Exploiting Minority Pseudo-Labels for Semi-Supervised Semantic Segmentation in Autonomous Driving

• URL: http://arxiv.org/abs/2409.12680v2
• Date: Sun, 22 Sep 2024 13:58:19 GMT
• Title: Exploiting Minority Pseudo-Labels for Semi-Supervised Semantic Segmentation in Autonomous Driving
• Authors: Yuting Hong, Hui Xiao, Huazheng Hao, Xiaojie Qiu, Baochen Yao, Chengbin Peng,
• Abstract summary: We propose a professional training module to enhance minority class learning and a general training module to learn more comprehensive semantic information. In experiments, our framework demonstrates superior performance compared to state-of-the-art methods on benchmark datasets.
• Score: 2.638145329894673
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the advancement of autonomous driving, semantic segmentation has achieved remarkable progress. The training of such networks heavily relies on image annotations, which are very expensive to obtain. Semi-supervised learning can utilize both labeled data and unlabeled data with the help of pseudo-labels. However, in many real-world scenarios where classes are imbalanced, majority classes often play a dominant role during training and the learning quality of minority classes can be undermined. To overcome this limitation, we propose a synergistic training framework, including a professional training module to enhance minority class learning and a general training module to learn more comprehensive semantic information. Based on a pixel selection strategy, they can iteratively learn from each other to reduce error accumulation and coupling. In addition, a dual contrastive learning with anchors is proposed to guarantee more distinct decision boundaries. In experiments, our framework demonstrates superior performance compared to state-of-the-art methods on benchmark datasets.

Related papers

• A Classifier-Free Incremental Learning Framework for Scalable Medical Image Segmentation [6.591403935303867]
  We introduce a novel segmentation paradigm enabling the segmentation of a variable number of classes within a single classifier-free network. This network is trained using contrastive learning and produces discriminative feature representations that facilitate straightforward interpretation. We demonstrate the flexibility of our method in handling varying class numbers within a unified network and its capacity for incremental learning.
  arXiv  Detail & Related papers  (2024-05-25T19:05:07Z)
• DiverseNet: Decision Diversified Semi-supervised Semantic Segmentation Networks for Remote Sensing Imagery [17.690698736544626]
  We propose DiverseNet which explores multi-head and multi-model semi-supervised learning algorithms by simultaneously enhancing precision and diversity during training. The two proposed methods in the DiverseNet family, namely DiverseHead and DiverseModel, both achieve the better semantic segmentation performance in four widely utilised remote sensing imagery data sets.
  arXiv  Detail & Related papers  (2023-11-22T22:20:10Z)
• Voxel-wise Adversarial Semi-supervised Learning for Medical Image Segmentation [4.489713477369384]
  We introduce a novel adversarial learning-based semi-supervised segmentation method for medical image segmentation. Our method embeds both local and global features from multiple hidden layers and learns context relations between multiple classes. Our method outperforms current best-performing state-of-the-art semi-supervised learning approaches on the image segmentation of the left atrium (single class) and multiorgan datasets (multiclass)
  arXiv  Detail & Related papers  (2022-05-14T06:57:19Z)
• A Pixel-Level Meta-Learner for Weakly Supervised Few-Shot Semantic Segmentation [40.27705176115985]
  Few-shot semantic segmentation addresses the learning task in which only few images with ground truth pixel-level labels are available for the novel classes of interest. We propose a novel meta-learning framework, which predicts pseudo pixel-level segmentation masks from a limited amount of data and their semantic labels. Our proposed learning model can be viewed as a pixel-level meta-learner.
  arXiv  Detail & Related papers  (2021-11-02T08:28:11Z)
• Learning Debiased and Disentangled Representations for Semantic Segmentation [52.35766945827972]
  We propose a model-agnostic and training scheme for semantic segmentation. By randomly eliminating certain class information in each training iteration, we effectively reduce feature dependencies among classes. Models trained with our approach demonstrate strong results on multiple semantic segmentation benchmarks.
  arXiv  Detail & Related papers  (2021-10-31T16:15:09Z)
• Dense Contrastive Visual-Linguistic Pretraining [53.61233531733243]
  Several multimodal representation learning approaches have been proposed that jointly represent image and text. These approaches achieve superior performance by capturing high-level semantic information from large-scale multimodal pretraining. We propose unbiased Dense Contrastive Visual-Linguistic Pretraining to replace the region regression and classification with cross-modality region contrastive learning.
  arXiv  Detail & Related papers  (2021-09-24T07:20:13Z)
• Contrastive Learning for Fair Representations [50.95604482330149]
  Trained classification models can unintentionally lead to biased representations and predictions. Existing debiasing methods for classification models, such as adversarial training, are often expensive to train and difficult to optimise. We propose a method for mitigating bias by incorporating contrastive learning, in which instances sharing the same class label are encouraged to have similar representations.
  arXiv  Detail & Related papers  (2021-09-22T10:47:51Z)
• Semi-weakly Supervised Contrastive Representation Learning for Retinal Fundus Images [0.2538209532048867]
  We propose a semi-weakly supervised contrastive learning framework for representation learning using semi-weakly annotated images. We empirically validate the transfer learning performance of SWCL on seven public retinal fundus datasets.
  arXiv  Detail & Related papers  (2021-08-04T15:50:09Z)
• Multi-Label Image Classification with Contrastive Learning [57.47567461616912]
  We show that a direct application of contrastive learning can hardly improve in multi-label cases. We propose a novel framework for multi-label classification with contrastive learning in a fully supervised setting.
  arXiv  Detail & Related papers  (2021-07-24T15:00:47Z)
• Learning Diverse Representations for Fast Adaptation to Distribution Shift [78.83747601814669]
  We present a method for learning multiple models, incorporating an objective that pressures each to learn a distinct way to solve the task. We demonstrate our framework's ability to facilitate rapid adaptation to distribution shift.
  arXiv  Detail & Related papers  (2020-06-12T12:23:50Z)
• Improving Semantic Segmentation via Self-Training [75.07114899941095]
  We show that we can obtain state-of-the-art results using a semi-supervised approach, specifically a self-training paradigm. We first train a teacher model on labeled data, and then generate pseudo labels on a large set of unlabeled data. Our robust training framework can digest human-annotated and pseudo labels jointly and achieve top performances on Cityscapes, CamVid and KITTI datasets.
  arXiv  Detail & Related papers  (2020-04-30T17:09:17Z)

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.