Normalizing Flow based Feature Synthesis for Outlier-Aware Object Detection

• URL: http://arxiv.org/abs/2302.07106v3
• Date: Sun, 28 May 2023 16:51:14 GMT
• Title: Normalizing Flow based Feature Synthesis for Outlier-Aware Object Detection
• Authors: Nishant Kumar, Sini\v{s}a \v{S}egvi\'c, Abouzar Eslami, Stefan Gumhold
• Abstract summary: General-purpose object detectors like Faster R-CNN are prone to providing overconfident predictions for outlier objects. We propose a novel outlier-aware object detection framework that distinguishes outliers from inlier objects. Our approach significantly outperforms the state-of-the-art for outlier-aware object detection on both image and video datasets.
• Score: 8.249143014271887
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Real-world deployment of reliable object detectors is crucial for applications such as autonomous driving. However, general-purpose object detectors like Faster R-CNN are prone to providing overconfident predictions for outlier objects. Recent outlier-aware object detection approaches estimate the density of instance-wide features with class-conditional Gaussians and train on synthesized outlier features from their low-likelihood regions. However, this strategy does not guarantee that the synthesized outlier features will have a low likelihood according to the other class-conditional Gaussians. We propose a novel outlier-aware object detection framework that distinguishes outliers from inlier objects by learning the joint data distribution of all inlier classes with an invertible normalizing flow. The appropriate sampling of the flow model ensures that the synthesized outliers have a lower likelihood than inliers of all object classes, thereby modeling a better decision boundary between inlier and outlier objects. Our approach significantly outperforms the state-of-the-art for outlier-aware object detection on both image and video datasets. Code available at https://github.com/nish03/FFS

Related papers

• Regularized Contrastive Partial Multi-view Outlier Detection [76.77036536484114]
  We propose a novel method named Regularized Contrastive Partial Multi-view Outlier Detection (RCPMOD) In this framework, we utilize contrastive learning to learn view-consistent information and distinguish outliers by the degree of consistency. Experimental results on four benchmark datasets demonstrate that our proposed approach could outperform state-of-the-art competitors.
  arXiv  Detail & Related papers  (2024-08-02T14:34:27Z)
• 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)
• SSB: Simple but Strong Baseline for Boosting Performance of Open-Set Semi-Supervised Learning [106.46648817126984]
  In this paper, we study the challenging and realistic open-set SSL setting. The goal is to both correctly classify inliers and to detect outliers. We find that inlier classification performance can be largely improved by incorporating high-confidence pseudo-labeled data.
  arXiv  Detail & Related papers  (2023-11-17T15:14:40Z)
• LS-VOS: Identifying Outliers in 3D Object Detections Using Latent Space Virtual Outlier Synthesis [10.920640666237833]
  LiDAR-based 3D object detectors have achieved unprecedented speed and accuracy in autonomous driving applications. They are often biased toward high-confidence predictions or return detections where no real object is present. We propose LS-VOS, a framework for identifying outliers in 3D object detections.
  arXiv  Detail & Related papers  (2023-10-02T07:44:26Z)
• Quantile-based Maximum Likelihood Training for Outlier Detection [5.902139925693801]
  We introduce a quantile-based maximum likelihood objective for learning the inlier distribution to improve the outlier separation during inference. Our approach fits a normalizing flow to pre-trained discriminative features and detects the outliers according to the evaluated log-likelihood.
  arXiv  Detail & Related papers  (2023-08-20T22:27:54Z)
• Robust Outlier Rejection for 3D Registration with Variational Bayes [70.98659381852787]
  We develop a novel variational non-local network-based outlier rejection framework for robust alignment. We propose a voting-based inlier searching strategy to cluster the high-quality hypothetical inliers for transformation estimation.
  arXiv  Detail & Related papers  (2023-04-04T03:48:56Z)
• VOS: Learning What You Don't Know by Virtual Outlier Synthesis [23.67449949146439]
  Out-of-distribution (OOD) detection has received much attention lately due to its importance in the safe deployment of neural networks. Previous approaches rely on real outlier datasets for model regularization. We present VOS, a novel framework for OOD detection by adaptively synthesizing virtual outliers.
  arXiv  Detail & Related papers  (2022-02-02T18:43:01Z)
• Unsupervised Outlier Detection using Memory and Contrastive Learning [53.77693158251706]
  We think outlier detection can be done in the feature space by measuring the feature distance between outliers and inliers. We propose a framework, MCOD, using a memory module and a contrastive learning module. Our proposed MCOD achieves a considerable performance and outperforms nine state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-07-27T07:35:42Z)
• Homophily Outlier Detection in Non-IID Categorical Data [43.51919113927003]
  This work introduces a novel outlier detection framework and its two instances to identify outliers in categorical data. It first defines and incorporates distribution-sensitive outlier factors and their interdependence into a value-value graph-based representation. The learned value outlierness allows for either direct outlier detection or outlying feature selection.
  arXiv  Detail & Related papers  (2021-03-21T23:29:33Z)
• Probabilistic Outlier Detection and Generation [11.35109169978955]
  A Wasserstein double autoencoder is used to both detect and generate inliers and outliers. WALDO is evaluated on classical data sets for detection accuracy and robustness.
  arXiv  Detail & Related papers  (2020-12-22T22:42:56Z)
• Multi-scale Interactive Network for Salient Object Detection [91.43066633305662]
  We propose the aggregate interaction modules to integrate the features from adjacent levels. To obtain more efficient multi-scale features, the self-interaction modules are embedded in each decoder unit. Experimental results on five benchmark datasets demonstrate that the proposed method without any post-processing performs favorably against 23 state-of-the-art approaches.
  arXiv  Detail & Related papers  (2020-07-17T15:41:37Z)

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.