Transformer-CNN Cohort: Semi-supervised Semantic Segmentation by the Best of Both Students

• URL: http://arxiv.org/abs/2209.02178v2
• Date: Sun, 17 Dec 2023 01:31:57 GMT
• Title: Transformer-CNN Cohort: Semi-supervised Semantic Segmentation by the Best of Both Students
• Authors: Xu Zheng, Yunhao Luo, Chong Fu, Kangcheng Liu and Lin Wang
• Abstract summary: We propose a novel Semi-supervised Learning (SSL) approach that consists of two students with one based on the vision transformer (ViT) and the other based on the convolutional neural network (CNN) Our method subtly incorporates the multi-level consistency regularization on the predictions and the heterogeneous feature spaces via pseudo-labeling for the unlabeled data. We validate the TCC framework on Cityscapes and Pascal VOC 2012 datasets, which outperforms existing SSL methods by a large margin.
• Score: 18.860732413631887
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The popular methods for semi-supervised semantic segmentation mostly adopt a unitary network model using convolutional neural networks (CNNs) and enforce consistency of the model's predictions over perturbations applied to the inputs or model. However, such a learning paradigm suffers from two critical limitations: a) learning the discriminative features for the unlabeled data; b) learning both global and local information from the whole image. In this paper, we propose a novel Semi-supervised Learning (SSL) approach, called Transformer-CNN Cohort (TCC), that consists of two students with one based on the vision transformer (ViT) and the other based on the CNN. Our method subtly incorporates the multi-level consistency regularization on the predictions and the heterogeneous feature spaces via pseudo-labeling for the unlabeled data. First, as the inputs of the ViT student are image patches, the feature maps extracted encode crucial class-wise statistics. To this end, we propose class-aware feature consistency distillation (CFCD) that first leverages the outputs of each student as the pseudo labels and generates class-aware feature (CF) maps for knowledge transfer between the two students. Second, as the ViT student has more uniform representations for all layers, we propose consistency-aware cross distillation (CCD) to transfer knowledge between the pixel-wise predictions from the cohort. We validate the TCC framework on Cityscapes and Pascal VOC 2012 datasets, which outperforms existing SSL methods by a large margin.

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