Papers › RANet: Ranking Attention Network for Fast Video Object Segmentation

RANet: Ranking Attention Network for Fast Video Object Segmentation

19 Aug 2019ICCV 2019 10arXiv:1908.06647archive 2025-07-28

Ziqin Wang, Jun Xu, Li Liu, Fan Zhu, Ling Shao

Despite online learning (OL) techniques have boosted the performance of semi-supervised video object segmentation (VOS) methods, the huge time costs of OL greatly restrict their practicality. Matching based and propagation based methods run at a faster speed by avoiding OL techniques. However, they are limited by sub-optimal accuracy, due to mismatching and drifting problems. In this paper, we develop a real-time yet very accurate Ranking Attention Network (RANet) for VOS. Specifically, to integrate the insights of matching based and propagation based methods, we employ an encoder-decoder framework to learn pixel-level similarity and segmentation in an end-to-end manner. To better utilize the similarity maps, we propose a novel ranking attention module, which automatically ranks and selects these maps for fine-grained VOS performance. Experiments on DAVIS-16 and DAVIS-17 datasets show that our RANet achieves the best speed-accuracy trade-off, e.g., with 33 milliseconds per frame and J&F=85.5% on DAVIS-16. With OL, our RANet reaches J&F=87.1% on DAVIS-16, exceeding state-of-the-art VOS methods. The code can be found at https://github.com/Storife/RANet.

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Tasks

DecoderObjectSemantic 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) RANet D16 val (F) 85.4 #24 of 26 Archive leaderboard report
Semi-Supervised Video Object Segmentation DAVIS (no YouTube-VOS training) RANet D16 val (G) 85.5 #24 of 26 Archive leaderboard report
Semi-Supervised Video Object Segmentation DAVIS (no YouTube-VOS training) RANet D16 val (J) 85.5 #24 of 26 Archive leaderboard report
Semi-Supervised Video Object Segmentation DAVIS (no YouTube-VOS training) RANet D17 test (F) 57.2 #24 of 26 Archive leaderboard report
Semi-Supervised Video Object Segmentation DAVIS (no YouTube-VOS training) RANet D17 test (G) 55.3 #24 of 26 Archive leaderboard report
Semi-Supervised Video Object Segmentation DAVIS (no YouTube-VOS training) RANet D17 test (J) 53.4 #24 of 26 Archive leaderboard report
Semi-Supervised Video Object Segmentation DAVIS (no YouTube-VOS training) RANet D17 val (F) 68.2 #24 of 26 Archive leaderboard report
Semi-Supervised Video Object Segmentation DAVIS (no YouTube-VOS training) RANet D17 val (G) 65.7 #24 of 26 Archive leaderboard report
Semi-Supervised Video Object Segmentation DAVIS (no YouTube-VOS training) RANet D17 val (J) 63.2 #24 of 26 Archive leaderboard report
Semi-Supervised Video Object Segmentation DAVIS (no YouTube-VOS training) RANet FPS 30.3 #24 of 26 Archive leaderboard report
Semi-Supervised Video Object Segmentation DAVIS 2016 RANet+ (online learning) F-measure (Decay) 8.2 #43 of 78 Archive leaderboard report
Semi-Supervised Video Object Segmentation DAVIS 2016 RANet+ (online learning) F-measure (Mean) 87.6 #43 of 78 Archive leaderboard report
Semi-Supervised Video Object Segmentation DAVIS 2016 RANet+ (online learning) F-measure (Recall) 96.1 #43 of 78 Archive leaderboard report
Semi-Supervised Video Object Segmentation DAVIS 2016 RANet+ (online learning) J&F 87.1 #43 of 78 Archive leaderboard report
Semi-Supervised Video Object Segmentation DAVIS 2016 RANet+ (online learning) Jaccard (Decay) 7.4 #43 of 78 Archive leaderboard report
Semi-Supervised Video Object Segmentation DAVIS 2016 RANet+ (online learning) Jaccard (Mean) 86.6 #43 of 78 Archive leaderboard report
Semi-Supervised Video Object Segmentation DAVIS 2016 RANet+ (online learning) Jaccard (Recall) 97 #43 of 78 Archive leaderboard report
Semi-Supervised Video Object Segmentation DAVIS 2016 RANet F-measure (Decay) 5.1 #51 of 78 Archive leaderboard report
Semi-Supervised Video Object Segmentation DAVIS 2016 RANet F-measure (Mean) 85.4 #51 of 78 Archive leaderboard report
Semi-Supervised Video Object Segmentation DAVIS 2016 RANet F-measure (Recall) 94.9 #51 of 78 Archive leaderboard report
Semi-Supervised Video Object Segmentation DAVIS 2016 RANet J&F 85.45 #51 of 78 Archive leaderboard report
Semi-Supervised Video Object Segmentation DAVIS 2016 RANet Jaccard (Decay) 6.2 #51 of 78 Archive leaderboard report
Semi-Supervised Video Object Segmentation DAVIS 2016 RANet Jaccard (Mean) 85.5 #51 of 78 Archive leaderboard report
Semi-Supervised Video Object Segmentation DAVIS 2016 RANet Jaccard (Recall) 97.2 #51 of 78 Archive leaderboard report
Semi-Supervised Video Object Segmentation DAVIS 2017 (test-dev) RANet F-measure (Decay) 22.1 #49 of 59 Archive leaderboard report
Semi-Supervised Video Object Segmentation DAVIS 2017 (test-dev) RANet F-measure (Mean) 57.3 #49 of 59 Archive leaderboard report
Semi-Supervised Video Object Segmentation DAVIS 2017 (test-dev) RANet F-measure (Recall) 67.7 #49 of 59 Archive leaderboard report
Semi-Supervised Video Object Segmentation DAVIS 2017 (test-dev) RANet J&F 55.4 #49 of 59 Archive leaderboard report
Semi-Supervised Video Object Segmentation DAVIS 2017 (test-dev) RANet Jaccard (Decay) 21.9 #49 of 59 Archive leaderboard report
Semi-Supervised Video Object Segmentation DAVIS 2017 (test-dev) RANet Jaccard (Mean) 53.4 #49 of 59 Archive leaderboard report
Semi-Supervised Video Object Segmentation DAVIS 2017 (test-dev) RANet Jaccard (Recall) 61.9 #49 of 59 Archive leaderboard report
Semi-Supervised Video Object Segmentation DAVIS 2017 (val) RANet F-measure (Decay) 19.7 #66 of 81 Archive leaderboard report
Semi-Supervised Video Object Segmentation DAVIS 2017 (val) RANet F-measure (Mean) 68.2 #66 of 81 Archive leaderboard report
Semi-Supervised Video Object Segmentation DAVIS 2017 (val) RANet F-measure (Recall) 78.8 #66 of 81 Archive leaderboard report
Semi-Supervised Video Object Segmentation DAVIS 2017 (val) RANet J&F 65.7 #66 of 81 Archive leaderboard report
Semi-Supervised Video Object Segmentation DAVIS 2017 (val) RANet Jaccard (Decay) 18.6 #66 of 81 Archive leaderboard report
Semi-Supervised Video Object Segmentation DAVIS 2017 (val) RANet Jaccard (Mean) 63.2 #66 of 81 Archive leaderboard report
Semi-Supervised Video Object Segmentation DAVIS 2017 (val) RANet Jaccard (Recall) 73.7 #66 of 81 Archive leaderboard report

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