Uncertainty Aware Proposal Segmentation for Unknown Object Detection

• URL: http://arxiv.org/abs/2111.12866v1
• Date: Thu, 25 Nov 2021 01:53:05 GMT
• Title: Uncertainty Aware Proposal Segmentation for Unknown Object Detection
• Authors: Yimeng Li, Jana Kosecka
• Abstract summary: This paper proposes to exploit additional predictions of semantic segmentation models and quantifying its confidences. We use object proposals generated by Region Proposal Network (RPN) and adapt distance aware uncertainty estimation of semantic segmentation. The augmented object proposals are then used to train a classifier for known vs. unknown objects categories.
• Score: 13.249453757295083
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent efforts in deploying Deep Neural Networks for object detection in real world applications, such as autonomous driving, assume that all relevant object classes have been observed during training. Quantifying the performance of these models in settings when the test data is not represented in the training set has mostly focused on pixel-level uncertainty estimation techniques of models trained for semantic segmentation. This paper proposes to exploit additional predictions of semantic segmentation models and quantifying its confidences, followed by classification of object hypotheses as known vs. unknown, out of distribution objects. We use object proposals generated by Region Proposal Network (RPN) and adapt distance aware uncertainty estimation of semantic segmentation using Radial Basis Functions Networks (RBFN) for class agnostic object mask prediction. The augmented object proposals are then used to train a classifier for known vs. unknown objects categories. Experimental results demonstrate that the proposed method achieves parallel performance to state of the art methods for unknown object detection and can also be used effectively for reducing object detectors' false positive rate. Our method is well suited for applications where prediction of non-object background categories obtained by semantic segmentation is reliable.

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)
• 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)
• Pixel-wise Gradient Uncertainty for Convolutional Neural Networks applied to Out-of-Distribution Segmentation [0.43512163406552007]
  We present a method for obtaining uncertainty scores from pixel-wise loss gradients which can be computed efficiently during inference. Our experiments show the ability of our method to identify wrong pixel classifications and to estimate prediction quality at negligible computational overhead.
  arXiv  Detail & Related papers  (2023-03-13T08:37:59Z)
• Object Detection in Aerial Images with Uncertainty-Aware Graph Network [61.02591506040606]
  We propose a novel uncertainty-aware object detection framework with a structured-graph, where nodes and edges are denoted by objects. We refer to our model as Uncertainty-Aware Graph network for object DETection (UAGDet)
  arXiv  Detail & Related papers  (2022-08-23T07:29:03Z)
• The Familiarity Hypothesis: Explaining the Behavior of Deep Open Set Methods [86.39044549664189]
  Anomaly detection algorithms for feature-vector data identify anomalies as outliers, but outlier detection has not worked well in deep learning. This paper proposes the Familiarity Hypothesis that these methods succeed because they are detecting the absence of familiar learned features rather than the presence of novelty. The paper concludes with a discussion of whether familiarity detection is an inevitable consequence of representation learning.
  arXiv  Detail & Related papers  (2022-03-04T18:32:58Z)
• NUQ: Nonparametric Uncertainty Quantification for Deterministic Neural Networks [151.03112356092575]
  We show the principled way to measure the uncertainty of predictions for a classifier based on Nadaraya-Watson's nonparametric estimate of the conditional label distribution. We demonstrate the strong performance of the method in uncertainty estimation tasks on a variety of real-world image datasets.
  arXiv  Detail & Related papers  (2022-02-07T12:30:45Z)
• Uncertainty-Aware Model Adaptation for Unsupervised Cross-Domain Object Detection [12.807987076435928]
  This work tackles the unsupervised cross-domain object detection problem. It aims to generalize a pre-trained object detector to a new target domain without labels.
  arXiv  Detail & Related papers  (2021-08-28T09:37:18Z)
• Learning Uncertainty For Safety-Oriented Semantic Segmentation In Autonomous Driving [77.39239190539871]
  We show how uncertainty estimation can be leveraged to enable safety critical image segmentation in autonomous driving. We introduce a new uncertainty measure based on disagreeing predictions as measured by a dissimilarity function. We show experimentally that our proposed approach is much less computationally intensive at inference time than competing methods.
  arXiv  Detail & Related papers  (2021-05-28T09:23:05Z)
• Class-agnostic Object Detection [16.97782147401037]
  We propose class-agnostic object detection as a new problem that focuses on detecting objects irrespective of their object-classes. Specifically, the goal is to predict bounding boxes for all objects in an image but not their object-classes. We propose training and evaluation protocols for benchmarking class-agnostic detectors to advance future research in this domain.
  arXiv  Detail & Related papers  (2020-11-28T19:22:38Z)
• 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)
• Probabilistic Deep Learning for Instance Segmentation [9.62543698736491]
  We propose a generic method to obtain model-inherent uncertainty estimates within proposal-free instance segmentation models. We evaluate our method on the BBBC010 C. elegans dataset, where it yields competitive performance.
  arXiv  Detail & Related papers  (2020-08-24T19:51:48Z)

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.