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Recently, RGB-Thermal based perception has shown significant advances.Thermal information provides useful clues when visual cameras suffer from poorlighting conditions, such as low light and fog. However, how to effectivelyfuse RGB images and thermal data remains an open challenge. Previous worksinvolve naive fusion strategies such as merging them at the input,concatenating multi-modality features inside models, or applying attention toeach data modality. These fusion strategies are straightforward yetinsufficient. In this paper, we propose a novel fusion method named ExplicitAttention-Enhanced Fusion (EAEF) that fully takes advantage of each type ofdata. Specifically, we consider the following cases: i) both RGB data andthermal data, ii) only one of the types of data, and iii) none of them generatediscriminative features. EAEF uses one branch to enhance feature extraction fori) and iii) and the other branch to remedy insufficient representations forii). The outputs of two branches are fused to form complementary features. As aresult, the proposed fusion method outperforms state-of-the-art by 1.6\% inmIoU on semantic segmentation, 3.1\% in MAE on salient object detection, 2.3\%in mAP on object detection, and 8.1\% in MAE on crowd counting. The code isavailable at https://github.com/FreeformRobotics/EAEFNet.

Explicit Attention-Enhanced Fusion for RGB-Thermal Perception Tasks