Weakly-supervised Video Anomaly Detection with Contrastive Learning of Long and Short-range Temporal Features

• URL: http://arxiv.org/abs/2101.10030v1
• Date: Mon, 25 Jan 2021 12:04:00 GMT
• Title: Weakly-supervised Video Anomaly Detection with Contrastive Learning of Long and Short-range Temporal Features
• Authors: Yu Tian, Guansong Pang, Yuanhong Chen, Rajvinder Singh, Johan W. Verjans, Gustavo Carneiro
• Abstract summary: We propose a novel method, named Multi-scale Temporal Network trained with top-K Contrastive Multiple Instance Learning (MTN-KMIL) Our method outperforms several state-of-the-art methods by a large margin on three benchmark data sets.
• Score: 26.474395581531194
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we address the problem of weakly-supervised video anomaly detection, in which given video-level labels for training, we aim to identify in test videos, the snippets containing abnormal events. Although current methods based on multiple instance learning (MIL) show effective detection performance, they ignore important video temporal dependencies. Also, the number of abnormal snippets can vary per anomaly video, which complicates the training process of MIL-based methods because they tend to focus on the most abnormal snippet -- this can cause it to mistakenly select a normal snippet instead of an abnormal snippet, and also to fail to select all abnormal snippets available. We propose a novel method, named Multi-scale Temporal Network trained with top-K Contrastive Multiple Instance Learning (MTN-KMIL), to address the issues above. The main contributions of MTN-KMIL are: 1) a novel synthesis of a pyramid of dilated convolutions and a self-attention mechanism, with the former capturing the multi-scale short-range temporal dependencies between snippets and the latter capturing long-range temporal dependencies; and 2) a novel contrastive MIL learning method that enforces large margins between the top-K normal and abnormal video snippets at the feature representation level and anomaly score level, resulting in accurate anomaly discrimination. Extensive experiments show that our method outperforms several state-of-the-art methods by a large margin on three benchmark data sets (ShanghaiTech, UCF-Crime and XD-Violence). The code is available at https://github.com/tianyu0207/MTN-KMIL

Related papers

• Dynamic Erasing Network Based on Multi-Scale Temporal Features for Weakly Supervised Video Anomaly Detection [103.92970668001277]
  We propose a Dynamic Erasing Network (DE-Net) for weakly supervised video anomaly detection. We first propose a multi-scale temporal modeling module, capable of extracting features from segments of varying lengths. Then, we design a dynamic erasing strategy, which dynamically assesses the completeness of the detected anomalies.
  arXiv  Detail & Related papers  (2023-12-04T09:40:11Z)
• Delving into CLIP latent space for Video Anomaly Recognition [24.37974279994544]
  We introduce the novel method AnomalyCLIP, the first to combine Large Language and Vision (LLV) models, such as CLIP. Our approach specifically involves manipulating the latent CLIP feature space to identify the normal event subspace. When anomalous frames are projected onto these directions, they exhibit a large feature magnitude if they belong to a particular class.
  arXiv  Detail & Related papers  (2023-10-04T14:01:55Z)
• Don't Miss Out on Novelty: Importance of Novel Features for Deep Anomaly Detection [64.21963650519312]
  Anomaly Detection (AD) is a critical task that involves identifying observations that do not conform to a learned model of normality. We propose a novel approach to AD using explainability to capture such novel features as unexplained observations in the input space. Our approach establishes a new state-of-the-art across multiple benchmarks, handling diverse anomaly types.
  arXiv  Detail & Related papers  (2023-10-01T21:24:05Z)
• Weakly-Supervised Video Anomaly Detection with Snippet Anomalous Attention [22.985681654402153]
  We propose an Anomalous Attention mechanism for weakly-supervised anomaly detection. Our approach takes into account snippet-level encoded features without the supervision of pseudo labels.
  arXiv  Detail & Related papers  (2023-09-28T10:03:58Z)
• Towards Video Anomaly Retrieval from Video Anomaly Detection: New Benchmarks and Model [70.97446870672069]
  Video anomaly detection (VAD) has been paid increasing attention due to its potential applications. Video Anomaly Retrieval ( VAR) aims to pragmatically retrieve relevant anomalous videos by cross-modalities. We present two benchmarks, UCFCrime-AR and XD-Violence, constructed on top of prevalent anomaly datasets.
  arXiv  Detail & Related papers  (2023-07-24T06:22:37Z)
• Unbiased Multiple Instance Learning for Weakly Supervised Video Anomaly Detection [74.80595632328094]
  Multiple Instance Learning (MIL) is prevailing in Weakly Supervised Video Anomaly Detection (WSVAD) We propose a new MIL framework: Unbiased MIL (UMIL), to learn unbiased anomaly features that improve WSVAD.
  arXiv  Detail & Related papers  (2023-03-22T08:11:22Z)
• Anomaly detection in surveillance videos using transformer based attention model [3.2968779106235586]
  This research suggests using a weakly supervised strategy to avoid annotating anomalous segments in training videos. The proposed framework is validated on real-world dataset i.e. ShanghaiTech Campus dataset.
  arXiv  Detail & Related papers  (2022-06-03T12:19:39Z)
• Anomaly Crossing: A New Method for Video Anomaly Detection as Cross-domain Few-shot Learning [32.0713939637202]
  Video anomaly detection aims to identify abnormal events that occurred in videos. Most previous approaches learn only from normal videos using unsupervised or semi-supervised methods. We propose a new learning paradigm by making full use of both normal and abnormal videos for video anomaly detection.
  arXiv  Detail & Related papers  (2021-12-12T20:49:38Z)
• UBnormal: New Benchmark for Supervised Open-Set Video Anomaly Detection [103.06327681038304]
  We propose a supervised open-set benchmark composed of multiple virtual scenes for video anomaly detection. Unlike existing data sets, we introduce abnormal events annotated at the pixel level at training time. We show that UBnormal can enhance the performance of a state-of-the-art anomaly detection framework.
  arXiv  Detail & Related papers  (2021-11-16T17:28:46Z)
• Anomaly Detection in Video Sequences: A Benchmark and Computational Model [25.25968958782081]
  We contribute a new Large-scale Anomaly Detection (LAD) database as the benchmark for anomaly detection in video sequences. It contains 2000 video sequences including normal and abnormal video clips with 14 anomaly categories including crash, fire, violence, etc. It provides the annotation data, including video-level labels (abnormal/normal video, anomaly type) and frame-level labels (abnormal/normal video frame) to facilitate anomaly detection. We propose a multi-task deep neural network to solve anomaly detection as a fully-supervised learning problem.
  arXiv  Detail & Related papers  (2021-06-16T06:34:38Z)
• Self-trained Deep Ordinal Regression for End-to-End Video Anomaly Detection [114.9714355807607]
  We show that applying self-trained deep ordinal regression to video anomaly detection overcomes two key limitations of existing methods. We devise an end-to-end trainable video anomaly detection approach that enables joint representation learning and anomaly scoring without manually labeled normal/abnormal data.
  arXiv  Detail & Related papers  (2020-03-15T08:44:55Z)

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.