| Name | Affiliation | Pixel Accuracy | Mean IoU | Score | Submission Time | |
|---|---|---|---|---|---|---|
| SenseCUSceneParsing | SenseTime and CUHK | 0.7478 | 0.3973 | 0.5721 | ||
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Description: It is one of the top entries from Scene Parsing Challenge 2016. |
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| Adelaide | Adelaide | 0.7454 | 0.3903 | 0.5673 | ||
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Description: It is one of the top entries from Scene Parsing Challenge 2016. |
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| EncNet-101 | Amazon, Rutgers, SenseTime, CUHK | 0.7374 | 0.3817 | 0.5596 | 2018-04-12 13:25 | |
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Description: Context Encoding for Semantic Segmentation (CVPR2018). Single model with base network of ResNet101. |
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| 360+MCG-ICT-CAS_SP | 360, MCG-ICT-CAS_SP and NUS | 0.7373 | 0.3750 | 0.5562 | ||
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Description: It is one of the top entries from Scene Parsing Challenge 2016. |
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| fromandto | thu | 0.7226 | 0.3669 | 0.5447 | 2018-03-05 05:18 | |
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Description:
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| LSF | Hunan University | 0.6893 | 0.3147 | 0.5020 | 2017-10-20 20:53 | |
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Description:
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| eaglevision | MIT | 0.6973 | 0.2971 | 0.4972 | 2017-12-09 10:54 | |
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Description:
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| xxx | xxx | 0.6849 | 0.3085 | 0.4967 | 2017-10-17 09:04 | |
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Description:
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| kw | ilc | 0.6788 | 0.3014 | 0.4901 | 2018-02-18 20:22 | |
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Description:
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| baseline-DilatedNet | MIT | 0.6541 | 0.2592 | 0.4567 | ||
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Description: This is a baseline model modified from Dilated Convolutional Networks by MIT Vision Group. |
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| uts_ppb | University of Technology Sydney | 0.6517 | 0.2599 | 0.4558 | 2017-01-21 00:43 | |
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Description:
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| baseline-FCN | MIT | 0.6477 | 0.2483 | 0.4480 | ||
|
Description: This is a baseline model modified from Fully Convolutional Networks (FCN) by MIT Vision Group. Original FCN model can be found at arXiv:1411.4038 |
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| baseline-SegNet | MIT | 0.6403 | 0.1754 | 0.4079 | ||
|
Description: This is a baseline model modified from SegNet by MIT Vision Group. Original SegNet model can be found at arXiv:1505.07293 |
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| SDR | SFGD | 0.0075 | 0.0008 | 0.0041 | 2017-02-23 01:57 | |
|
Description:
Link: chengdou,china |
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| Shuai Li | MIT | 0.0047 | 0.0004 | 0.0026 | 2017-12-08 17:02 | |
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Description:
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