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SOTA
语义分割
Semantic Segmentation On Pascal Context
Semantic Segmentation On Pascal Context
评估指标
mIoU
评测结果
各个模型在此基准测试上的表现结果
Columns
模型名称
mIoU
Paper Title
Repository
VPNeXt
71.1
VPNeXt -- Rethinking Dense Decoding for Plain Vision Transformer
-
PlainSeg (EVA-02-L)
71.0
Minimalist and High-Performance Semantic Segmentation with Plain Vision Transformers
InternImage-H
70.3
InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions
RSSeg-ViT-L (BEiT pretrain)
68.9
Representation Separation for Semantic Segmentation with Vision Transformers
-
ViT-Adapter-L (Mask2Former, BEiT pretrain)
68.2
Vision Transformer Adapter for Dense Predictions
ViT-Adapter-L (UperNet, BEiT pretrain)
67.5
Vision Transformer Adapter for Dense Predictions
RSSeg-ViT-L
67.5
Representation Separation for Semantic Segmentation with Vision Transformers
-
SegViT (ours)
65.3
SegViT: Semantic Segmentation with Plain Vision Transformers
CAA + CAR (ConvNeXt-Large + JPU)
64.1
CAR: Class-aware Regularizations for Semantic Segmentation
SenFormer (Swin-L)
64.0
Efficient Self-Ensemble for Semantic Segmentation
Sequential Ensemble (Segformer + HRNet)
62.1
Sequential Ensembling for Semantic Segmentation
-
CAA + Simple decoder (Efficientnet-B7)
60.5
Channelized Axial Attention for Semantic Segmentation -- Considering Channel Relation within Spatial Attention for Semantic Segmentation
DPT-Hybrid
60.46
Vision Transformers for Dense Prediction
CAA (Efficientnet-B7)
60.1
Channelized Axial Attention for Semantic Segmentation -- Considering Channel Relation within Spatial Attention for Semantic Segmentation
HRNetV2 + OCR + RMI (PaddleClas pretrained)
59.6
Segmentation Transformer: Object-Contextual Representations for Semantic Segmentation
Seg-L-Mask/16
59.0
Segmenter: Transformer for Semantic Segmentation
ResNeSt-269
58.9
ResNeSt: Split-Attention Networks
DEPICT-SA (ViT-L multi-scale)
58.6
Rethinking Decoders for Transformer-based Semantic Segmentation: A Compression Perspective
ResNeSt-200
58.4
ResNeSt: Split-Attention Networks
DEPICT-SA (ViT-L single-scale)
57.9
Rethinking Decoders for Transformer-based Semantic Segmentation: A Compression Perspective
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