Learning to Adapt to Unseen Abnormal Activities under Weak Supervision

• URL: http://arxiv.org/abs/2203.13610v1
• Date: Fri, 25 Mar 2022 12:15:44 GMT
• Title: Learning to Adapt to Unseen Abnormal Activities under Weak Supervision
• Authors: Jaeyoo Park, Junha Kim, Bohyung Han
• Abstract summary: We present a meta-learning framework for weakly supervised anomaly detection in videos. Our framework learns to adapt to unseen types of abnormal activities effectively when only video-level annotations of binary labels are available.
• Score: 43.40900198498228
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a meta-learning framework for weakly supervised anomaly detection in videos, where the detector learns to adapt to unseen types of abnormal activities effectively when only video-level annotations of binary labels are available. Our work is motivated by the fact that existing methods suffer from poor generalization to diverse unseen examples. We claim that an anomaly detector equipped with a meta-learning scheme alleviates the limitation by leading the model to an initialization point for better optimization. We evaluate the performance of our framework on two challenging datasets, UCF-Crime and ShanghaiTech. The experimental results demonstrate that our algorithm boosts the capability to localize unseen abnormal events in a weakly supervised setting. Besides the technical contributions, we perform the annotation of missing labels in the UCF-Crime dataset and make our task evaluated effectively.

Related papers

• Distilling Aggregated Knowledge for Weakly-Supervised Video Anomaly Detection [11.250490586786878]
  Video anomaly detection aims to develop automated models capable of identifying abnormal events in surveillance videos. We show that distilling knowledge from aggregated representations into a relatively simple model achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2024-06-05T00:44:42Z)
• Anomaly Detection by Adapting a pre-trained Vision Language Model [48.225404732089515]
  We present a unified framework named CLIP-ADA for Anomaly Detection by Adapting a pre-trained CLIP model. We introduce the learnable prompt and propose to associate it with abnormal patterns through self-supervised learning. We achieve the state-of-the-art 97.5/55.6 and 89.3/33.1 on MVTec-AD and VisA for anomaly detection and localization.
  arXiv  Detail & Related papers  (2024-03-14T15:35:07Z)
• 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)
• Active anomaly detection based on deep one-class classification [9.904380236739398]
  We tackle two essential problems of active learning for Deep SVDD: query strategy and semi-supervised learning method. First, rather than solely identifying anomalies, our query strategy selects uncertain samples according to an adaptive boundary. Second, we apply noise contrastive estimation in training a one-class classification model to incorporate both labeled normal and abnormal data effectively.
  arXiv  Detail & Related papers  (2023-09-18T03:56:45Z)
• 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)
• Cluster-level pseudo-labelling for source-free cross-domain facial expression recognition [94.56304526014875]
  We propose the first Source-Free Unsupervised Domain Adaptation (SFUDA) method for Facial Expression Recognition (FER) Our method exploits self-supervised pretraining to learn good feature representations from the target data. We validate the effectiveness of our method in four adaptation setups, proving that it consistently outperforms existing SFUDA methods when applied to FER.
  arXiv  Detail & Related papers  (2022-10-11T08:24:50Z)
• An Outlier Exposure Approach to Improve Visual Anomaly Detection Performance for Mobile Robots [76.36017224414523]
  We consider the problem of building visual anomaly detection systems for mobile robots. Standard anomaly detection models are trained using large datasets composed only of non-anomalous data. We tackle the problem of exploiting these data to improve the performance of a Real-NVP anomaly detection model.
  arXiv  Detail & Related papers  (2022-09-20T15:18:13Z)
• MIST: Multiple Instance Self-Training Framework for Video Anomaly Detection [76.80153360498797]
  We develop a multiple instance self-training framework (MIST) to efficiently refine task-specific discriminative representations. MIST is composed of 1) a multiple instance pseudo label generator, which adapts a sparse continuous sampling strategy to produce more reliable clip-level pseudo labels, and 2) a self-guided attention boosted feature encoder. Our method performs comparably to or even better than existing supervised and weakly supervised methods, specifically obtaining a frame-level AUC 94.83% on ShanghaiTech.
  arXiv  Detail & Related papers  (2021-04-04T15:47:14Z)
• Meta-learning One-class Classifiers with Eigenvalue Solvers for Supervised Anomaly Detection [55.888835686183995]
  We propose a neural network-based meta-learning method for supervised anomaly detection. We experimentally demonstrate that the proposed method achieves better performance than existing anomaly detection and few-shot learning methods.
  arXiv  Detail & Related papers  (2021-03-01T01:43:04Z)

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.