I3Net: Intensive information interaction network for RGB-T salient object detection

Abstract

Multi-modality salient object detection (SOD) is receiving more and more attention in recent years. Infrared thermal images can provide useful information in extreme situations, such as low illumination and cluttered background. Accompany with extra information, we need a more delicate design to properly integrate multi-modal and multi-scale clues. In this paper, we propose an intensively information interaction network (I³Net) to perform Red-Green-Blue and Thermal (RGB-T) SOD, which optimizes the performance through modality interaction, level interaction, and scale interaction. Firstly, feature channels from different sources are dynamically selected according to the modality interaction with dynamic merging module. Then, adjacent level interaction is conducted under the guidance of coordinate channel and spatial attention with spatial feature aggregation module. Finally, we deploy pyramid attention module to obtain a more comprehensive scale interaction. Extensive experiments on four RGB-T datasets, VT821, VT1000, VT5000 and VI-RGBT3500, show that the proposed I³Net achieves a competitive and excellent performance against 13 state-of-the-art methods in multiple evaluation metrics, with a 1.70%, 1.41%, and 1.54% improvement in terms of weighted F-measure, mean E-measure, and S-measure.