Toward Multi-class Anomaly Detection: Exploring Class-aware Unified Model against Inter-class Interference

• URL: http://arxiv.org/abs/2403.14213v1
• Date: Thu, 21 Mar 2024 08:08:31 GMT
• Title: Toward Multi-class Anomaly Detection: Exploring Class-aware Unified Model against Inter-class Interference
• Authors: Xi Jiang, Ying Chen, Qiang Nie, Jianlin Liu, Yong Liu, Chengjie Wang, Feng Zheng,
• Abstract summary: We introduce a Multi-class Implicit Neural representation Transformer for unified Anomaly Detection (MINT-AD) By learning the multi-class distributions, the model generates class-aware query embeddings for the transformer decoder. MINT-AD can project category and position information into a feature embedding space, further supervised by classification and prior probability loss functions.
• Score: 67.36605226797887
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the context of high usability in single-class anomaly detection models, recent academic research has become concerned about the more complex multi-class anomaly detection. Although several papers have designed unified models for this task, they often overlook the utility of class labels, a potent tool for mitigating inter-class interference. To address this issue, we introduce a Multi-class Implicit Neural representation Transformer for unified Anomaly Detection (MINT-AD), which leverages the fine-grained category information in the training stage. By learning the multi-class distributions, the model generates class-aware query embeddings for the transformer decoder, mitigating inter-class interference within the reconstruction model. Utilizing such an implicit neural representation network, MINT-AD can project category and position information into a feature embedding space, further supervised by classification and prior probability loss functions. Experimental results on multiple datasets demonstrate that MINT-AD outperforms existing unified training models.

Related papers

• Investigating Self-Supervised Methods for Label-Efficient Learning [27.029542823306866]
  We study different self supervised pretext tasks, namely contrastive learning, clustering, and masked image modelling for their low-shot capabilities. We introduce a framework involving both mask image modelling and clustering as pretext tasks, which performs better across all low-shot downstream tasks. When testing the model on full scale datasets, we show performance gains in multi-class classification, multi-label classification and semantic segmentation.
  arXiv  Detail & Related papers  (2024-06-25T10:56:03Z)
• Attend, Distill, Detect: Attention-aware Entropy Distillation for Anomaly Detection [4.0679780034913335]
  A knowledge-distillation based multi-class anomaly detection promises a low latency with a reasonably good performance but with a significant drop as compared to one-class version. We propose a DCAM (Distributed Convolutional Attention Module) which improves the distillation process between teacher and student networks.
  arXiv  Detail & Related papers  (2024-05-10T13:25:39Z)
• Multi-Class Anomaly Detection based on Regularized Discriminative Coupled hypersphere-based Feature Adaptation [85.15324009378344]
  This paper introduces a new model by including class discriminative properties obtained by a modified Regularized Discriminative Variational Auto-Encoder (RD-VAE) in the feature extraction process. The proposed Regularized Discriminative Coupled-hypersphere-based Feature Adaptation (RD-CFA) forms a solution for multi-class anomaly detection.
  arXiv  Detail & Related papers  (2023-11-24T14:26:07Z)
• Open-Vocabulary Video Anomaly Detection [57.552523669351636]
  Video anomaly detection (VAD) with weak supervision has achieved remarkable performance in utilizing video-level labels to discriminate whether a video frame is normal or abnormal. Recent studies attempt to tackle a more realistic setting, open-set VAD, which aims to detect unseen anomalies given seen anomalies and normal videos. This paper takes a step further and explores open-vocabulary video anomaly detection (OVVAD), in which we aim to leverage pre-trained large models to detect and categorize seen and unseen anomalies.
  arXiv  Detail & Related papers  (2023-11-13T02:54:17Z)
• mixed attention auto encoder for multi-class industrial anomaly detection [2.8519768339207356]
  We propose a unified mixed-attention auto encoder (MAAE) to implement multi-class anomaly detection with a single model. To alleviate the performance degradation due to the diverse distribution patterns of different categories, we employ spatial attentions and channel attentions. MAAE delivers remarkable performances on the benchmark dataset compared with the state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-09-22T08:17:48Z)
• Multiclass classification for multidimensional functional data through deep neural networks [0.22843885788439797]
  We introduce a novel functional deep neural network (mfDNN) as an innovative data mining classification tool. We consider sparse deep neural network architecture with linear unit (ReLU) activation function and minimize the cross-entropy loss in the multiclass classification setup. We demonstrate the performance of mfDNN on simulated data and several benchmark datasets from different application domains.
  arXiv  Detail & Related papers  (2023-05-22T16:56:01Z)
• Dual Prototypical Contrastive Learning for Few-shot Semantic Segmentation [55.339405417090084]
  We propose a dual prototypical contrastive learning approach tailored to the few-shot semantic segmentation (FSS) task. The main idea is to encourage the prototypes more discriminative by increasing inter-class distance while reducing intra-class distance in prototype feature space. We demonstrate that the proposed dual contrastive learning approach outperforms state-of-the-art FSS methods on PASCAL-5i and COCO-20i datasets.
  arXiv  Detail & Related papers  (2021-11-09T08:14:50Z)
• Learning Debiased and Disentangled Representations for Semantic Segmentation [52.35766945827972]
  We propose a model-agnostic and training scheme for semantic segmentation. By randomly eliminating certain class information in each training iteration, we effectively reduce feature dependencies among classes. Models trained with our approach demonstrate strong results on multiple semantic segmentation benchmarks.
  arXiv  Detail & Related papers  (2021-10-31T16:15:09Z)
• Multi-class Classification Based Anomaly Detection of Insider Activities [18.739091829480234]
  We propose an approach that combines generative model with supervised learning to perform multi-class classification using deep learning. The generative adversarial network (GAN) based insider detection model introduces Conditional Generative Adversarial Network (CGAN) to enrich minority class samples. The comprehensive experiments performed on the benchmark dataset demonstrates the effectiveness of introducing GAN derived synthetic data.
  arXiv  Detail & Related papers  (2021-02-15T00:08:39Z)
• Adaptive Object Detection with Dual Multi-Label Prediction [78.69064917947624]
  We propose a novel end-to-end unsupervised deep domain adaptation model for adaptive object detection. The model exploits multi-label prediction to reveal the object category information in each image. We introduce a prediction consistency regularization mechanism to assist object detection.
  arXiv  Detail & Related papers  (2020-03-29T04:23:22Z)

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.