task_path stringlengths 3 199 ⌀ | dataset stringlengths 1 128 ⌀ | model_name stringlengths 1 223 ⌀ | paper_url stringlengths 21 601 ⌀ | metric_name stringlengths 1 50 ⌀ | metric_value stringlengths 1 9.22k ⌀ |
|---|---|---|---|---|---|
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | RGB-T-Glass-Segmentation | SegFormer | https://arxiv.org/abs/2105.15203v3 | MAE | 0.053 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | RGB-T-Glass-Segmentation | ShapeConv | https://arxiv.org/abs/2108.10528v1 | MAE | 0.054 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | RGB-T-Glass-Segmentation | DCFNet | http://openaccess.thecvf.com//content/CVPR2021/html/Ji_Calibrated_RGB-D_Salient_Object_Detection_CVPR_2021_paper.html | MAE | 0.056 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | RGB-T-Glass-Segmentation | RTFNet | https://ieeexplore.ieee.org/abstract/document/8666745 | MAE | 0.058 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | RGB-T-Glass-Segmentation | DANet | https://arxiv.org/abs/2007.06811v2 | MAE | 0.069 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | RGB-T-Glass-Segmentation | UCNet | https://arxiv.org/abs/2004.05763v1 | MAE | 0.071 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | RGB-T-Glass-Segmentation | Segmenter | https://arxiv.org/abs/2105.05633v3 | MAE | 0.072 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | RGB-T-Glass-Segmentation | SSF | http://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Select_Supplement_and_Focus_for_RGB-D_Saliency_Detection_CVPR_2020_paper.html | MAE | 0.097 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | RGB-T-Glass-Segmentation | ATSA | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/6188_ECCV_2020_paper.php | MAE | 0.098 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | RGB-T-Glass-Segmentation | EBLNet | https://arxiv.org/abs/2103.15734v2 | MAE | 0.104 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | RGB-T-Glass-Segmentation | CoNet | https://arxiv.org/abs/2007.11782v1 | MAE | 0.145 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | RGB-T-Glass-Segmentation | DPANet | https://arxiv.org/abs/2003.08608v4 | MAE | 0.154 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | MFN Dataset | RoadFormer+ (ConvNeXt-L) | https://arxiv.org/abs/2407.21631v2 | mIOU | 62.7 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | MFN Dataset | HAPNet | https://arxiv.org/abs/2404.03527v2 | mIOU | 61.5 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | MFN Dataset | CRM_RGBT_Seg | https://arxiv.org/abs/2303.17386v2 | mIOU | 61.4 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | MFN Dataset | Sigma-base | https://arxiv.org/abs/2404.04256v2 | mIOU | 61.3 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | MFN Dataset | CSFNet-2 | https://arxiv.org/abs/2407.01328v1 | mIOU | 59.98 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | MFN Dataset | CSFNet-2 | https://arxiv.org/abs/2407.01328v1 | Frame (fps) | 72.7 (3090) |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | MFN Dataset | CMNeXt (B4) | https://arxiv.org/abs/2303.01480v1 | mIOU | 59.9 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | MFN Dataset | CMX (B4) | https://arxiv.org/abs/2203.04838v5 | mIOU | 59.7 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | MFN Dataset | DPLNet | https://arxiv.org/abs/2312.00360v2 | mIOU | 59.3 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | MFN Dataset | UniRGB-IR | https://arxiv.org/abs/2404.17360v2 | mIOU | 59.3 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | MFN Dataset | SHIFNet | https://arxiv.org/abs/2503.02581v1 | mIOU | 59.2 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | MFN Dataset | IGFNet(B2) | https://ieeexplore.ieee.org/abstract/document/10354613 | mIOU | 59.0 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | MFN Dataset | EAEFNet (ResNet-152) | https://arxiv.org/abs/2303.15710v1 | mIOU | 58.9 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | MFN Dataset | CAINet (MobileNet-V2) | https://arxiv.org/abs/2401.01624v1 | mIOU | 58.6% |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | MFN Dataset | SpiderMesh (B4) | https://arxiv.org/abs/2303.08692v2 | mIOU | 58.4 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | MFN