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Plateforme

Semantic Segmentation On Cityscapes

Métriques

Mean IoU (class)

Résultats

Résultats de performance de divers modèles sur ce benchmark

Paper Title
VLTSeg86.4Strong but simple: A Baseline for Domain Generalized Dense Perception by CLIP-based Transfer Learning
MetaPrompt-SD86.2Harnessing Diffusion Models for Visual Perception with Meta Prompts
InternImage-H86.1%InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions
HS3-Fuse85.8%HS3: Learning with Proper Task Complexity in Hierarchically Supervised Semantic Segmentation
InverseForm85.6%InverseForm: A Loss Function for Structured Boundary-Aware Segmentation
ViT-Adapter-L (Mask2Former, BEiT pretrain)85.2%Vision Transformer Adapter for Dense Predictions
SERNet-Former84.83SERNet-Former: Semantic Segmentation by Efficient Residual Network with Attention-Boosting Gates and Attention-Fusion Networks
Depth Anything84.8%Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data
HRNetV2 + OCR +84.5%Segmentation Transformer: Object-Contextual Representations for Semantic Segmentation
EfficientPS84.21%EfficientPS: Efficient Panoptic Segmentation
Panoptic-DeepLab84.2%Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation
HRNetV2 + OCR (w/ ASP)83.7%Segmentation Transformer: Object-Contextual Representations for Semantic Segmentation
DCNAS(coarse + Mapillary)83.6%DCNAS: Densely Connected Neural Architecture Search for Semantic Image Segmentation
Euclidean Frank-Wolfe CRFs (backbone: DeepLabv3+)(coarse)83.6%Regularized Frank-Wolfe for Dense CRFs: Generalizing Mean Field and Beyond
ResNeSt200 (Mapillary)83.3%ResNeSt: Split-Attention Networks
GALDNet(+Mapillary)++83.3%Global Aggregation then Local Distribution in Fully Convolutional Networks
HANet (Height-driven Attention Networks by LGE A&B)(coarse)83.2%Cars Can't Fly up in the Sky: Improving Urban-Scene Segmentation via Height-driven Attention Networks
kMaX-DeepLab (ConvNeXt-L, fine only)83.2%kMaX-DeepLab: k-means Mask Transformer
SegFormer (MiT-B5, Mapillary)83.1%SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers
MRFM(coarse)83.0%Multi Receptive Field Network for Semantic Segmentation
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