A Good Student is Cooperative and Reliable: CNN-Transformer Collaborative Learning for Semantic Segmentation
This work addresses the problem of improving semantic segmentation accuracy for computer vision applications by enabling effective collaboration between CNN and ViT models, though it is incremental as it builds on existing distillation methods.
The paper tackles collaborative learning between CNN-based and ViT-based models for semantic segmentation by proposing an online knowledge distillation framework with heterogeneous feature distillation and bidirectional selective distillation, achieving state-of-the-art performance on three benchmark datasets with significant margins.
In this paper, we strive to answer the question "how to collaboratively learn convolutional neural network (CNN)-based and vision transformer (ViT)-based models by selecting and exchanging the reliable knowledge between them for semantic segmentation?" Accordingly, we propose an online knowledge distillation (KD) framework that can simultaneously learn compact yet effective CNN-based and ViT-based models with two key technical breakthroughs to take full advantage of CNNs and ViT while compensating their limitations. Firstly, we propose heterogeneous feature distillation (HFD) to improve students' consistency in low-layer feature space by mimicking heterogeneous features between CNNs and ViT. Secondly, to facilitate the two students to learn reliable knowledge from each other, we propose bidirectional selective distillation (BSD) that can dynamically transfer selective knowledge. This is achieved by 1) region-wise BSD determining the directions of knowledge transferred between the corresponding regions in the feature space and 2) pixel-wise BSD discerning which of the prediction knowledge to be transferred in the logit space. Extensive experiments on three benchmark datasets demonstrate that our proposed framework outperforms the state-of-the-art online distillation methods by a large margin, and shows its efficacy in learning collaboratively between ViT-based and CNN-based models.