Feature Reintegration over Differential Treatment: A Top-down and Adaptive Fusion Network for RGB-D Salient Object Detection
Miao Zhang, Yu Zhang, Yongri Piao, Beiqi Hu, Huchuan Lu
Abstract
Most methods for RGB-D salient object detection (SOD) utilize the same fusion strategy to explore the cross-modal complementary information at each level. However, this may ignore different feature contributions from two modalities on different levels towards prediction. In this paper, we propose a novel top-down multi-level fusion structure where different fusion strategies are utilized to effectively explore the low-level and high-level features. This is achieved by designing the interweave fusion module (IFM) to effectively integrate the global information and designing the gated select fusion module (GSFM) to discriminatively select useful local information by filtering out the unnecessary one from RGB and depth data. Moreover, we propose an adaptive fusion module (AFM) to reintegrate the fused cross-modal features of each level to predict a more accurate result. Comprehensive experiments on 7 challenging benchmark datasets demonstrate that our method achieves the competitive performance over 14 state-of-the-art RGB-D alternative methods.