Building Blocks for Robust and Effective Semi-Supervised Real-World Object Detection

• URL: http://arxiv.org/abs/2503.18903v1
• Date: Mon, 24 Mar 2025 17:15:24 GMT
• Title: Building Blocks for Robust and Effective Semi-Supervised Real-World Object Detection
• Authors: Moussa Kassem Sbeyti, Nadja Klein, Azarm Nowzad, Fikret Sivrikaya, Sahin Albayrak,
• Abstract summary: Semi-supervised object detection (SSOD) based on pseudo-labeling significantly reduces dependence on large labeled datasets.<n>However, real-world applications of SSOD often face critical challenges, including class imbalance, label noise, and labeling errors.<n>We present an in-depth analysis of SSOD under real-world conditions, uncovering causes of suboptimal pseudo-labeling and key trade-offs between label quality and quantity.
• Score: 1.188383832081829
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Semi-supervised object detection (SSOD) based on pseudo-labeling significantly reduces dependence on large labeled datasets by effectively leveraging both labeled and unlabeled data. However, real-world applications of SSOD often face critical challenges, including class imbalance, label noise, and labeling errors. We present an in-depth analysis of SSOD under real-world conditions, uncovering causes of suboptimal pseudo-labeling and key trade-offs between label quality and quantity. Based on our findings, we propose four building blocks that can be seamlessly integrated into an SSOD framework. Rare Class Collage (RCC): a data augmentation method that enhances the representation of rare classes by creating collages of rare objects. Rare Class Focus (RCF): a stratified batch sampling strategy that ensures a more balanced representation of all classes during training. Ground Truth Label Correction (GLC): a label refinement method that identifies and corrects false, missing, and noisy ground truth labels by leveraging the consistency of teacher model predictions. Pseudo-Label Selection (PLS): a selection method for removing low-quality pseudo-labeled images, guided by a novel metric estimating the missing detection rate while accounting for class rarity. We validate our methods through comprehensive experiments on autonomous driving datasets, resulting in up to 6% increase in SSOD performance. Overall, our investigation and novel, data-centric, and broadly applicable building blocks enable robust and effective SSOD in complex, real-world scenarios. Code is available at https://mos-ks.github.io/publications.

Related papers

• Collaborative Feature-Logits Contrastive Learning for Open-Set Semi-Supervised Object Detection [75.02249869573994]
  In open-set scenarios, the unlabeled dataset contains both in-distribution (ID) classes and out-of-distribution (OOD) classes.<n>Applying semi-supervised detectors in such settings can lead to misclassifying OOD class as ID classes.<n>We propose a simple yet effective method, termed Collaborative Feature-Logits Detector (CFL-Detector)
  arXiv  Detail & Related papers  (2024-11-20T02:57:35Z)
• Online Pseudo-Label Unified Object Detection for Multiple Datasets Training [0.0]
  We propose an Online Pseudo-Label Unified Object Detection scheme. Our method uses a periodically updated teacher model to generate pseudo-labels for the unlabelled objects in each sub-dataset. We also propose a category specific box regression and a pseudo-label RPN head to improve the recall rate of the Region Proposal Network (PRN)
  arXiv  Detail & Related papers  (2024-10-21T01:23:42Z)
• Continuous Contrastive Learning for Long-Tailed Semi-Supervised Recognition [50.61991746981703]
  Current state-of-the-art LTSSL approaches rely on high-quality pseudo-labels for large-scale unlabeled data. This paper introduces a novel probabilistic framework that unifies various recent proposals in long-tail learning. We introduce a continuous contrastive learning method, CCL, extending our framework to unlabeled data using reliable and smoothed pseudo-labels.
  arXiv  Detail & Related papers  (2024-10-08T15:06:10Z)
• Credible Teacher for Semi-Supervised Object Detection in Open Scene [106.25850299007674]
  In Open Scene Semi-Supervised Object Detection (O-SSOD), unlabeled data may contain unknown objects not observed in the labeled data. It is detrimental to the current methods that mainly rely on self-training, as more uncertainty leads to the lower localization and classification precision of pseudo labels. We propose Credible Teacher, an end-to-end framework to prevent uncertain pseudo labels from misleading the model.
  arXiv  Detail & Related papers  (2024-01-01T08:19:21Z)
• Semi-Supervised Object Detection with Uncurated Unlabeled Data for Remote Sensing Images [16.660668160785615]
  Semi-supervised object detection (SSOD) methods tackle this issue by generating pseudo-labels for the unlabeled data. However, real-world situations introduce the possibility of out-of-distribution (OOD) samples being mixed with in-distribution (ID) samples within the unlabeled dataset. We propose Open-Set Semi-Supervised Object Detection (OSSOD) on uncurated unlabeled data.
  arXiv  Detail & Related papers  (2023-10-09T07:59:31Z)
• Revisiting Class Imbalance for End-to-end Semi-Supervised Object Detection [1.6249267147413524]
  Semi-supervised object detection (SSOD) has made significant progress with the development of pseudo-label-based end-to-end methods. Many methods face challenges due to class imbalance, which hinders the effectiveness of the pseudo-label generator. In this paper, we examine the root causes of low-quality pseudo-labels and present novel learning mechanisms to improve the label generation quality.
  arXiv  Detail & Related papers  (2023-06-04T06:01:53Z)
• All Points Matter: Entropy-Regularized Distribution Alignment for Weakly-supervised 3D Segmentation [67.30502812804271]
  Pseudo-labels are widely employed in weakly supervised 3D segmentation tasks where only sparse ground-truth labels are available for learning. We propose a novel learning strategy to regularize the generated pseudo-labels and effectively narrow the gaps between pseudo-labels and model predictions.
  arXiv  Detail & Related papers  (2023-05-25T08:19:31Z)
• 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)
• Complementary Labels Learning with Augmented Classes [22.460256396941528]
  Complementary Labels Learning (CLL) arises in many real-world tasks such as private questions classification and online learning. We propose a novel problem setting called Complementary Labels Learning with Augmented Classes (CLLAC) By using unlabeled data, we propose an unbiased estimator of classification risk for CLLAC, which is guaranteed to be provably consistent.
  arXiv  Detail & Related papers  (2022-11-19T13:55:27Z)
• Unsupervised Domain Adaptive Salient Object Detection Through Uncertainty-Aware Pseudo-Label Learning [104.00026716576546]
  We propose to learn saliency from synthetic but clean labels, which naturally has higher pixel-labeling quality without the effort of manual annotations. We show that our proposed method outperforms the existing state-of-the-art deep unsupervised SOD methods on several benchmark datasets.
  arXiv  Detail & Related papers  (2022-02-26T16:03:55Z)

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.