Video Salient Object Detection
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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.
<span style="color:grey; opacity: 0.6">( Image credit: Shifting More Attention to Video Salient Object Detection, CVPR2019-Best Paper Finalist )</span>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Learning Motion and Temporal Cues for Unsupervised Video Object Segmentation
Transforming Static Images Using Generative Models for Video Salient Object Detection
Improving Unsupervised Video Object Segmentation via Fake Flow Generation
ViDSOD-100: A New Dataset and a Baseline Model for RGB-D Video Salient Object Detection
Reframe Anything: LLM Agent for Open World Video Reframing
SimulFlow: Simultaneously Extracting Feature and Identifying Target for Unsupervised Video Object Segmentation
A Spatial-Temporal Dual-Mode Mixed Flow Network for Panoramic Video Salient Object Detection
UniST: Towards Unifying Saliency Transformer for Video Saliency Prediction and Detection
Panoramic Video Salient Object Detection with Ambisonic Audio Guidance
PSNet: Parallel Symmetric Network for Video Salient Object Detection
Depth Quality-Inspired Feature Manipulation for Efficient RGB-D and Video Salient Object Detection
Motion-aware Memory Network for Fast Video Salient Object Detection
Hierarchical Feature Alignment Network for Unsupervised Video Object Segmentation
Weakly Supervised Video Salient Object Detection via Point Supervision
A Novel Long-term Iterative Mining Scheme for Video Salient Object Detection
Learning Video Salient Object Detection Progressively from Unlabeled Videos
A Unified Transformer Framework for Group-based Segmentation: Co-Segmentation, Co-Saliency Detection and Video Salient Object Detection
Depth-Cooperated Trimodal Network for Video Salient Object Detection
Video Salient Object Detection via Contrastive Features and Attention Modules
Full-Duplex Strategy for Video Object Segmentation
Guidance and Teaching Network for Video Salient Object Detection
Confidence-guided Adaptive Gate and Dual Differential Enhancement for Video Salient Object Detection
Video Salient Object Detection via Adaptive Local-Global Refinement
Weakly Supervised Video Salient Object Detection
DS-Net: Dynamic Spatiotemporal Network for Video Salient Object Detection
Fast Video Salient Object Detection via Spatiotemporal Knowledge Distillation
A Novel Video Salient Object Detection Method via Semi-supervised Motion Quality Perception
Exploring Rich and Efficient Spatial Temporal Interactions for Real Time Video Salient Object Detection
TENet: Triple Excitation Network for Video Salient Object Detection
Motion Guided Attention for Video Salient Object Detection
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels
Time Masking: Leveraging Temporal Information in Spoken Dialogue Systems
Salient Object Detection in Video using Deep Non-Local Neural Networks
Unsupervised Video Object Segmentation using Motion Saliency-Guided Spatio-Temporal Propagation
Video Salient Object Detection Using Spatiotemporal Deep Features
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.