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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 1120 of 48 papers

TitleStatusHype
Learning Motion and Temporal Cues for Unsupervised Video Object SegmentationCode1
Full-Duplex Strategy for Video Object SegmentationCode1
Guidance and Teaching Network for Video Salient Object Detection0
Flow Guided Recurrent Neural Encoder for Video Salient Object Detection0
A Spatial-Temporal Dual-Mode Mixed Flow Network for Panoramic Video Salient Object Detection0
Fast Video Salient Object Detection via Spatiotemporal Knowledge Distillation0
Confidence-guided Adaptive Gate and Dual Differential Enhancement for Video Salient Object Detection0
A Novel Long-term Iterative Mining Scheme for Video Salient Object Detection0
Minimum Barrier Salient Object Detection at 80 FPS0
Improving Unsupervised Video Object Segmentation via Fake Flow Generation0
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