset and Method [96.63801368613177]
  We present a new task that elevates video anomaly analysis from descriptive understanding to structured, multi-stage reasoning.<n>We present a new dataset with 8,641 videos, totaling more than 50,000 samples, making it one of the largest datasets for video anomaly understanding.<n>Building upon the proposed task and dataset, we develop an end-to-end MLLM-based VAR model termed Vad-R1-Plus, which supports adaptive hierarchical reasoning and risk-aware decision making.
  arXiv  Detail & Related papers  (2026-01-15T08:09:04Z)
• Towards Fine-Grained Vision-Language Alignment for Few-Shot Anomaly Detection [65.29550320117526]
  We propose a novel framework named FineGrainedAD to improve anomaly localization performance.<n> Experiments demonstrate that the proposed FineGrainedAD achieves superior overall performance in few-shot settings.
  arXiv  Detail & Related papers  (2025-10-30T13:09:00Z)
• Continual Learning for VLMs: A Survey and Taxonomy Beyond Forgetting [70.83781268763215]
  Vision-language models (VLMs) have achieved impressive performance across diverse multimodal tasks by leveraging large-scale pre-training.<n>VLMs face unique challenges such as cross-modal feature drift, parameter interference due to shared architectures, and zero-shot capability erosion.<n>This survey aims to serve as a comprehensive and diagnostic reference for researchers developing lifelong vision-language systems.
  arXiv  Detail & Related papers  (2025-08-06T09:03:10Z)
• Discrete Tokenization for Multimodal LLMs: A Comprehensive Survey [69.45421620616486]
  This work presents the first structured taxonomy and analysis of discrete tokenization methods designed for large language models (LLMs)<n>We categorize 8 representative VQ variants that span classical and modern paradigms and analyze their algorithmic principles, training dynamics, and integration challenges with LLM pipelines.<n>We identify key challenges including codebook collapse, unstable gradient estimation, and modality-specific encoding constraints.
  arXiv  Detail & Related papers  (2025-07-21T10:52:14Z)
• VMAD: Visual-enhanced Multimodal Large Language Model for Zero-Shot Anomaly Detection [19.79027968793026]
  Zero-shot anomaly detection (ZSAD) recognizes and localizes anomalies in previously unseen objects. Existing ZSAD methods are limited by closed-world settings, struggling to unseen defects with predefined prompts. We propose a novel framework VMAD (Visual-enhanced MLLM Anomaly Detection) that enhances MLLM with visual-based IAD knowledge and fine-grained perception.
  arXiv  Detail & Related papers  (2024-09-30T09:51:29Z)
• Sample-agnostic Adversarial Perturbation for Vision-Language Pre-training Models [7.350203999073509]
  Recent studies on AI security have highlighted the vulnerability of Vision-Language Pre-training models to subtle yet intentionally designed perturbations in images and texts. To the best of our knowledge, it is the first work through multimodal decision boundaries to explore the creation of a universal, sample-agnostic perturbation that applies to any image.
  arXiv  Detail & Related papers  (2024-08-06T06:25:39Z)
• Designing Algorithms Empowered by Language Models: An Analytical Framework, Case Studies, and Insights [86.06371692309972]
  This work presents an analytical framework for the design and analysis of large language models (LLMs)-based algorithms.<n>Our proposed framework serves as an attempt to mitigate such headaches.
  arXiv  Detail & Related papers  (2024-07-20T07:39:07Z)
• An Empirical Study of Automated Vulnerability Localization with Large Language Models [21.84971967029474]
  Large Language Models (LLMs) have shown potential in various domains, yet their effectiveness in vulnerability localization remains underexplored. Our investigation encompasses 10+ leading LLMs suitable for code analysis, including ChatGPT and various open-source models. We explore the efficacy of these LLMs using 4 distinct paradigms: zero-shot learning, one-shot learning, discriminative fine-tuning, and generative fine-tuning.
  arXiv  Detail & Related papers  (2024-03-30T08:42:10Z)
• Learning Prompt-Enhanced Context Features for Weakly-Supervised Video Anomaly Detection [37.99031842449251]
  Video anomaly detection under weak supervision presents significant challenges. We present a weakly supervised anomaly detection framework that focuses on efficient context modeling and enhanced semantic discriminability. Our approach significantly improves the detection accuracy of certain anomaly sub-classes, underscoring its practical value and efficacy.
  arXiv  Detail & Related papers  (2023-06-26T06:45:16Z)
• Learning Common Rationale to Improve Self-Supervised Representation for Fine-Grained Visual Recognition Problems [61.11799513362704]
  We propose learning an additional screening mechanism to identify discriminative clues commonly seen across instances and classes. We show that a common rationale detector can be learned by simply exploiting the GradCAM induced from the SSL objective.
  arXiv  Detail & Related papers  (2023-03-03T02:07:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.