Attention-Based Multimodal Image Matching
This addresses the problem of robust image matching across different modalities for computer vision applications, representing an incremental advancement with a novel architectural integration.
The paper tackles multimodal image patch matching by proposing an attention-based approach using a Transformer encoder with a multiscale Siamese CNN, achieving new state-of-the-art accuracy on both multimodal and single modality benchmarks.
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.