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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 4148 of 48 papers

TitleStatusHype
Flow Guided Recurrent Neural Encoder for Video Salient Object Detection0
Video Salient Object Detection Using Spatiotemporal Deep Features0
Structure-measure: A New Way to Evaluate Foreground MapsCode1
Video Salient Object Detection via Fully Convolutional Networks0
Real-Time Salient Object Detection With a Minimum Spanning TreeCode0
Minimum Barrier Salient Object Detection at 80 FPS0
Saliency-Aware Geodesic Video Object Segmentation0
Time-Mapping Using Space-Time Saliency0
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