Region-adaptive and context-complementary cross modulation for RGB-T semantic segmentation

Abstract

RGB-Thermal (RGB-T) semantic segmentation is an emerging task aiming to improve the robustness of segmentation methods under extreme imaging conditions with the aid of thermal infrared modality. Foreground–background distinguishment and complementary information mining are two key challenges of this task. Recent methods use naive channel attention and cross-attention to tackle these challenges, but they still struggle with a sub-optimal solution where salient foreground features and noisy background ones might be equally modulated without distinction. The quadratic computational overhead of cross-attention also blocks its application on high-resolution features. Moreover, lacking complementary information mining in the encoding phase hinders the comprehensive scene encoding as well. To alleviate these limitations, we propose a cross modulation process with two collaborative components. The first Region-Adaptive Channel Modulation (RACM) module conducts channel attention at a fine-grained region level where foreground and background regions can be modulated differently in each channel. The second Context-Complementary Spatial Modulation (CCSM) module mines and transfers complementary information between the two modalities early in the encoding phase. Experiments show that our method achieves state-of-the-art performances on current RGB-T segmentation benchmarks.