Towards Open-World Object-based Anomaly Detection via Self-Supervised Outlier Synthesis

• URL: http://arxiv.org/abs/2407.15763v1
• Date: Mon, 22 Jul 2024 16:16:38 GMT
• Title: Towards Open-World Object-based Anomaly Detection via Self-Supervised Outlier Synthesis
• Authors: Brian K. S. Isaac-Medina, Yona Falinie A. Gaus, Neelanjan Bhowmik, Toby P. Breckon,
• Abstract summary: This work aims to bridge the gap by leveraging an open-world object detector and an OoD detector via virtual outlier. Our approach empowers our overall object detector architecture to learn anomaly-aware feature representations without relying on class labels. Our method establishes state-of-the-art performance on object-level anomaly detection, achieving an average recall score improvement of over 5.4% for natural images.
• Score: 15.748043194987075
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Object detection is a pivotal task in computer vision that has received significant attention in previous years. Nonetheless, the capability of a detector to localise objects out of the training distribution remains unexplored. Whilst recent approaches in object-level out-of-distribution (OoD) detection heavily rely on class labels, such approaches contradict truly open-world scenarios where the class distribution is often unknown. In this context, anomaly detection focuses on detecting unseen instances rather than classifying detections as OoD. This work aims to bridge this gap by leveraging an open-world object detector and an OoD detector via virtual outlier synthesis. This is achieved by using the detector backbone features to first learn object pseudo-classes via self-supervision. These pseudo-classes serve as the basis for class-conditional virtual outlier sampling of anomalous features that are classified by an OoD head. Our approach empowers our overall object detector architecture to learn anomaly-aware feature representations without relying on class labels, hence enabling truly open-world object anomaly detection. Empirical validation of our approach demonstrates its effectiveness across diverse datasets encompassing various imaging modalities (visible, infrared, and X-ray). Moreover, our method establishes state-of-the-art performance on object-level anomaly detection, achieving an average recall score improvement of over 5.4% for natural images and 23.5% for a security X-ray dataset compared to the current approaches. In addition, our method detects anomalies in datasets where current approaches fail. Code available at https://github.com/KostadinovShalon/oln-ssos.

Related papers

• On the Inherent Robustness of One-Stage Object Detection against Out-of-Distribution Data [6.7236795813629]
  We propose a novel detection algorithm for detecting unknown objects in image data. It exploits supervised dimensionality reduction techniques to mitigate the effects of the curse of dimensionality on the features extracted by the model. It utilizes high-resolution feature maps to identify potential unknown objects in an unsupervised fashion.
  arXiv  Detail & Related papers  (2024-11-07T10:15:25Z)
• Open-World Object Detection with Instance Representation Learning [1.8749305679160366]
  We propose a method to train an object detector that can both detect novel objects and extract semantically rich features in open-world conditions. Our method learns a robust and generalizable feature space, outperforming other OWOD-based feature extraction methods.
  arXiv  Detail & Related papers  (2024-09-24T13:13:34Z)
• 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)
• Bayesian Detector Combination for Object Detection with Crowdsourced Annotations [49.43709660948812]
  Acquiring fine-grained object detection annotations in unconstrained images is time-consuming, expensive, and prone to noise. We propose a novel Bayesian Detector Combination (BDC) framework to more effectively train object detectors with noisy crowdsourced annotations. BDC is model-agnostic, requires no prior knowledge of the annotators' skill level, and seamlessly integrates with existing object detection models.
  arXiv  Detail & Related papers  (2024-07-10T18:00:54Z)
• Unsupervised Recognition of Unknown Objects for Open-World Object Detection [28.787586991713535]
  Open-World Object Detection (OWOD) extends object detection problem to a realistic and dynamic scenario. Current OWOD models, such as ORE and OW-DETR, focus on pseudo-labeling regions with high objectness scores as unknowns. This paper proposes a novel approach that learns an unsupervised discriminative model to recognize true unknown objects.
  arXiv  Detail & Related papers  (2023-08-31T08:17:29Z)
• Weakly-supervised Contrastive Learning for Unsupervised Object Discovery [52.696041556640516]
  Unsupervised object discovery is promising due to its ability to discover objects in a generic manner. We design a semantic-guided self-supervised learning model to extract high-level semantic features from images. We introduce Principal Component Analysis (PCA) to localize object regions.
  arXiv  Detail & Related papers  (2023-07-07T04:03:48Z)
• Open World DETR: Transformer based Open World Object Detection [60.64535309016623]
  We propose a two-stage training approach named Open World DETR for open world object detection based on Deformable DETR. We fine-tune the class-specific components of the model with a multi-view self-labeling strategy and a consistency constraint. Our proposed method outperforms other state-of-the-art open world object detection methods by a large margin.
  arXiv  Detail & Related papers  (2022-12-06T13:39:30Z)
• Contrastive Object Detection Using Knowledge Graph Embeddings [72.17159795485915]
  We compare the error statistics of the class embeddings learned from a one-hot approach with semantically structured embeddings from natural language processing or knowledge graphs. We propose a knowledge-embedded design for keypoint-based and transformer-based object detection architectures.
  arXiv  Detail & Related papers  (2021-12-21T17:10:21Z)
• Slender Object Detection: Diagnoses and Improvements [74.40792217534]
  In this paper, we are concerned with the detection of a particular type of objects with extreme aspect ratios, namely textbfslender objects. For a classical object detection method, a drastic drop of $18.9%$ mAP on COCO is observed, if solely evaluated on slender objects.
  arXiv  Detail & Related papers  (2020-11-17T09:39:42Z)

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.