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Models/Multi-Task Light-Weight-RefineNet

Multi-Task Light-Weight-RefineNet

Reported on 6 benchmarks across 4 tasks · 1 paper

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Medical2 results

Semantic SegmentationonNYU Depth v2
Speed(ms/f)· 2018-09-13
13
best: 72 (PSPNet101)
Real-Time Joint Semantic Segmentation and Depth Estimation Using Asymmetric Annotations arXiv:1809.04766
Semantic SegmentationonNYU Depth v2
mIoU· 2018-09-13
42
best: 54.1 (AsymFormer)
Real-Time Joint Semantic Segmentation and Depth Estimation Using Asymmetric Annotations arXiv:1809.04766

Audio2 results

10-shot image generationonNYU Depth v2
Speed(ms/f)· 2018-09-13
13
best: 72 (PSPNet101)
Real-Time Joint Semantic Segmentation and Depth Estimation Using Asymmetric Annotations arXiv:1809.04766
10-shot image generationonNYU Depth v2
mIoU· 2018-09-13
42
best: 54.1 (AsymFormer)
Real-Time Joint Semantic Segmentation and Depth Estimation Using Asymmetric Annotations arXiv:1809.04766

Computer Vision1 result

Depth EstimationonNYU-Depth V2
RMSE· 2018-09-13
0.565
best: 0.013 (Defocus/DepthNet (Normalized))
Real-Time Joint Semantic Segmentation and Depth Estimation Using Asymmetric Annotations arXiv:1809.04766

Methodology1 result

3DonNYU-Depth V2
RMSE· 2018-09-13
0.565
best: 0.013 (Defocus/DepthNet (Normalized))
Real-Time Joint Semantic Segmentation and Depth Estimation Using Asymmetric Annotations arXiv:1809.04766