Papers › Efficient Regional Memory Network for Video Object Segmentation

Efficient Regional Memory Network for Video Object Segmentation

24 Mar 2021CVPR 2021 1arXiv:2103.12934archive 2025-07-28

Haozhe Xie, Hongxun Yao, Shangchen Zhou, Shengping Zhang, Wenxiu Sun

Recently, several Space-Time Memory based networks have shown that the object cues (e.g. video frames as well as the segmented object masks) from the past frames are useful for segmenting objects in the current frame. However, these methods exploit the information from the memory by global-to-global matching between the current and past frames, which lead to mismatching to similar objects and high computational complexity. To address these problems, we propose a novel local-to-local matching solution for semi-supervised VOS, namely Regional Memory Network (RMNet). In RMNet, the precise regional memory is constructed by memorizing local regions where the target objects appear in the past frames. For the current query frame, the query regions are tracked and predicted based on the optical flow estimated from the previous frame. The proposed local-to-local matching effectively alleviates the ambiguity of similar objects in both memory and query frames, which allows the information to be passed from the regional memory to the query region efficiently and effectively. Experimental results indicate that the proposed RMNet performs favorably against state-of-the-art methods on the DAVIS and YouTube-VOS datasets.

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Decoder hzxie/RMNet/models/rmnet.py official repository ran · metamorphic tier: invariant MIT (permissive) · 2825de5a65f2df6f · report
KeyValue hzxie/RMNet/models/rmnet.py official repository ran · metamorphic tier: deterministic fingerprinted MIT (permissive) · 49a9e22dab36ada0 · report
MemoryReader hzxie/RMNet/models/rmnet.py official repository ran · metamorphic tier: deterministic fingerprinted MIT (permissive) · 3e5c7b748d94955b · report
Refine hzxie/RMNet/models/rmnet.py official repository ran · metamorphic tier: invariant MIT (permissive) · 47c819e0ff62ea4b · report
ResBlock hzxie/RMNet/models/rmnet.py official repository ran · metamorphic tier: invariant fingerprinted MIT (permissive) · 80d697d9ca997bc6 · report
EncoderMemory hzxie/RMNet/models/rmnet.py official repository unverified MIT (permissive) · a64ada2dd41f87cf · report
EncoderQuery hzxie/RMNet/models/rmnet.py official repository unverified MIT (permissive) · f3c30027b1d3eb8a · report
RMNet hzxie/RMNet/models/rmnet.py official repository unverified MIT (permissive) · a77041027801ccb7 · report
RegionalAttentionMapGenerator hzxie/RMNet/models/rmnet.py official repository unverified MIT (permissive) · 3f210dd69e2c0c9c · report
RegionalAttentionMapGeneratorFunction hzxie/RMNet/models/rmnet.py official repository unverified MIT (permissive) · cd28fc6fe9cf1306 · report

Tasks

ObjectOne-shot visual object segmentationOptical Flow EstimationSemantic SegmentationSemi-Supervised Video Object SegmentationVideo Object SegmentationVideo Semantic Segmentation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Semi-Supervised Video Object Segmentation DAVIS (no YouTube-VOS training) RMNet D16 val (F) 82.3 #9 of 26 Archive leaderboard report
Semi-Supervised Video Object Segmentation DAVIS (no YouTube-VOS training) RMNet D16 val (G) 81.5 #9 of 26 Archive leaderboard report
Semi-Supervised Video Object Segmentation DAVIS (no YouTube-VOS training) RMNet D16 val (J) 80.6 #9 of 26 Archive leaderboard report
Semi-Supervised Video Object Segmentation DAVIS (no YouTube-VOS training) RMNet D17 val (F) 77.2 #9 of 26 Archive leaderboard report
Semi-Supervised Video Object Segmentation DAVIS (no YouTube-VOS training) RMNet D17 val (G) 75.0 #9 of 26 Archive leaderboard report
Semi-Supervised Video Object Segmentation DAVIS (no YouTube-VOS training) RMNet D17 val (J) 72.8 #9 of 26 Archive leaderboard report
Semi-Supervised Video Object Segmentation DAVIS (no YouTube-VOS training) RMNet FPS 11.9 #9 of 26 Archive leaderboard report
Semi-Supervised Video Object Segmentation DAVIS 2016 RMNet F-measure (Mean) 88.7 #39 of 78 Archive leaderboard report
Semi-Supervised Video Object Segmentation DAVIS 2016 RMNet J&F 88.8 #39 of 78 Archive leaderboard report
Semi-Supervised Video Object Segmentation DAVIS 2016 RMNet Jaccard (Mean) 88.9 #39 of 78 Archive leaderboard report
Semi-Supervised Video Object Segmentation DAVIS 2017 (test-dev) RMNet F-measure (Mean) 78.1 #34 of 59 Archive leaderboard report
Semi-Supervised Video Object Segmentation DAVIS 2017 (test-dev) RMNet J&F 75.0 #34 of 59 Archive leaderboard report
Semi-Supervised Video Object Segmentation DAVIS 2017 (test-dev) RMNet Jaccard (Mean) 71.9 #34 of 59 Archive leaderboard report
Semi-Supervised Video Object Segmentation DAVIS 2017 (val) RMNet F-measure (Mean) 86.0 #34 of 81 Archive leaderboard report
Semi-Supervised Video Object Segmentation DAVIS 2017 (val) RMNet J&F 83.5 #34 of 81 Archive leaderboard report
Semi-Supervised Video Object Segmentation DAVIS 2017 (val) RMNet Jaccard (Mean) 81.0 #34 of 81 Archive leaderboard report
Semi-Supervised Video Object Segmentation YouTube-VOS 2018 RMNet F-Measure (Seen) 85.7 #36 of 53 Archive leaderboard report
Semi-Supervised Video Object Segmentation YouTube-VOS 2018 RMNet F-Measure (Unseen) 82.4 #36 of 53 Archive leaderboard report
Semi-Supervised Video Object Segmentation YouTube-VOS 2018 RMNet Jaccard (Seen) 82.1 #36 of 53 Archive leaderboard report
Semi-Supervised Video Object Segmentation YouTube-VOS 2018 RMNet Jaccard (Unseen) 75.7 #36 of 53 Archive leaderboard report
Semi-Supervised Video Object Segmentation YouTube-VOS 2018 RMNet Overall 81.5 #36 of 53 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

VOS

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