paper-with-me

Unsupervised Video Object Segmentation 벤치마크

Unsupervised Video Object Segmentation on FBMS test

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J

74 76.67 79.35 82.03 84.7 2018-09 2026-09 PDB — 74.0 (2018-09-01) PDB — 74.0 (2018-09-01) COSNet — 75.6 (2020-01-19) COSNet — 75.6 (2020-01-19) MATNet — 76.1 (2020-03-09) MATNet — 76.1 (2020-03-09) F2Net — 77.5 (2020-12-04) F2Net — 77.5 (2020-12-04) TransportNet — 78.7 (2021-01-01) AMC-Net — 76.5 (2021-01-01) TransportNet — 78.7 (2021-01-01) AMC-Net — 76.5 (2021-01-01) IMP — 77.5 (2021-12-23) IMP — 77.5 (2021-12-23) TMO (MiT-b1) — 80.0 (2022-09-04) TMO (RN-101) — 79.9 (2022-09-04) TMO (MiT-b1) — 80.0 (2022-09-04) TMO (RN-101) — 79.9 (2022-09-04) PMN — 77.7 (2022-09-08) PMN — 77.7 (2022-09-08) DPA — 83.4 (2022-11-22) DPA — 83.4 (2022-11-22) GSANet — 83.1 (2023-03-15) GSANet — 83.1 (2023-03-15) TMO++ (MiT-b1) — 83.2 (2023-09-26) TMO++ (RN-101) — 81.2 (2023-09-26) TMO++ (MiT-b1) — 83.2 (2023-09-26) TMO++ (RN-101) — 81.2 (2023-09-26) FakeFlow — 84.7 (2024-07-16) FakeFlow — 84.7 (2024-07-16) PDB — 74.0 (2018-09-01) COSNet — 75.6 (2020-01-19) MATNet — 76.1 (2020-03-09) F2Net — 77.5 (2020-12-04) TransportNet — 78.7 (2021-01-01) TMO (MiT-b1) — 80.0 (2022-09-04) DPA — 83.4 (2022-11-22) FakeFlow — 84.7 (2024-07-16)
RankModel J PaperCodeYear
1 FakeFlow 84.7 Improving Unsupervised Video Object Segmentation via Fake Flow Generation 2024
2 DPA 83.4 Dual Prototype Attention for Unsupervised Video Object Segmentation hydragon516/dpa 2022
3 TMO++ (MiT-b1) 83.2 Treating Motion as Option with Output Selection for Unsupervised Video Object Segmentation suhwan-cho/tmo 2023
4 GSANet 83.1 Guided Slot Attention for Unsupervised Video Object Segmentation hydragon516/gsanet 2023
5 TMO++ (RN-101) 81.2 Treating Motion as Option with Output Selection for Unsupervised Video Object Segmentation suhwan-cho/tmo 2023
6 TMO (MiT-b1) 80.0 Treating Motion as Option to Reduce Motion Dependency in Unsupervised Video Object Segmentation suhwan-cho/tmo · ahasan-haque/TMO-RAFT 2022
7 TMO (RN-101) 79.9 Treating Motion as Option to Reduce Motion Dependency in Unsupervised Video Object Segmentation suhwan-cho/tmo · ahasan-haque/TMO-RAFT 2022
8 TransportNet 78.7 Deep Transport Network for Unsupervised Video Object Segmentation 2021
9 PMN 77.7 Unsupervised Video Object Segmentation via Prototype Memory Network Hydragon516/PMN 2022
10 F2Net 77.5 F2Net: Learning to Focus on the Foreground for Unsupervised Video Object Segmentation 2020
10 IMP 77.5 Iteratively Selecting an Easy Reference Frame Makes Unsupervised Video Object Segmentation Easier 2021
12 AMC-Net 76.5 Learning Motion-Appearance Co-Attention for Zero-Shot Video Object Segmentation isyangshu/amc-net 2021
13 MATNet 76.1 Motion-Attentive Transition for Zero-Shot Video Object Segmentation tfzhou/MATNet 2020
14 COSNet 75.6 See More, Know More: Unsupervised Video Object Segmentation with Co-Attention Siamese Networks carrierlxk/COSNet 2020
15 PDB 74.0 Pyramid Dilated Deeper ConvLSTM for Video Salient Object Detection 2018
16 FakeFlow 84.7 Improving Unsupervised Video Object Segmentation via Fake Flow Generation 2024
17 DPA 83.4 Dual Prototype Attention for Unsupervised Video Object Segmentation hydragon516/dpa 2022
18 TMO++ (MiT-b1) 83.2 Treating Motion as Option with Output Selection for Unsupervised Video Object Segmentation suhwan-cho/tmo 2023
19 GSANet 83.1 Guided Slot Attention for Unsupervised Video Object Segmentation hydragon516/gsanet 2023
20 TMO++ (RN-101) 81.2 Treating Motion as Option with Output Selection for Unsupervised Video Object Segmentation suhwan-cho/tmo 2023
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