Breaking the SSL-AL Barrier: A Synergistic Semi-Supervised Active Learning Framework for 3D Object Detection

• URL: http://arxiv.org/abs/2501.15449v1
• Date: Sun, 26 Jan 2025 08:43:59 GMT
• Title: Breaking the SSL-AL Barrier: A Synergistic Semi-Supervised Active Learning Framework for 3D Object Detection
• Authors: Zengran Wang, Yanan Zhang, Jiaxin Chen, Di Huang,
• Abstract summary: We propose a Synergistic Semi-Supervised Active Learning framework, dubbed as S-SSAL. We show that S-SSAL can achieve performance comparable to models trained on the full dataset.
• Score: 34.049483237480615
• License:
• Abstract: To address the annotation burden in LiDAR-based 3D object detection, active learning (AL) methods offer a promising solution. However, traditional active learning approaches solely rely on a small amount of labeled data to train an initial model for data selection, overlooking the potential of leveraging the abundance of unlabeled data. Recently, attempts to integrate semi-supervised learning (SSL) into AL with the goal of leveraging unlabeled data have faced challenges in effectively resolving the conflict between the two paradigms, resulting in less satisfactory performance. To tackle this conflict, we propose a Synergistic Semi-Supervised Active Learning framework, dubbed as S-SSAL. Specifically, from the perspective of SSL, we propose a Collaborative PseudoScene Pre-training (CPSP) method that effectively learns from unlabeled data without introducing adverse effects. From the perspective of AL, we design a Collaborative Active Learning (CAL) method, which complements the uncertainty and diversity methods by model cascading. This allows us to fully exploit the potential of the CPSP pre-trained model. Extensive experiments conducted on KITTI and Waymo demonstrate the effectiveness of our S-SSAL framework. Notably, on the KITTI dataset, utilizing only 2% labeled data, S-SSAL can achieve performance comparable to models trained on the full dataset.

Related papers

• DyConfidMatch: Dynamic Thresholding and Re-sampling for 3D Semi-supervised Learning [4.259908158892314]
  Semi-supervised learning (SSL) leverages limited labeled and abundant unlabeled data but often faces challenges with data imbalance. This study investigates class-level confidence as an indicator of learning status in 3D SSL, proposing a novel method that utilizes dynamic thresholding. A re-sampling strategy is also introduced to mitigate bias towards well-represented classes, ensuring equitable class representation.
  arXiv  Detail & Related papers  (2024-11-13T05:09:28Z)
• Learning Objective-Specific Active Learning Strategies with Attentive Neural Processes [72.75421975804132]
  Learning Active Learning (LAL) suggests to learn the active learning strategy itself, allowing it to adapt to the given setting. We propose a novel LAL method for classification that exploits symmetry and independence properties of the active learning problem. Our approach is based on learning from a myopic oracle, which gives our model the ability to adapt to non-standard objectives.
  arXiv  Detail & Related papers  (2023-09-11T14:16:37Z)
• How To Overcome Confirmation Bias in Semi-Supervised Image Classification By Active Learning [2.1805442504863506]
  We present three data challenges common in real-world applications: between-class imbalance, within-class imbalance, and between-class similarity. We find that random sampling does not mitigate confirmation bias and, in some cases, leads to worse performance than supervised learning. Our results provide insights into the potential of combining active and semi-supervised learning in the presence of common real-world challenges.
  arXiv  Detail & Related papers  (2023-08-16T08:52:49Z)
• Active Semi-Supervised Learning by Exploring Per-Sample Uncertainty and Consistency [30.94964727745347]
  We propose a method called Active Semi-supervised Learning (ASSL) to improve accuracy of models at a lower cost. ASSL involves more dynamic model updates than Active Learning (AL) due to the use of unlabeled data. ASSL achieved about 5.3 times higher computational efficiency than Semi-supervised Learning (SSL) while achieving the same performance.
  arXiv  Detail & Related papers  (2023-03-15T22:58:23Z)
• Decoupled Adversarial Contrastive Learning for Self-supervised Adversarial Robustness [69.39073806630583]
  Adversarial training (AT) for robust representation learning and self-supervised learning (SSL) for unsupervised representation learning are two active research fields. We propose a two-stage framework termed Decoupled Adversarial Contrastive Learning (DeACL)
  arXiv  Detail & Related papers  (2022-07-22T06:30:44Z)
• Collaborative Intelligence Orchestration: Inconsistency-Based Fusion of Semi-Supervised Learning and Active Learning [60.26659373318915]
  Active learning (AL) and semi-supervised learning (SSL) are two effective, but often isolated, means to alleviate the data-hungry problem. We propose an innovative Inconsistency-based virtual aDvErial algorithm to further investigate SSL-AL's potential superiority. Two real-world case studies visualize the practical industrial value of applying and deploying the proposed data sampling algorithm.
  arXiv  Detail & Related papers  (2022-06-07T13:28:43Z)
• Open-Set Semi-Supervised Learning for 3D Point Cloud Understanding [62.17020485045456]
  It is commonly assumed in semi-supervised learning (SSL) that the unlabeled data are drawn from the same distribution as that of the labeled ones. We propose to selectively utilize unlabeled data through sample weighting, so that only conducive unlabeled data would be prioritized.
  arXiv  Detail & Related papers  (2022-05-02T16:09:17Z)
• Trash to Treasure: Harvesting OOD Data with Cross-Modal Matching for Open-Set Semi-Supervised Learning [101.28281124670647]
  Open-set semi-supervised learning (open-set SSL) investigates a challenging but practical scenario where out-of-distribution (OOD) samples are contained in the unlabeled data. We propose a novel training mechanism that could effectively exploit the presence of OOD data for enhanced feature learning. Our approach substantially lifts the performance on open-set SSL and outperforms the state-of-the-art by a large margin.
  arXiv  Detail & Related papers  (2021-08-12T09:14:44Z)
• Adversarial Self-Supervised Learning for Semi-Supervised 3D Action Recognition [123.62183172631443]
  We present Adversarial Self-Supervised Learning (ASSL), a novel framework that tightly couples SSL and the semi-supervised scheme. Specifically, we design an effective SSL scheme to improve the discrimination capability of learned representations for 3D action recognition.
  arXiv  Detail & Related papers  (2020-07-12T08:01:06Z)

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.