Computer Science > Computer Vision and Pattern Recognition
[Submitted on 31 Jul 2021 (v1), last revised 27 Aug 2021 (this version, v2)]
Title:Multi-scale Matching Networks for Semantic Correspondence
View PDFAbstract:Deep features have been proven powerful in building accurate dense semantic correspondences in various previous works. However, the multi-scale and pyramidal hierarchy of convolutional neural networks has not been well studied to learn discriminative pixel-level features for semantic correspondence. In this paper, we propose a multi-scale matching network that is sensitive to tiny semantic differences between neighboring pixels. We follow the coarse-to-fine matching strategy and build a top-down feature and matching enhancement scheme that is coupled with the multi-scale hierarchy of deep convolutional neural networks. During feature enhancement, intra-scale enhancement fuses same-resolution feature maps from multiple layers together via local self-attention and cross-scale enhancement hallucinates higher-resolution feature maps along the top-down hierarchy. Besides, we learn complementary matching details at different scales thus the overall matching score is refined by features of different semantic levels gradually. Our multi-scale matching network can be trained end-to-end easily with few additional learnable parameters. Experimental results demonstrate that the proposed method achieves state-of-the-art performance on three popular benchmarks with high computational efficiency.
Submission history
From: Dongyang Zhao [view email][v1] Sat, 31 Jul 2021 10:57:24 UTC (31,057 KB)
[v2] Fri, 27 Aug 2021 10:05:19 UTC (31,061 KB)
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