3 citations · 3 across the 3 of their papers we have counts for
4 papers · 1 filter
When Depth Hurts: Reliability-Aware Geometry Distillation for Depth-Free RGB-D Salient Object Detection
Xuehao Wang, Jiaxin Hua, Runmei Li +4
Depth can resolve appearance ambiguity in RGB-D salient object detection (SOD), yet sensor depth is not uniformly reliable. Missing regions, blurred boundaries, and structural arti…
Data-Level Recombination and Lightweight Fusion Scheme for RGB-D Salient Object Detection
Xuehao Wang, Shuai Li, Chenglizhao Chen +3
Existing RGB-D salient object detection methods treat depth information as an independent component to complement its RGB part, and widely follow the bi-stream parallel network arc…
Recursive Multi-model Complementary Deep Fusion forRobust Salient Object Detection via Parallel Sub Networks
Zhenyu Wu, Shuai Li, Chenglizhao Chen +2
Fully convolutional networks have shown outstanding performance in the salient object detection (SOD) field. The state-of-the-art (SOTA) methods have a tendency to become deeper an…
Knowing Depth Quality In Advance: A Depth Quality Assessment Method For RGB-D Salient Object Detection
Xuehao Wang, Shuai Li, Chenglizhao Chen +2
Previous RGB-D salient object detection (SOD) methods have widely adopted deep learning tools to automatically strike a trade-off between RGB and D (depth), whose key rationale is…