Efficient Semantic Matching with Hypercolumn Correlation

• URL: http://arxiv.org/abs/2311.04336v1
• Date: Tue, 7 Nov 2023 20:40:07 GMT
• Title: Efficient Semantic Matching with Hypercolumn Correlation
• Authors: Seungwook Kim, Juhong Min, Minsu Cho
• Abstract summary: HCCNet is an efficient yet effective semantic matching method. It exploits the full potential of multi-scale correlation maps. It eschews the reliance on expensive match-wise relationship mining on the 4D correlation map.
• Score: 58.92933923647451
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent studies show that leveraging the match-wise relationships within the 4D correlation map yields significant improvements in establishing semantic correspondences - but at the cost of increased computation and latency. In this work, we focus on the aspect that the performance improvements of recent methods can also largely be attributed to the usage of multi-scale correlation maps, which hold various information ranging from low-level geometric cues to high-level semantic contexts. To this end, we propose HCCNet, an efficient yet effective semantic matching method which exploits the full potential of multi-scale correlation maps, while eschewing the reliance on expensive match-wise relationship mining on the 4D correlation map. Specifically, HCCNet performs feature slicing on the bottleneck features to yield a richer set of intermediate features, which are used to construct a hypercolumn correlation. HCCNet can consequently establish semantic correspondences in an effective manner by reducing the volume of conventional high-dimensional convolution or self-attention operations to efficient point-wise convolutions. HCCNet demonstrates state-of-the-art or competitive performances on the standard benchmarks of semantic matching, while incurring a notably lower latency and computation overhead compared to the existing SoTA methods.

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