most citedCIR-Net: Cross-modality Interaction and Refinement for RGB-D Salient Object Detection

220 citations · 230 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV20221 cited

Exploiting Completeness and Uncertainty of Pseudo Labels for Weakly Supervised Video Anomaly Detection

Chen Zhang, Guorong Li, Yuankai Qi +4

Weakly supervised video anomaly detection aims to identify abnormal events in videos using only video-level labels. Recently, two-stage self-training methods have achieved signific…

cs.CV20224 cited

Does Thermal Really Always Matter for RGB-T Salient Object Detection?

Runmin Cong, Kepu Zhang, Chen Zhang +4

In recent years, RGB-T salient object detection (SOD) has attracted continuous attention, which makes it possible to identify salient objects in environments such as low light by i…

cs.CV2022220 cited

CIR-Net: Cross-modality Interaction and Refinement for RGB-D Salient Object Detection

Runmin Cong, Qinwei Lin, Chen Zhang +4

Focusing on the issue of how to effectively capture and utilize cross-modality information in RGB-D salient object detection (SOD) task, we present a convolutional neural network (…

cs.CV2022

A Weakly Supervised Learning Framework for Salient Object Detection via Hybrid Labels

Runmin Cong, Qi Qin, Chen Zhang +4

Fully-supervised salient object detection (SOD) methods have made great progress, but such methods often rely on a large number of pixel-level annotations, which are time-consuming…

cs.CV20215 cited

Cross-modality Discrepant Interaction Network for RGB-D Salient Object Detection

Chen Zhang, Runmin Cong, Qinwei Lin +4

The popularity and promotion of depth maps have brought new vigor and vitality into salient object detection (SOD), and a mass of RGB-D SOD algorithms have been proposed, mainly co…