Towards Modality-agnostic Label-efficient Segmentation with Entropy-Regularized Distribution Alignment

• URL: http://arxiv.org/abs/2408.16520v1
• Date: Thu, 29 Aug 2024 13:31:15 GMT
• Title: Towards Modality-agnostic Label-efficient Segmentation with Entropy-Regularized Distribution Alignment
• Authors: Liyao Tang, Zhe Chen, Shanshan Zhao, Chaoyue Wang, Dacheng Tao,
• Abstract summary: This topic is widely studied in 3D point cloud segmentation due to the difficulty of annotating point clouds densely. Until recently, pseudo-labels have been widely employed to facilitate training with limited ground-truth labels. Existing pseudo-labeling approaches could suffer heavily from the noises and variations in unlabelled data. We propose a novel learning strategy to regularize the pseudo-labels generated for training, thus effectively narrowing the gaps between pseudo-labels and model predictions.
• Score: 62.73503467108322
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Label-efficient segmentation aims to perform effective segmentation on input data using only sparse and limited ground-truth labels for training. This topic is widely studied in 3D point cloud segmentation due to the difficulty of annotating point clouds densely, while it is also essential for cost-effective segmentation on 2D images. Until recently, pseudo-labels have been widely employed to facilitate training with limited ground-truth labels, and promising progress has been witnessed in both the 2D and 3D segmentation. However, existing pseudo-labeling approaches could suffer heavily from the noises and variations in unlabelled data, which would result in significant discrepancies between generated pseudo-labels and current model predictions during training. We analyze that this can further confuse and affect the model learning process, which shows to be a shared problem in label-efficient learning across both 2D and 3D modalities. To address this issue, we propose a novel learning strategy to regularize the pseudo-labels generated for training, thus effectively narrowing the gaps between pseudo-labels and model predictions. More specifically, our method introduces an Entropy Regularization loss and a Distribution Alignment loss for label-efficient learning, resulting in an ERDA learning strategy. Interestingly, by using KL distance to formulate the distribution alignment loss, ERDA reduces to a deceptively simple cross-entropy-based loss which optimizes both the pseudo-label generation module and the segmentation model simultaneously. In addition, we innovate in the pseudo-label generation to make our ERDA consistently effective across both 2D and 3D data modalities for segmentation. Enjoying simplicity and more modality-agnostic pseudo-label generation, our method has shown outstanding performance in fully utilizing all unlabeled data points for training across ...

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