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Semantic Segmentation
Semantic Segmentation On Densepass
Semantic Segmentation On Densepass
Metrics
mIoU
Results
Performance results of various models on this benchmark
Columns
Model Name
mIoU
Paper Title
Repository
PVT (Tiny, FPN)
31.20%
Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions
SwiftNet (Merge3)
32.04%
ISSAFE: Improving Semantic Segmentation in Accidents by Fusing Event-based Data
CLAN
31.46%
Taking A Closer Look at Domain Shift: Category-level Adversaries for Semantics Consistent Domain Adaptation
ERFNet
16.65%
ERFNet: Efficient Residual Factorized ConvNet for Real-time Semantic Segmentation
Seamless (Mapillary)
34.14%
Seamless Scene Segmentation
Trans4PASS+ (multi-scale)
57.23%
Behind Every Domain There is a Shift: Adapting Distortion-aware Vision Transformers for Panoramic Semantic Segmentation
SIM
44.58%
Differential Treatment for Stuff and Things: A Simple Unsupervised Domain Adaptation Method for Semantic Segmentation
SegFormer (MiT-B1)
38.5%
SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers
SETR (MLA, Transformer-L)
35.6%
Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers
Trans4PASS (single-scale)
55.25%
Bending Reality: Distortion-aware Transformers for Adapting to Panoramic Semantic Segmentation
Trans4PASS+ (single-scale)
56.45%
Behind Every Domain There is a Shift: Adapting Distortion-aware Vision Transformers for Panoramic Semantic Segmentation
DANet (ResNet-101)
28.5%
Dual Attention Network for Scene Segmentation
Fast-SCNN
24.6%
Fast-SCNN: Fast Semantic Segmentation Network
ECANet
43.02%
Capturing Omni-Range Context for Omnidirectional Segmentation
SegFormer (MiT-B2)
42.4%
SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers
USSS (Mapillary)
30.87%
Universal Semi-Supervised Semantic Segmentation
USSS (IDD)
26.98%
Universal Semi-Supervised Semantic Segmentation
PCS
53.83%
Prototypical Cross-domain Self-supervised Learning for Few-shot Unsupervised Domain Adaptation
DNL (ResNet-101)
32.1%
Disentangled Non-Local Neural Networks
ASMLP (MiT-B1)
42.05%
AS-MLP: An Axial Shifted MLP Architecture for Vision
0 of 36 row(s) selected.
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