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Efficient Regional Memory Network for Video Object Segmentation
[CVPR2021] Guided Interactive Video Object Segmentation Using Reliability-Based Attention Maps - 2
Learning Video Object Segmentation with Visual Memory
Unsupervised Video Object Segmentation via Prototype Memory Network
Video Object Segmentation using Space-Time Memory Networks (ICCV 2019)
Memory Enhanced Global Local Aggregation for Video Object Detection
Fast Video Object Segmentation With Temporal Aggregation Network and Dynamic Template Matching
Learning What to Learn for Video Object Segmentation: ECCV2020 Oral, Long video
BubbleNets: Video object segmentation for computer vision
Rethinking Space-Time Networks with Improved Memory Coverage for Efficient Video Object Segmentation
Video Object Segmentation using Space-Time Memory Networks (ICCV 2019) 2
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Last Updated: September 29, 2026
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Summary
Authors: Jiaxu Miao, Yunchao Wei, Yi Yang Description: A description and demo of our work for Paper(arXiv) - arxiv.org/abs/2104.10386 Github - github.com/yuk6heo/GIS-RAmap. Authors: Lee, Minhyeok*; Cho, Suhwan; LEE, SEUNGHOON; Park, Chaewon; Lee, Sangyoun Description: Unsupervised Video comparisons on DAVIS benchmark. Learn all the ways Microsoft is a part of CVPR 2020: microsoft.com/en-us/research/event/cvpr-2020/ Authors: Xuhua Huang, Jiarui Xu, Yu-Wing Tai, Chi-Keung Tang Description: Significant progress has been made in To improve computer vision of emerging technologies, University of Michigan researchers are working on Bubblnets: A new deep ... Project page: hkchengrex.github.io/STCN Paper: arxiv.org/abs/2106.05210 Code: ...
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