Dataset | CMX (B2) | https://arxiv.org/abs/2203.04838v5 | mIOU | 58.2 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | MFN Dataset | StitchFusion | https://arxiv.org/abs/2408.01343v1 | mIOU | 58.13 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | MFN Dataset | SpiderMesh (ResNet-152) | https://arxiv.org/abs/2303.08692v2 | mIOU | 57.9 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | MFN Dataset | CACFNet | https://ieeexplore.ieee.org/abstract/document/10251592 | mIOU | 57.8 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | MFN Dataset | VPFNet | https://arxiv.org/abs/2307.08536v1 | mIOU | 57.61 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | MFN Dataset | EGFNet(ConvNeXt) | https://ieeexplore.ieee.org/abstract/document/10234530 | mIOU | 57.5 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | MFN Dataset | DooDLeNet | https://arxiv.org/abs/2204.10266v1 | mIOU | 57.3 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | MFN Dataset | PAIF | https://arxiv.org/abs/2308.03979v1 | mIOU | 56.5 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | MFN Dataset | RSFNet (ResNet-101) | https://arxiv.org/abs/2306.10364v1 | mIOU | 56.2 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | MFN Dataset | GEBNet | https://ieeexplore.ieee.org/abstract/document/9937048 | mIOU | 56.2 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | MFN Dataset | SegMiF | https://arxiv.org/abs/2308.02097v1 | mIOU | 56.1 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | MFN Dataset | SpiderMesh (ResNet-101) | https://arxiv.org/abs/2303.08692v2 | mIOU | 56.1 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | MFN Dataset | MTANet | https://ieeexplore.ieee.org/abstract/document/9749834 | mIOU | 56.1 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | MFN Dataset | CENet | https://ieeexplore.ieee.org/document/10049523 | mIOU | 56.1 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | MFN Dataset | CSFNet-1 | https://arxiv.org/abs/2407.01328v1 | mIOU | 56.05 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | MFN Dataset | CSFNet-1 | https://arxiv.org/abs/2407.01328v1 | Frame (fps) | 106.3 (3090) |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | MFN Dataset | CSRPNet | https://arxiv.org/abs/2308.12534v1 | mIOU | 56.0 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | MFN Dataset | EAFFNet (ResNet-50) | https://arxiv.org/abs/2303.15710v1 | mIOU | 55.9 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | MFN Dataset | FEANet | https://arxiv.org/abs/2110.08988v1 | mIOU | 55.3 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | MFN Dataset | LASNet | https://arxiv.org/abs/2210.14530v1 | mIOU | 54.9 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | MFN Dataset | ABMDRNet | http://openaccess.thecvf.com//content/CVPR2021/html/Zhang_ABMDRNet_Adaptive-Weighted_Bi-Directional_Modality_Difference_Reduction_Network_for_RGB-T_Semantic_CVPR_2021_paper.html | mIOU | 54.8 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | MFN Dataset | EGFNet | https://arxiv.org/abs/2112.05144v1 | mIOU | 54.8 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | MFN Dataset | SegFormer (B4) | https://arxiv.org/abs/2105.15203v3 | mIOU | 54.8 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | MFN Dataset | SpiderMesh (ResNet-50) | https://arxiv.org/abs/2303.08692v2 | mIOU | 54.4 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | MFN Dataset | SegFormer (B2) | https://arxiv.org/abs/2105.15203v3 | mIOU | 53.2 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | MFN Dataset | RTFNet | https://ieeexplore.ieee.org/abstract/document/8666745 | mIOU | 53.2 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | MFN Dataset | HRNet | https://arxiv.org/abs/1908.07919v2 | mIOU | 51.7 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | MFN Dataset | APCNet | http://openaccess.thecvf.com/content_CVPR_2019/html/He_Adaptive_Pyramid_Context_Network_for_Semantic_Segmentation_CVPR_2019_paper.html | mIOU | 49.0 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | MFN Dataset | SwinT | https://arxiv.org/abs/2103.14030v2 | mIOU | 49.0 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | MFN Dataset | PST900 | https://arxiv.org/abs/1909.10980v1 | mIOU | 48.4 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | MFN Dataset | FTNet | https://ieeexplore.ieee.org/abstract/document/9585453 | mIOU | 47.12 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | MFN Dataset | ACNet | https://arxiv.org/abs/1905.10089v1 | mIOU | 46.3 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | MFN Dataset | Depth-aware CNN | http://arxiv.org/abs/1803.06791v1 | mIOU | 46.1 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | MFN Dataset | PSPNet | http://arxiv.org/abs/1612.01105v2 | mIOU | 46.1 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | MFN Dataset | SA-Gate | https://arxiv.org/abs/2007.09183v1 | mIOU | 45.8 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | MFN Dataset | UNet | http://arxiv.org/abs/1505.04597v1 | mIOU | 45.1 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | MFN Dataset | FRRN | http://arxiv.org/abs/1611.08323v2 | mIOU | 44.2 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | MFN Dataset | CCNet | https://arxiv.org/abs/1811.11721v2 | mIOU | 43.3 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | MFN Dataset | SegNet | http://arxiv.org/abs/1511.00561v3 | mIOU | 42.3 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | MFN Dataset | DANet | http://arxiv.org/abs/1809.02983v4 | mIOU | 41.3 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | MFN Dataset | MFNet | https://ieeexplore.ieee.org/abstract/document/8206396 | mIOU | 39.7 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | MFN Dataset | ERFNet | https://ieeexplore.ieee.org/abstract/document/8063438 | mIOU | 36.1 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | SCUT-Seg Dataset | FTNet | https://ieeexplore.ieee.org/abstract/document/9585453 | mIOU | 66.73 |
10-shot image generation > Semantic Segmentation > Semi-Supervised Semantic Segmentation | PASCAL Context 12.5% labeled | GuidedMix-Net(DeepLab v2 with ResNet101, ImageNet pretrained) | https://arxiv.org/abs/2106.15064v2 | Validation mIoU | 40.3% |
10-shot image generation > Semantic Segmentation > Semi-Supervised Semantic Segmentation | PASCAL Context 12.5% labeled | s4GAN+MLMT (DeepLab v2 ImageNet pre-trained) | https://arxiv.org/abs/1908.05724v1 | Validation mIoU | 35.3 |
10-shot image generation > Semantic Segmentation > Semi-Supervised Semantic Segmentation | Cityscapes 50% labeled | UniMatch V2 (DINOv2-B) | https://arxiv.org/abs/2410.10777v2 | Validation mIoU | 85.1% |
10-shot image generation > Semantic Segmentation > Semi-Supervised Semantic Segmentation | Cityscapes 50% labeled | FARCLUSS | https://arxiv.org/abs/2506.11142v2 | Validation mIoU | 81.0 |
10-shot image generation > Semantic Segmentation > Semi-Supervised Semantic Segmentation | Cityscapes 50% labeled | SemiVL (ViT-B/16) | https://arxiv.org/abs/2311.16241v1 | Validation mIoU | 80.6% |
10-shot image generation > Semantic Segmentation > Semi-Supervised Semantic Segmentation | Cityscapes 50% labeled | Dual Teacher | https://openreview.net/forum?id=JXvszuOqY3 | Validation mIoU | 80.52 |
10-shot image generation > Semantic Segmentation > Semi-Supervised Semantic Segmentation | Cityscapes 50% labeled | CorrMatch (Deeplabv3+ with ResNet-101) | https://arxiv.org/abs/2306.04300v3 | Validation mIoU | 80.4% |
10-shot image generation > Semantic Segmentation > Semi-Supervised Semantic Segmentation | Cityscapes 50% labeled | AEL (DeepLab v3+ with ResNet-101 pretraind on ImageNet-1K) | https://arxiv.org/abs/2110.05474v1 | Validation mIoU | 80.28% |
10-shot image generation > Semantic Segmentation > Semi-Supervised Semantic Segmentation | Cityscapes 50% labeled | CPS (DeepLab v3+ with ImageNet-pretrained ResNet-101, single scale inference) | https://arxiv.org/abs/2106.01226v2 | Validation mIoU | 80.21% |
10-shot image generation > Semantic Segmentation > Semi-Supervised Semantic Segmentation | Cityscapes 50% labeled | PrevMatch (ResNet-101) | https://arxiv.org/abs/2405.20610v1 | Validation mIoU | 80.1% |
10-shot image generation > Semantic Segmentation > Semi-Supervised Semantic Segmentation | Cityscapes 50% labeled | S4MC | https://arxiv.org/abs/2308.13900v2 | Validation mIoU | 79.76% |
