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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
Exploring Rich and Efficient Spatial Temporal Interactions for Real Time Video Salient Object DetectionCode1
Structure-measure: A New Way to Evaluate Foreground MapsCode1
Saliency-Motion Guided Trunk-Collateral Network for Unsupervised Video Object Segmentation0
Transforming Static Images Using Generative Models for Video Salient Object Detection0
Improving Unsupervised Video Object Segmentation via Fake Flow Generation0
ViDSOD-100: A New Dataset and a Baseline Model for RGB-D Video Salient Object DetectionCode0
Reframe Anything: LLM Agent for Open World Video Reframing0
SimulFlow: Simultaneously Extracting Feature and Identifying Target for Unsupervised Video Object Segmentation0
A Spatial-Temporal Dual-Mode Mixed Flow Network for Panoramic Video Salient Object Detection0
UniST: Towards Unifying Saliency Transformer for Video Saliency Prediction and Detection0
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