  Semantic segmentation is important for scene understanding. To address the
scenes of adverse illumination conditions of natural images, thermal infrared
(TIR) images are introduced. Most existing RGB-T semantic segmentation methods
follow three cross-modal fusion paradigms, i.e. encoder fusion, decoder fusion,
and feature fusion. Some methods, unfortunately, ignore the properties of RGB
and TIR features or the properties of features at different levels. In this
paper, we propose a novel feature fusion-based network for RGB-T semantic
segmentation, named \emph{LASNet}, which follows three steps of location,
activation, and sharpening. The highlight of LASNet is that we fully consider
the characteristics of cross-modal features at different levels, and
accordingly propose three specific modules for better segmentation. Concretely,
we propose a Collaborative Location Module (CLM) for high-level semantic
features, aiming to locate all potential objects. We propose a Complementary
Activation Module for middle-level features, aiming to activate exact regions
of different objects. We propose an Edge Sharpening Module (ESM) for low-level
texture features, aiming to sharpen the edges of objects. Furthermore, in the
training phase, we attach a location supervision and an edge supervision after
CLM and ESM, respectively, and impose two semantic supervisions in the decoder
part to facilitate network convergence. Experimental results on two public
datasets demonstrate that the superiority of our LASNet over relevant
state-of-the-art methods. The code and results of our method are available at
https://github.com/MathLee/LASNet.
