Human-free Prompted Based Anomaly Detection: prompt optimization with Meta-guiding prompt scheme
- URL: http://arxiv.org/abs/2406.18197v1
- Date: Wed, 26 Jun 2024 09:29:05 GMT
- Title: Human-free Prompted Based Anomaly Detection: prompt optimization with Meta-guiding prompt scheme
- Authors: Pi-Wei Chen, Jerry Chun-Wei Lin, Jia Ji, Feng-Hao Yeh, Chao-Chun Chen,
- Abstract summary: Pre-trained vision-language models (VLMs) are highly adaptable to various downstream tasks through few-shot learning.
Traditional methods depend on human-crafted prompts that require prior knowledge of specific anomaly types.
Our goal is to develop a human-free prompt-based anomaly detection framework that optimally learns prompts through data-driven methods.
- Score: 19.278039994431477
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Pre-trained vision-language models (VLMs) are highly adaptable to various downstream tasks through few-shot learning, making prompt-based anomaly detection a promising approach. Traditional methods depend on human-crafted prompts that require prior knowledge of specific anomaly types. Our goal is to develop a human-free prompt-based anomaly detection framework that optimally learns prompts through data-driven methods, eliminating the need for human intervention. The primary challenge in this approach is the lack of anomalous samples during the training phase. Additionally, the Vision Transformer (ViT)-based image encoder in VLMs is not ideal for pixel-wise anomaly segmentation due to a locality feature mismatch between the original image and the output feature map. To tackle the first challenge, we have developed the Object-Attention Anomaly Generation Module (OAGM) to synthesize anomaly samples for training. Furthermore, our Meta-Guiding Prompt-Tuning Scheme (MPTS) iteratively adjusts the gradient-based optimization direction of learnable prompts to avoid overfitting to the synthesized anomalies. For the second challenge, we propose Locality-Aware Attention, which ensures that each local patch feature attends only to nearby patch features, preserving the locality features corresponding to their original locations. This framework allows for the optimal prompt embeddings by searching in the continuous latent space via backpropagation, free from human semantic constraints. Additionally, the modified locality-aware attention improves the precision of pixel-wise anomaly segmentation.
Related papers
- GeneralAD: Anomaly Detection Across Domains by Attending to Distorted Features [68.14842693208465]
GeneralAD is an anomaly detection framework designed to operate in semantic, near-distribution, and industrial settings.
We propose a novel self-supervised anomaly generation module that employs straightforward operations like noise addition and shuffling to patch features.
We extensively evaluated our approach on ten datasets, achieving state-of-the-art results in six and on-par performance in the remaining.
arXiv Detail & Related papers (2024-07-17T09:27:41Z) - Do LLMs Understand Visual Anomalies? Uncovering LLM Capabilities in Zero-shot Anomaly Detection [11.045394540409363]
Large vision-language models (LVLMs) are proficient in deriving visual representations guided by natural language.
Recent explorations have utilized LVLMs to tackle zero-shot visual anomaly detection (VAD) challenges.
We present ALFA, a training-free approach designed to address these challenges via a unified model.
arXiv Detail & Related papers (2024-04-15T10:42:22Z) - Self-supervised Feature Adaptation for 3D Industrial Anomaly Detection [59.41026558455904]
We focus on multi-modal anomaly detection. Specifically, we investigate early multi-modal approaches that attempted to utilize models pre-trained on large-scale visual datasets.
We propose a Local-to-global Self-supervised Feature Adaptation (LSFA) method to finetune the adaptors and learn task-oriented representation toward anomaly detection.
arXiv Detail & Related papers (2024-01-06T07:30:41Z) - Continual-MAE: Adaptive Distribution Masked Autoencoders for Continual Test-Time Adaptation [49.827306773992376]
Continual Test-Time Adaptation (CTTA) is proposed to migrate a source pre-trained model to continually changing target distributions.
Our proposed method attains state-of-the-art performance in both classification and segmentation CTTA tasks.
arXiv Detail & Related papers (2023-12-19T15:34:52Z) - Small Object Detection via Coarse-to-fine Proposal Generation and
Imitation Learning [52.06176253457522]
We propose a two-stage framework tailored for small object detection based on the Coarse-to-fine pipeline and Feature Imitation learning.
CFINet achieves state-of-the-art performance on the large-scale small object detection benchmarks, SODA-D and SODA-A.
arXiv Detail & Related papers (2023-08-18T13:13:09Z) - LafitE: Latent Diffusion Model with Feature Editing for Unsupervised
Multi-class Anomaly Detection [12.596635603629725]
We develop a unified model to detect anomalies from objects belonging to multiple classes when only normal data is accessible.
We first explore the generative-based approach and investigate latent diffusion models for reconstruction.
We introduce a feature editing strategy that modifies the input feature space of the diffusion model to further alleviate identity shortcuts''
arXiv Detail & Related papers (2023-07-16T14:41:22Z) - MAPS: A Noise-Robust Progressive Learning Approach for Source-Free
Domain Adaptive Keypoint Detection [76.97324120775475]
Cross-domain keypoint detection methods always require accessing the source data during adaptation.
This paper considers source-free domain adaptive keypoint detection, where only the well-trained source model is provided to the target domain.
arXiv Detail & Related papers (2023-02-09T12:06:08Z) - Feature Alignment by Uncertainty and Self-Training for Source-Free
Unsupervised Domain Adaptation [1.6498361958317636]
Most unsupervised domain adaptation (UDA) methods assume that labeled source images are available during model adaptation.
We propose a source-free UDA method that uses only a pre-trained source model and unlabeled target images.
Our method captures the aleatoric uncertainty by incorporating data augmentation and trains the feature generator with two consistency objectives.
arXiv Detail & Related papers (2022-08-31T14:28:36Z) - Self-Supervised Training with Autoencoders for Visual Anomaly Detection [61.62861063776813]
We focus on a specific use case in anomaly detection where the distribution of normal samples is supported by a lower-dimensional manifold.
We adapt a self-supervised learning regime that exploits discriminative information during training but focuses on the submanifold of normal examples.
We achieve a new state-of-the-art result on the MVTec AD dataset -- a challenging benchmark for visual anomaly detection in the manufacturing domain.
arXiv Detail & Related papers (2022-06-23T14:16:30Z) - Indirect-Instant Attention Optimization for Crowd Counting in Dense
Scenes [3.8950254639440094]
Indirect-Instant Attention Optimization (IIAO) module based on SoftMax-Attention.
Special transformation yields relatively coarse features and, originally, the predictive fallibility of regions varies by crowd density distribution.
We tailor the Regional Correlation Loss (RCLoss) to retrieve continuous error-prone regions and smooth spatial information.
arXiv Detail & Related papers (2022-06-12T03:29:50Z) - Gleo-Det: Deep Convolution Feature-Guided Detector with Local Entropy
Optimization for Salient Points [5.955667705173262]
We propose to achieve fine constraint based on the requirement of repeatability while coarse constraint with guidance of deep convolution features.
With the guidance of convolution features, we define the cost function from both positive and negative sides.
arXiv Detail & Related papers (2022-04-27T12:40:21Z)
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.