DBAT: Dynamic Backward Attention Transformer for Material Segmentation with Cross-Resolution Patches

• URL: http://arxiv.org/abs/2305.03919v2
• Date: Wed, 28 Feb 2024 10:22:03 GMT
• Title: DBAT: Dynamic Backward Attention Transformer for Material Segmentation with Cross-Resolution Patches
• Authors: Yuwen Heng, Srinandan Dasmahapatra, Hansung Kim
• Abstract summary: We propose the Dynamic Backward Attention Transformer (DBAT) to aggregate cross-resolution features. Experiments show that our DBAT achieves an accuracy of 86.85%, which is the best performance among state-of-the-art real-time models. We further align features to semantic labels, performing network dissection, to infer that the proposed model can extract material-related features better than other methods.
• Score: 8.812837829361923
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The objective of dense material segmentation is to identify the material categories for every image pixel. Recent studies adopt image patches to extract material features. Although the trained networks can improve the segmentation performance, their methods choose a fixed patch resolution which fails to take into account the variation in pixel area covered by each material. In this paper, we propose the Dynamic Backward Attention Transformer (DBAT) to aggregate cross-resolution features. The DBAT takes cropped image patches as input and gradually increases the patch resolution by merging adjacent patches at each transformer stage, instead of fixing the patch resolution during training. We explicitly gather the intermediate features extracted from cross-resolution patches and merge them dynamically with predicted attention masks. Experiments show that our DBAT achieves an accuracy of 86.85%, which is the best performance among state-of-the-art real-time models. Like other successful deep learning solutions with complex architectures, the DBAT also suffers from lack of interpretability. To address this problem, this paper examines the properties that the DBAT makes use of. By analysing the cross-resolution features and the attention weights, this paper interprets how the DBAT learns from image patches. We further align features to semantic labels, performing network dissection, to infer that the proposed model can extract material-related features better than other methods. We show that the DBAT model is more robust to network initialisation, and yields fewer variable predictions compared to other models. The project code is available at https://github.com/heng-yuwen/Dynamic-Backward-Attention-Transformer.

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