SOTAVerified

Video Salient Object Detection

Video salient object detection (VSOD) is significantly essential for understanding the underlying mechanism behind HVS during free-viewing in general and instrumental to a wide range of real-world applications, e.g., video segmentation, video captioning, video compression, autonomous driving, robotic interaction, weakly supervised attention. Besides its academic value and practical significance, VSOD presents great difficulties due to the challenges carried by video data (diverse motion patterns, occlusions, blur, large object deformations, etc.) and the inherent complexity of human visual attention behavior (i.e., selective attention allocation, attention shift) during dynamic scenes. Online benchmark: http://dpfan.net/davsod.

( Image credit: Shifting More Attention to Video Salient Object Detection, CVPR2019-Best Paper Finalist )

Papers

Showing 110 of 48 papers

TitleStatusHype
A Unified Transformer Framework for Group-based Segmentation: Co-Segmentation, Co-Saliency Detection and Video Salient Object DetectionCode2
Learning Motion and Temporal Cues for Unsupervised Video Object SegmentationCode1
Depth Quality-Inspired Feature Manipulation for Efficient RGB-D and Video Salient Object DetectionCode1
Hierarchical Feature Alignment Network for Unsupervised Video Object SegmentationCode1
Learning Video Salient Object Detection Progressively from Unlabeled VideosCode1
Depth-Cooperated Trimodal Network for Video Salient Object DetectionCode1
Full-Duplex Strategy for Video Object SegmentationCode1
Weakly Supervised Video Salient Object DetectionCode1
Dynamic Context-Sensitive Filtering Network for Video Salient Object DetectionCode1
DS-Net: Dynamic Spatiotemporal Network for Video Salient Object DetectionCode1
Show:102550
← PrevPage 1 of 5Next →

No leaderboard results yet.