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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 3140 of 48 papers

TitleStatusHype
Pyramid Dilated Deeper ConvLSTM for Video Salient Object Detection0
Reframe Anything: LLM Agent for Open World Video Reframing0
Saliency-Aware Geodesic Video Object Segmentation0
Saliency-Motion Guided Trunk-Collateral Network for Unsupervised Video Object Segmentation0
Salient Object Detection in Video using Deep Non-Local Neural Networks0
Sequential Clique Optimization for Video Object Segmentation0
SimulFlow: Simultaneously Extracting Feature and Identifying Target for Unsupervised Video Object Segmentation0
TENet: Triple Excitation Network for Video Salient Object Detection0
Time-Mapping Using Space-Time Saliency0
Time Masking: Leveraging Temporal Information in Spoken Dialogue Systems0
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