Cross-Modality Fusion Transformer for Multispectral Object Detection

• URL: http://arxiv.org/abs/2111.00273v1
• Date: Sat, 30 Oct 2021 15:34:12 GMT
• Title: Cross-Modality Fusion Transformer for Multispectral Object Detection
• Authors: Fang Qingyun, Han Dapeng and Wang Zhaokui
• Abstract summary: Multispectral image pairs can provide the combined information, making object detection applications more reliable and robust. We present a simple yet effective cross-modality feature fusion approach, named Cross-Modality Fusion Transformer (CFT) in this paper.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multispectral image pairs can provide the combined information, making object detection applications more reliable and robust in the open world. To fully exploit the different modalities, we present a simple yet effective cross-modality feature fusion approach, named Cross-Modality Fusion Transformer (CFT) in this paper. Unlike prior CNNs-based works, guided by the transformer scheme, our network learns long-range dependencies and integrates global contextual information in the feature extraction stage. More importantly, by leveraging the self attention of the transformer, the network can naturally carry out simultaneous intra-modality and inter-modality fusion, and robustly capture the latent interactions between RGB and Thermal domains, thereby significantly improving the performance of multispectral object detection. Extensive experiments and ablation studies on multiple datasets demonstrate that our approach is effective and achieves state-of-the-art detection performance. Our code and models will be released soon at https://github.com/DocF/multispectral-object-detection.

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