Attention-Based Multimodal Image Matching

TL;DR

This paper introduces an attention-based Transformer approach for multimodal image patch matching that effectively captures appearance-invariant features, achieving state-of-the-art accuracy across multiple benchmarks.

Contribution

It is the first to successfully apply Transformer encoders to multimodal image patch matching, enhancing feature aggregation and invariance.

Findings

Achieves new state-of-the-art accuracy on multimodal benchmarks

Demonstrates effective multiscale feature aggregation

Validates general applicability across modalities

Abstract

We propose an attention-based approach for multimodal image patch matching using a Transformer encoder attending to the feature maps of a multiscale Siamese CNN. Our encoder is shown to efficiently aggregate multiscale image embeddings while emphasizing task-specific appearance-invariant image cues. We also introduce an attention-residual architecture, using a residual connection bypassing the encoder. This additional learning signal facilitates end-to-end training from scratch. Our approach is experimentally shown to achieve new state-of-the-art accuracy on both multimodal and single modality benchmarks, illustrating its general applicability. To the best of our knowledge, this is the first successful implementation of the Transformer encoder architecture to the multimodal image patch matching task.