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Video Salient Object Detection On Davis 2016
评估指标
AVERAGE MAE
MAX F-MEASURE
S-Measure
评测结果
各个模型在此基准测试上的表现结果
| Paper Title | Repository | ||||
|---|---|---|---|---|---|
| TIMP | 0.185 | - | 0.574 | Time-Mapping Using Space-Time Saliency | - |
| MSTM | 0.174 | - | 0.566 | Real-Time Salient Object Detection With a Minimum Spanning Tree | - |
| MB+M | 0.173 | - | 0.600 | Minimum Barrier Salient Object Detection at 80 FPS | - |
| SAGM | 0.105 | - | 0.664 | Saliency-Aware Geodesic Video Object Segmentation | - |
| FGRN | 0.043 | 0.783 | 0.838 | Flow Guided Recurrent Neural Encoder for Video Salient Object Detection | - |
| MBNM | 0.031 | 0.862 | 0.887 | Unsupervised Video Object Segmentation with Motion-based Bilateral Networks | - |
| RCRNet+NER | 0.028 | 0.859 | 0.884 | Semi-Supervised Video Salient Object Detection Using Pseudo-Labels | |
| PDB | 0.028 | - | 0.882 | Pyramid Dilated Deeper ConvLSTM for Video Salient Object Detection | - |
| SSAV | 0.028 | 0.861 | 0.893 | Shifting More Attention to Video Salient Object Detection | - |
| UFO | 0.015 | 0.906 | 0.918 | A Unified Transformer Framework for Group-based Segmentation: Co-Segmentation, Co-Saliency Detection and Video Salient Object Detection | |
| RealFlow | 0.010 | 0.939 | 0.945 | Transforming Static Images Using Generative Models for Video Salient Object Detection | - |
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