  Recently, RGB-Thermal based perception has shown significant advances.
Thermal information provides useful clues when visual cameras suffer from poor
lighting conditions, such as low light and fog. However, how to effectively
fuse RGB images and thermal data remains an open challenge. Previous works
involve naive fusion strategies such as merging them at the input,
concatenating multi-modality features inside models, or applying attention to
each data modality. These fusion strategies are straightforward yet
insufficient. In this paper, we propose a novel fusion method named Explicit
Attention-Enhanced Fusion (EAEF) that fully takes advantage of each type of
data. Specifically, we consider the following cases: i) both RGB data and
thermal data, ii) only one of the types of data, and iii) none of them generate
discriminative features. EAEF uses one branch to enhance feature extraction for
i) and iii) and the other branch to remedy insufficient representations for
ii). The outputs of two branches are fused to form complementary features. As a
result, the proposed fusion method outperforms state-of-the-art by 1.6\% in
mIoU 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 is
available at https://github.com/FreeformRobotics/EAEFNet.
