Correspondence Networks with Adaptive Neighbourhood Consensus

• URL: http://arxiv.org/abs/2003.12059v1
• Date: Thu, 26 Mar 2020 17:58:09 GMT
• Title: Correspondence Networks with Adaptive Neighbourhood Consensus
• Authors: Shuda Li, Kai Han, Theo W. Costain, Henry Howard-Jenkins, and Victor Prisacariu
• Abstract summary: We propose a convolutional neural network architecture, called adaptive neighbourhood consensus network (ANC-Net) ANC-Net can be trained end-to-end with sparse key-point annotations to handle this challenge. We thoroughly evaluate the effectiveness of our method on various benchmarks, where it substantially outperforms state-of-the-art methods.
• Score: 22.013820169455812
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we tackle the task of establishing dense visual correspondences between images containing objects of the same category. This is a challenging task due to large intra-class variations and a lack of dense pixel level annotations. We propose a convolutional neural network architecture, called adaptive neighbourhood consensus network (ANC-Net), that can be trained end-to-end with sparse key-point annotations, to handle this challenge. At the core of ANC-Net is our proposed non-isotropic 4D convolution kernel, which forms the building block for the adaptive neighbourhood consensus module for robust matching. We also introduce a simple and efficient multi-scale self-similarity module in ANC-Net to make the learned feature robust to intra-class variations. Furthermore, we propose a novel orthogonal loss that can enforce the one-to-one matching constraint. We thoroughly evaluate the effectiveness of our method on various benchmarks, where it substantially outperforms state-of-the-art methods.

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