Drawing the Same Bounding Box Twice? Coping Noisy Annotations in Object Detection with Repeated Labels

• URL: http://arxiv.org/abs/2309.09742v1
• Date: Mon, 18 Sep 2023 13:08:44 GMT
• Title: Drawing the Same Bounding Box Twice? Coping Noisy Annotations in Object Detection with Repeated Labels
• Authors: David Tschirschwitz, Christian Benz, Morris Florek, Henrik Norderhus, Benno Stein, Volker Rodehorst
• Abstract summary: We propose a novel localization algorithm that adapts well-established ground truth estimation methods. Our algorithm also shows superior performance during training on the TexBiG dataset.
• Score: 6.872072177648135
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The reliability of supervised machine learning systems depends on the accuracy and availability of ground truth labels. However, the process of human annotation, being prone to error, introduces the potential for noisy labels, which can impede the practicality of these systems. While training with noisy labels is a significant consideration, the reliability of test data is also crucial to ascertain the dependability of the results. A common approach to addressing this issue is repeated labeling, where multiple annotators label the same example, and their labels are combined to provide a better estimate of the true label. In this paper, we propose a novel localization algorithm that adapts well-established ground truth estimation methods for object detection and instance segmentation tasks. The key innovation of our method lies in its ability to transform combined localization and classification tasks into classification-only problems, thus enabling the application of techniques such as Expectation-Maximization (EM) or Majority Voting (MJV). Although our main focus is the aggregation of unique ground truth for test data, our algorithm also shows superior performance during training on the TexBiG dataset, surpassing both noisy label training and label aggregation using Weighted Boxes Fusion (WBF). Our experiments indicate that the benefits of repeated labels emerge under specific dataset and annotation configurations. The key factors appear to be (1) dataset complexity, the (2) annotator consistency, and (3) the given annotation budget constraints.

Related papers

• Substituting Data Annotation with Balanced Updates and Collective Loss in Multi-label Text Classification [19.592985329023733]
  Multi-label text classification (MLTC) is the task of assigning multiple labels to a given text. We study the MLTC problem in annotation-free and scarce-annotation settings in which the magnitude of available supervision signals is linear to the number of labels. Our method follows three steps, (1) mapping input text into a set of preliminary label likelihoods by natural language inference using a pre-trained language model, (2) calculating a signed label dependency graph by label descriptions, and (3) updating the preliminary label likelihoods with message passing along the label dependency graph.
  arXiv  Detail & Related papers  (2023-09-24T04:12:52Z)
• Multi-Label Noise Transition Matrix Estimation with Label Correlations: Theory and Algorithm [73.94839250910977]
  Noisy multi-label learning has garnered increasing attention due to the challenges posed by collecting large-scale accurate labels. The introduction of transition matrices can help model multi-label noise and enable the development of statistically consistent algorithms. We propose a novel estimator that leverages label correlations without the need for anchor points or precise fitting of noisy class posteriors.
  arXiv  Detail & Related papers  (2023-09-22T08:35:38Z)
• Robust Assignment of Labels for Active Learning with Sparse and Noisy Annotations [0.17188280334580192]
  Supervised classification algorithms are used to solve a growing number of real-life problems around the globe. Unfortunately, acquiring good-quality annotations for many tasks is infeasible or too expensive to be done in practice. We propose two novel annotation unification algorithms that utilize unlabeled parts of the sample space.
  arXiv  Detail & Related papers  (2023-07-25T19:40:41Z)
• Class-Distribution-Aware Pseudo Labeling for Semi-Supervised Multi-Label Learning [97.88458953075205]
  Pseudo-labeling has emerged as a popular and effective approach for utilizing unlabeled data. This paper proposes a novel solution called Class-Aware Pseudo-Labeling (CAP) that performs pseudo-labeling in a class-aware manner.
  arXiv  Detail & Related papers  (2023-05-04T12:52:18Z)
• Debiased Pseudo Labeling in Self-Training [77.83549261035277]
  Deep neural networks achieve remarkable performances on a wide range of tasks with the aid of large-scale labeled datasets. To mitigate the requirement for labeled data, self-training is widely used in both academia and industry by pseudo labeling on readily-available unlabeled data. We propose Debiased, in which the generation and utilization of pseudo labels are decoupled by two independent heads.
  arXiv  Detail & Related papers  (2022-02-15T02:14:33Z)
• S3: Supervised Self-supervised Learning under Label Noise [53.02249460567745]
  In this paper we address the problem of classification in the presence of label noise. In the heart of our method is a sample selection mechanism that relies on the consistency between the annotated label of a sample and the distribution of the labels in its neighborhood in the feature space. Our method significantly surpasses previous methods on both CIFARCIFAR100 with artificial noise and real-world noisy datasets such as WebVision and ANIMAL-10N.
  arXiv  Detail & Related papers  (2021-11-22T15:49:20Z)
• Learning with Noisy Labels by Targeted Relabeling [52.0329205268734]
  Crowdsourcing platforms are often used to collect datasets for training deep neural networks. We propose an approach which reserves a fraction of annotations to explicitly relabel highly probable labeling errors.
  arXiv  Detail & Related papers  (2021-10-15T20:37:29Z)
• Enhancing Label Correlation Feedback in Multi-Label Text Classification via Multi-Task Learning [6.1538971100140145]
  We introduce a novel approach with multi-task learning to enhance label correlation feedback. We propose two auxiliary label co-occurrence prediction tasks to enhance label correlation learning.
  arXiv  Detail & Related papers  (2021-06-06T12:26:14Z)
• EvidentialMix: Learning with Combined Open-set and Closed-set Noisy Labels [30.268962418683955]
  We study a new variant of the noisy label problem that combines the open-set and closed-set noisy labels. Our results show that our method produces superior classification results and better feature representations than previous state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-11-11T11:15:32Z)
• Exploiting Context for Robustness to Label Noise in Active Learning [47.341705184013804]
  We address the problems of how a system can identify which of the queried labels are wrong and how a multi-class active learning system can be adapted to minimize the negative impact of label noise. We construct a graphical representation of the unlabeled data to encode these relationships and obtain new beliefs on the graph when noisy labels are available. This is demonstrated in three different applications: scene classification, activity classification, and document classification.
  arXiv  Detail & Related papers  (2020-10-18T18:59:44Z)

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.