10-shot image generation > Semantic Segmentation > Semi-Supervised Semantic Segmentation | Cityscapes 50% labeled | UniMatch | https://arxiv.org/abs/2208.09910v2 | Validation mIoU | 79.5% |
10-shot image generation > Semantic Segmentation > Semi-Supervised Semantic Segmentation | Cityscapes 50% labeled | n-CPS (ResNet-50) | https://arxiv.org/abs/2112.07528v4 | Validation mIoU | 79.29% |
10-shot image generation > Semantic Segmentation > Semi-Supervised Semantic Segmentation | Cityscapes 50% labeled | PS-MT (DeepLab v3+ with ImageNet-pretrained ResNet-50, single scale inference) | https://arxiv.org/abs/2111.12903v3 | Validation mIoU | 79.22% |
10-shot image generation > Semantic Segmentation > Semi-Supervised Semantic Segmentation | Cityscapes 50% labeled | PrevMatch (ResNet-50) | https://arxiv.org/abs/2405.20610v1 | Validation mIoU | 79.2% |
10-shot image generation > Semantic Segmentation > Semi-Supervised Semantic Segmentation | Cityscapes 50% labeled | U2PL (DeepLab v3+ with ResNet-101 pretraind on ImageNet-1K, AEL) | https://arxiv.org/abs/2203.03884v2 | Validation mIoU | 79.12% |
10-shot image generation > Semantic Segmentation > Semi-Supervised Semantic Segmentation | Cityscapes 50% labeled | PCR (DeepLab v3+ with ResNet-101 pretraind on ImageNet-1K) | https://arxiv.org/abs/2210.04388v1 | Validation mIoU | 79.11% |
10-shot image generation > Semantic Segmentation > Semi-Supervised Semantic Segmentation | Cityscapes 50% labeled | LaserMix (DeepLab v3+, ImageNet pre- trained ResNet50, single scale inference) | https://arxiv.org/abs/2207.00026v4 | Validation mIoU | 79.1% |
10-shot image generation > Semantic Segmentation > Semi-Supervised Semantic Segmentation | Cityscapes 50% labeled | SimpleBaseline(DeepLabv3+ with ImageNet pretrained Xception65, single scale inference) | https://arxiv.org/abs/2104.07256v4 | Validation mIoU | 78.7% |
10-shot image generation > Semantic Segmentation > Semi-Supervised Semantic Segmentation | Cityscapes 50% labeled | CPCL (DeepLab v3+ with ResNet-50) | https://arxiv.org/abs/2211.16701v2 | Validation mIoU | 78.17% |
10-shot image generation > Semantic Segmentation > Semi-Supervised Semantic Segmentation | Cityscapes 50% labeled | Error Localization Network (DeeplabV3 with ResNet-50) | https://arxiv.org/abs/2204.02078v3 | Validation mIoU | 75.33% |
10-shot image generation > Semantic Segmentation > Semi-Supervised Semantic Segmentation | Cityscapes 50% labeled | GuidedMix-Net(DeepLab v2 with ResNet101, ImageNet pretrained) | https://arxiv.org/abs/2106.15064v2 | Validation mIoU | 69.8% |
10-shot image generation > Semantic Segmentation > Semi-Supervised Semantic Segmentation | Cityscapes 50% labeled | ReCo (DeepLab v2 with ResNet-101 backbone, ImageNet pretrained) | https://arxiv.org/abs/2104.04465v4 | Validation mIoU | 68.69% |
10-shot image generation > Semantic Segmentation > Semi-Supervised Semantic Segmentation | Cityscapes 50% labeled | ClassMix (DeepLab v2 MSCOCO pretrained) | https://arxiv.org/abs/2007.07936v2 | Validation mIoU | 66.29% |
10-shot image generation > Semantic Segmentation > Semi-Supervised Semantic Segmentation | Cityscapes 50% labeled | Adversarial (DeepLab v2 ImageNet pre-trained) | http://arxiv.org/abs/1802.07934v2 | Validation mIoU | 65.70% |
10-shot image generation > Semantic Segmentation > Semi-Supervised Semantic Segmentation | 2D-3D-S | M3L (Linear Fusion B2) | https://arxiv.org/abs/2304.10756v1 | mIoU (0.1% labels) | 40.05 |
10-shot image generation > Semantic Segmentation > Semi-Supervised Semantic Segmentation | 2D-3D-S | M3L (Linear Fusion B2) | https://arxiv.org/abs/2304.10756v1 | mIoU (0.2% labels) | 44.62 |
10-shot image generation > Semantic Segmentation > Semi-Supervised Semantic Segmentation | 2D-3D-S | M3L (Linear Fusion B2) | https://arxiv.org/abs/2304.10756v1 | mIoU (1% labels) | 49.28 |
10-shot image generation > Semantic Segmentation > Semi-Supervised Semantic Segmentation | Cityscapes 10% labeled | IM++ (416x208, 2.7m parameters, no pretraining) | https://arxiv.org/abs/2401.14387v2 | Mean IoU (class) | 0.428 |
10-shot image generation > Semantic Segmentation > Semi-Supervised Semantic Segmentation | Cityscapes 12.5% labeled | UniMatch V2 (DINOv2-B) | https://arxiv.org/abs/2410.10777v2 | Validation mIoU | 84.3% |
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