Semantic Segmentation on DDD17 (test)
78.56mIoUBRENet
Evaluation Results
| Method | Links | ||
|---|---|---|---|
| BRENetModality=RGB-Event, Backbone=MiT-B2, Event Representation=MET2025.05 | 78.56 | 96.61 | |
| EISNetModality=RGB-Event, Backbone=MiT-B2, Event Representation=AET2025.05 | 75.03 | 96.04 | |
| SpikingEDNModality=RGB-Event, Backbone=FCN, Event Representation=Voxel Grid2025.05 | 72.57 | — | |
| SegNeXtModality=RGB, Backbone=MSCAN-B2025.05 | 71.46 | 95.97 | |
| SegFormerModality=RGB, Backbone=MiT-B22025.05 | 71.05 | 95.73 | |
| SE-AdapterModality=RGB-Event, Backbone=SAM, Event Representation=MSP2025.05 | 69.06 | 95.32 | |
| CMXModality=RGB-Event, Backbone=MiT-B2, Event Representation=Voxel Grid2025.05 | 67.47 | 94.2 | |
| Hybrid-SegModality=RGB-Event, Backbone=FCN, Event Representation=Voxel Grid2025.05 | 67.31 | 95.07 | |
| CMNeXtModality=RGB-Event, Backbone=MiT-B2, Event Representation=Voxel Grid2025.05 | 66.99 | 93.82 | |
| CMESSModality=RGB-Event, Backbone=E2ViD, Event Representation=Voxel Grid2025.05 | 64.3 | 92.07 | |
| OpenESSModality=RGB-Event, Backbone=E2VID, Event Representation=Voxel Grid2025.05 | 63 | 91.05 | |
| EDCNet-S2DModality=RGB-Event, Backbone=ResNet-101, Event Representation=Voxel Grid2025.05 | 61.99 | 93.8 | |
| GEPBackbone=ViT-B/14, Pre-training Dataset=Event-1.8M, Ep.=242026.03 | 61.9 | 72.39 | |
| ESSModality=Event, Backbone=E2ViD, Event Representation=Voxel Grid2025.05 | 61.37 | 91.08 | |
| ESSTraining Data=events2022.03 | 61.37 | 91.08 | |
| ESS (E)Network=ANN, Params (×10^6)=12.91, #FLOPS_ANN (×10^9)=14.22, ETotal (mJ)=65.412022.11 | 61.37 | 91.08 | |
| ESSDataset=Cityscape, Ep.=502026.03 | 61.37 | 70.87 | |
| HALSIEModality=RGB-Event, Backbone=FCN, Event Representation=Voxel Grid2025.05 | 60.66 | 92.5 | |
| HALSIENetwork=Hybrid, Params (×10^6)=1.82, #FLOPS_ANN (×10^9)=3.84, #FLOPS_SNN (×10^9)=0.267, ETotal (mJ)=17.892022.11 | 60.66 | 92.5 | |
| ESSTraining Data=events+frames2022.03 | 60.43 | 90.37 | |
| ESS (E+F)Network=ANN, Params (×10^6)=12.91, #FLOPS_ANN (×10^9)=14.22, ETotal (mJ)=65.412022.11 | 60.43 | 90.37 | |
| GEPBackbone=ViT-S/14, Pre-training Dataset=Event-1.8M, Ep.=242026.03 | 60.28 | 72.14 | |
| DTLTraining Data=events2022.03 | 58.8 | — | |
| DTLNetwork=ANN, Params (×10^6)=60.48, #FLOPS_ANN (×10^9)=16.74, ETotal (mJ)=77.012022.11 | 58.8 | — | |
| GEPBackbone=ViT-S/14, Pre-training Dataset=N-ImageNet, Ep.=242026.03 | 58.42 | 69.59 | |
| EVDistillTraining Data=events2022.03 | 58.02 | — | |
| EvDistillNetwork=ANN, Params (×10^6)=59.34, #FLOPS_ANN (×10^9)=12.45, ETotal (mJ)=57.272022.11 | 58.02 | — | |
| VID2ETraining Data=synthetic + events2022.03 | 56.01 | 90.19 | |
| ViD2ENetwork=ANN, Params (×10^6)=29.09, #FLOPS_ANN (×10^9)=73.62, ETotal (mJ)=338.652022.11 | 56.01 | 90.19 | |
| ECDDPBackbone=ViT-S/16, Pre-training Dataset=E-TartanAir, Ep.=3002026.03 | 55.73 | 64.77 | |
| EV-SegNetModality=Event, Backbone=Xception, Event Representation=6-Channel Image2025.05 | 54.81 | 89.76 | |
| EV-SegNetTraining Data=events2022.03 | 54.81 | 89.76 | |
| EV-SegNetNetwork=ANN, Params (×10^6)=29.09, #FLOPS_ANN (×10^9)=73.62, ETotal (mJ)=338.652022.11 | 54.81 | 89.76 | |
| Spike-BRGNetModality=RGB-Event, Backbone=MiT-B2, Event Representation=Voxel Grid2025.05 | 54.72 | — | |
| ECDPBackbone=ViT-S/16, Pre-training Dataset=N-ImageNet, Ep.=3002026.03 | 54.66 | 66.08 | |
| DINOv2Backbone=ViT-S/14, Pre-training Dataset=LVD-142M2026.03 | 53.85 | 64.5 | |
| MAEBackbone=ViT-B/16, Pre-training Dataset=ImageNet1K, Ep.=8002026.03 | 53.76 | 64.78 | |
| ESSTraining Setting=Unsupervised Domain Adaptation (UDA)2022.03 | 52.46 | 87.86 | |
| BeiTBackbone=ViT-B/16, Pre-training Dataset=ImageNet1K, Ep.=8002026.03 | 52.39 | 61.95 | |
| VID2ETraining Setting=Unsupervised Domain Adaptation (UDA)2022.03 | 45.48 | 85.93 | |
| E2VIDTraining Setting=Unsupervised Domain Adaptation (UDA)2022.03 | 44.77 | 83.24 | |
| E2ViDNetwork=ANN, Params (×10^6)=10.71, #FLOPS_ANN (×10^9)=16.65, ETotal (mJ)=76.592022.11 | 44.77 | 83.24 | |
| ViTBackbone=ViT-S/162026.03 | 36.65 | 46.21 | |
| Spiking-DeeplabNetwork=SNN, Params (×10^6)=4.14, #FLOPS_SNN (×10^9)=54.34, ETotal (mJ)=48.912022.11 | 33.7 | — | |
| EV-TransferTraining Setting=Unsupervised Domain Adaptation (UDA)2022.03 | 14.91 | 47.37 | |
| EV-TransferNetwork=ANN, Params (×10^6)=7.37, #FLOPS_ANN (×10^9)=7.88, ETotal (mJ)=36.252022.11 | 14.91 | 47.37 |