KAnoCLIP: Zero-Shot Anomaly Detection through Knowledge-Driven Prompt Learning and Enhanced Cross-Modal Integration

• URL: http://arxiv.org/abs/2501.03786v1
• Date: Tue, 07 Jan 2025 13:51:41 GMT
• Title: KAnoCLIP: Zero-Shot Anomaly Detection through Knowledge-Driven Prompt Learning and Enhanced Cross-Modal Integration
• Authors: Chengyuan Li, Suyang Zhou, Jieping Kong, Lei Qi, Hui Xue,
• Abstract summary: Zero-shot anomaly detection (ZSAD) identifies anomalies without needing training samples from the target dataset.<n> vision-language models like CLIP show potential in ZSAD but have limitations.<n>We introduce KAnoCLIP, a novel ZSAD framework that leverages vision-language models.<n> KAnoCLIP achieves state-of-the-art performance in ZSAD across 12 industrial and medical datasets.
• Score: 9.688664292809785
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Zero-shot anomaly detection (ZSAD) identifies anomalies without needing training samples from the target dataset, essential for scenarios with privacy concerns or limited data. Vision-language models like CLIP show potential in ZSAD but have limitations: relying on manually crafted fixed textual descriptions or anomaly prompts is time-consuming and prone to semantic ambiguity, and CLIP struggles with pixel-level anomaly segmentation, focusing more on global semantics than local details. To address these limitations, We introduce KAnoCLIP, a novel ZSAD framework that leverages vision-language models. KAnoCLIP combines general knowledge from a Large Language Model (GPT-3.5) and fine-grained, image-specific knowledge from a Visual Question Answering system (Llama3) via Knowledge-Driven Prompt Learning (KnPL). KnPL uses a knowledge-driven (KD) loss function to create learnable anomaly prompts, removing the need for fixed text prompts and enhancing generalization. KAnoCLIP includes the CLIP visual encoder with V-V attention (CLIP-VV), Bi-Directional Cross-Attention for Multi-Level Cross-Modal Interaction (Bi-CMCI), and Conv-Adapter. These components preserve local visual semantics, improve local cross-modal fusion, and align global visual features with textual information, enhancing pixel-level anomaly detection. KAnoCLIP achieves state-of-the-art performance in ZSAD across 12 industrial and medical datasets, demonstrating superior generalization compared to existing methods.

Related papers

• Crane: Context-Guided Prompt Learning and Attention Refinement for Zero-Shot Anomaly Detections [50.343419243749054]
  Anomaly Detection (AD) involves identifying deviations from normal data distributions. We propose a novel approach that conditions the prompts of the text encoder based on image context extracted from the vision encoder. Our method achieves state-of-the-art performance, improving performance by 2% to 29% across different metrics on 14 datasets.
  arXiv  Detail & Related papers  (2025-04-15T10:42:25Z)
• Rethinking the Global Knowledge of CLIP in Training-Free Open-Vocabulary Semantic Segmentation [5.3499687969383345]
  We propose GCLIP to answer how to extract and utilize beneficial global knowledge of CLIP for TF-OVSS. We aim to equip the last-block attention with image-level properties while not introducing homogeneous attention patterns across patches.
  arXiv  Detail & Related papers  (2025-02-05T03:37:50Z)
• Point Cloud Understanding via Attention-Driven Contrastive Learning [64.65145700121442]
  Transformer-based models have advanced point cloud understanding by leveraging self-attention mechanisms. PointACL is an attention-driven contrastive learning framework designed to address these limitations. Our method employs an attention-driven dynamic masking strategy that guides the model to focus on under-attended regions.
  arXiv  Detail & Related papers  (2024-11-22T05:41:00Z)
• GlocalCLIP: Object-agnostic Global-Local Prompt Learning for Zero-shot Anomaly Detection [5.530212768657544]
  We introduce glocal contrastive learning to improve the complementary learning of global and local prompts.<n>The generalization performance of GlocalCLIP in ZSAD was demonstrated on 15 real-world datasets.
  arXiv  Detail & Related papers  (2024-11-09T05:22:13Z)
• DetailCLIP: Detail-Oriented CLIP for Fine-Grained Tasks [31.850184662606562]
  We introduce DetailCLIP: A Detail-Oriented CLIP to address the limitations of contrastive learning-based vision-language models. We show that DetailCLIP surpasses existing CLIP-based and traditional self-supervised learning (SSL) models in segmentation accuracy and exhibits superior generalization across diverse datasets.
  arXiv  Detail & Related papers  (2024-09-10T18:27:36Z)
• AdaCLIP: Adapting CLIP with Hybrid Learnable Prompts for Zero-Shot Anomaly Detection [14.916862007773341]
  This study introduces AdaCLIP for the ZSAD task, leveraging a pre-trained vision-language model (VLM), CLIP. AdaCLIP incorporates learnable prompts into CLIP and optimize them through training on auxiliary annotated anomaly detection data. Experiments conducted across 14 real-world anomaly detection datasets from industrial and medical domains indicate that AdaCLIP outperforms other ZSAD methods.
  arXiv  Detail & Related papers  (2024-07-22T16:52:37Z)
• Dual-Image Enhanced CLIP for Zero-Shot Anomaly Detection [58.228940066769596]
  We introduce a Dual-Image Enhanced CLIP approach, leveraging a joint vision-language scoring system. Our methods process pairs of images, utilizing each as a visual reference for the other, thereby enriching the inference process with visual context. Our approach significantly exploits the potential of vision-language joint anomaly detection and demonstrates comparable performance with current SOTA methods across various datasets.
  arXiv  Detail & Related papers  (2024-05-08T03:13:20Z)
• UMG-CLIP: A Unified Multi-Granularity Vision Generalist for Open-World Understanding [90.74967596080982]
  This paper extends Contrastive Language-Image Pre-training (CLIP) with multi-granularity alignment. We develop a Unified Multi-Granularity learning framework, termed UMG-CLIP, which simultaneously empowers the model with versatile perception abilities. With parameter efficient tuning, UMG-CLIP surpasses current widely used CLIP variants and achieves state-of-the-art performance on diverse image understanding benchmarks.
  arXiv  Detail & Related papers  (2024-01-12T06:35:09Z)
• Exploiting CLIP for Zero-shot HOI Detection Requires Knowledge Distillation at Multiple Levels [52.50670006414656]
  We employ CLIP, a large-scale pre-trained vision-language model, for knowledge distillation on multiple levels. To train our model, CLIP is utilized to generate HOI scores for both global images and local union regions. The model achieves strong performance, which is even comparable with some fully-supervised and weakly-supervised methods.
  arXiv  Detail & Related papers  (2023-09-10T16:27:54Z)
• Bootstrap Fine-Grained Vision-Language Alignment for Unified Zero-Shot Anomaly Localization [63.61093388441298]
  Contrastive Language-Image Pre-training models have shown promising performance on zero-shot visual recognition tasks. In this work, we propose AnoCLIP for zero-shot anomaly localization.
  arXiv  Detail & Related papers  (2023-08-30T10:35:36Z)

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.