APNet: Adversarial Learning Assistance and Perceived Importance Fusion Network for All-Day RGB-T Salient Object Detection
Wujie Zhou, Yun Zhu, Jingsheng Lei, Jian Wan, Lu Yu
Abstract
To improve the performance of salient object detection (SOD) in scenes with low-light conditions (e.g., nighttime) and cluttered backgrounds, infrared thermal images are used to supplement RGB images to achieve good all-day imaging as infrared images are insensitive to light source changes. Therefore, we built an adversarial learning assistance and perceived importance fusion network (APNet) for all-day RGB-thermal (RGB-T) SOD. First, an iterative adversarial learning approach was used to establish a generator and three discriminators. The generator provides salient maps that are eventually accepted by the discriminators and are used to determine their similarities with the labels. Second, a progressively guided optimization structure with high-level features is used to refine low-level features across multiple scales gradually. To further improve the detection results, a perceived importance fusion module (PIFM) is used to weigh and fuse different modalities in cases where the presence of noise may degrade sensor fusion. Extensive experiments on existing RGB-T datasets demonstrate that the proposed APNet notably outperforms state-of-the-art models.