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 > Semi-Supervised Semantic Segmentation | nuScenes | MeanTeacher (Range View) | http://arxiv.org/abs/1703.01780v6 | mIoU (50% Labels) | 69.4 |
10-shot image generation > Semantic Segmentation > Semi-Supervised Semantic Segmentation | nuScenes | CBST (Range View) | http://openaccess.thecvf.com/content_ECCV_2018/html/Yang_Zou_Unsupervised_Domain_Adaptation_ECCV_2018_paper.html | mIoU (1% Labels) | 40.9 |
10-shot image generation > Semantic Segmentation > Semi-Supervised Semantic Segmentation | nuScenes | CBST (Range View) | http://openaccess.thecvf.com/content_ECCV_2018/html/Yang_Zou_Unsupervised_Domain_Adaptation_ECCV_2018_paper.html | mIoU (10% Labels) | 60.5 |
10-shot image generation > Semantic Segmentation > Semi-Supervised Semantic Segmentation | nuScenes | CBST (Range View) | http://openaccess.thecvf.com/content_ECCV_2018/html/Yang_Zou_Unsupervised_Domain_Adaptation_ECCV_2018_paper.html | mIoU (20% Labels) | 64.3 |
10-shot image generation > Semantic Segmentation > Semi-Supervised Semantic Segmentation | nuScenes | CBST (Range View) | http://openaccess.thecvf.com/content_ECCV_2018/html/Yang_Zou_Unsupervised_Domain_Adaptation_ECCV_2018_paper.html | mIoU (50% Labels) | 69.3 |
10-shot image generation > Semantic Segmentation > Semi-Supervised Semantic Segmentation | nuScenes | CPS (Range View) | https://arxiv.org/abs/2106.01226v2 | mIoU (1% Labels) | 40.7 |
10-shot image generation > Semantic Segmentation > Semi-Supervised Semantic Segmentation | nuScenes | CPS (Range View) | https://arxiv.org/abs/2106.01226v2 | mIoU (10% Labels) | 60.8 |
10-shot image generation > Semantic Segmentation > Semi-Supervised Semantic Segmentation | nuScenes | CPS (Range View) | https://arxiv.org/abs/2106.01226v2 | mIoU (20% Labels) | 64.9 |
10-shot image generation > Semantic Segmentation > Semi-Supervised Semantic Segmentation | nuScenes | CPS (Range View) | https://arxiv.org/abs/2106.01226v2 | mIoU (50% Labels) | 68.0 |
10-shot image generation > Semantic Segmentation > Semi-Supervised Semantic Segmentation | nuScenes | Sup.-only (Range View) | https://arxiv.org/abs/2109.03787v1 | mIoU (1% Labels) | 38.3 |
10-shot image generation > Semantic Segmentation > Semi-Supervised Semantic Segmentation | nuScenes | Sup.-only (Range View) | https://arxiv.org/abs/2109.03787v1 | mIoU (10% Labels) | 57.5 |
10-shot image generation > Semantic Segmentation > Semi-Supervised Semantic Segmentation | nuScenes | Sup.-only (Range View) | https://arxiv.org/abs/2109.03787v1 | mIoU (20% Labels) | 62.7 |
10-shot image generation > Semantic Segmentation > Semi-Supervised Semantic Segmentation | nuScenes | Sup.-only (Range View) | https://arxiv.org/abs/2109.03787v1 | mIoU (50% Labels) | 67.6 |
10-shot image generation > Semantic Segmentation > Semi-Supervised Semantic Segmentation | Cityscapes 2% labeled | GIST and RIST (DeepLabv2 with ResNet101, MSCOCO pre-trained) | https://arxiv.org/abs/2103.17105v3 | Validation mIoU | 53.51% |
10-shot image generation > Semantic Segmentation > Semi-Supervised Semantic Segmentation | Cityscapes 2% labeled | ClassMix (DeepLabv2 with ResNet101, MSCOCO pre-trained) | https://arxiv.org/abs/2007.07936v2 | Validation mIoU | 52.14% |
10-shot image generation > Semantic Segmentation > Semi-Supervised Semantic Segmentation | Cityscapes 2% labeled | S4GAN (DeepLabv2 with ResNet101, MSCOCO pre-trained) | https://arxiv.org/abs/1908.05724v1 | Validation mIoU | 50.48% |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | PIDNet-L | https://arxiv.org/abs/2206.02066v3 | mIoU | 80.6% |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | PIDNet-L | https://arxiv.org/abs/2206.02066v3 | Time (ms) | 32.2 |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | PIDNet-L | https://arxiv.org/abs/2206.02066v3 | Frame (fps) | 31.1(3090) |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | SFNet-R18 | https://arxiv.org/abs/2002.10120v3 | mIoU | 80.4% |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | SFNet-R18 | https://arxiv.org/abs/2002.10120v3 | Time (ms) | 39.2 |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | SFNet-R18 | https://arxiv.org/abs/2002.10120v3 | Frame (fps) | 25.7(1080Ti) |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | PIDNet-M | https://arxiv.org/abs/2206.02066v3 | mIoU | 79.8% |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | PIDNet-M | https://arxiv.org/abs/2206.02066v3 | Time (ms) | 23.7 |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | PIDNet-M | https://arxiv.org/abs/2206.02066v3 | Frame (fps) | 42.2(3090) |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | PIDNet-S | https://arxiv.org/abs/2206.02066v3 | mIoU | 78.6% |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | PIDNet-S | https://arxiv.org/abs/2206.02066v3 | Time (ms) | 10.7 |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | PIDNet-S | https://arxiv.org/abs/2206.02066v3 | Frame (fps) | 93.2(3090) |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | RegSeg (no ImageNet pretraining) | https://arxiv.org/abs/2111.09957v3 | mIoU | 78.3% |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | RegSeg (no ImageNet pretraining) | https://arxiv.org/abs/2111.09957v3 | Time (ms) | 33 |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | RegSeg (no ImageNet pretraining) | https://arxiv.org/abs/2111.09957v3 | Frame (fps) | 30 |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | PP-LiteSeg-B2 | https://arxiv.org/abs/2204.02681v1 | mIoU | 77.5% |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | PP-LiteSeg-B2 | https://arxiv.org/abs/2204.02681v1 | Frame (fps) | 102.6(1080Ti) |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | DDRNet-23-slim | https://arxiv.org/abs/2101.06085v2 | mIoU | 77.4% |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | DDRNet-23-slim | https://arxiv.org/abs/2101.06085v2 | Time (ms) | 9.8 |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | DDRNet-23-slim | https://arxiv.org/abs/2101.06085v2 | Frame (fps) | 101.6(2080Ti) |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | STDC2-75 | https://arxiv.org/abs/2104.13188v1 | mIoU | 76.8% |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | STDC2-75 | https://arxiv.org/abs/2104.13188v1 | Frame (fps) | 97.0(1080Ti) |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | SwinMTL | https://arxiv.org/abs/2403.10662v1 | mIoU | 76.41% |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | U-HarDNet-70 | https://arxiv.org/abs/1909.00948v1 | mIoU | 75.9% |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | U-HarDNet-70 | https://arxiv.org/abs/1909.00948v1 | Time (ms) | 18.8
(1080Ti) |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | U-HarDNet-70 | https://arxiv.org/abs/1909.00948v1 | Frame (fps) | 53
(1080Ti) |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | HyperSeg-M | https://arxiv.org/abs/2012.11582v2 | mIoU | 75.8% |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | HyperSeg-M | https://arxiv.org/abs/2012.11582v2 | Time (ms) | 27.1 |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | HyperSeg-M | https://arxiv.org/abs/2012.11582v2 | Frame (fps) | 36.9 |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | SwiftNetRN-18 | http://arxiv.org/abs/1903.08469v2 | mIoU | 75.5% |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | SwiftNetRN-18 | http://arxiv.org/abs/1903.08469v2 | Frame (fps) | 39.9 |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | STDC1-75 | https://arxiv.org/abs/2104.13188v1 | mIoU | 75.3% |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | STDC1-75 | https://arxiv.org/abs/2104.13188v1 | Frame (fps) | 126.7 |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | BiSeNet V2-Large | https://arxiv.org/abs/2004.02147v1 | mIoU | 75.3% |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | BiSeNet V2-Large | https://arxiv.org/abs/2004.02147v1 | Time (ms) | 21.1 |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | BiSeNet V2-Large | https://arxiv.org/abs/2004.02147v1 | Frame (fps) | 47.3 |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | TD4-BISE18 | https://arxiv.org/abs/2004.01800v2 | mIoU | 74.9% |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | TD4-BISE18 | https://arxiv.org/abs/2004.01800v2 | Time (ms) | 21 |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | TD4-BISE18 | https://arxiv.org/abs/2004.01800v2 | Frame (fps) | 47.6 (Titan X) |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | PP-LiteSeg-T2 | https://arxiv.org/abs/2204.02681v1 | mIoU | 74.9% |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | PP-LiteSeg-T2 | https://arxiv.org/abs/2204.02681v1 | Frame (fps) | 143.6(1080Ti) |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | ShelfNet18 | https://arxiv.org/abs/1811.11254v6 | mIoU | 74.8% |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | ShelfNet18 | https://arxiv.org/abs/1811.11254v6 | Time (ms) | 16.9 |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | ShelfNet18 | https://arxiv.org/abs/1811.11254v6 | Frame (fps) | 59.2 |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | BiSeNet(ResNet-18) | http://arxiv.org/abs/1808.00897v1 | mIoU | 74.7% |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | BiSeNet(ResNet-18) | http://arxiv.org/abs/1808.00897v1 | Time (ms) | 15.2 |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | BiSeNet(ResNet-18) | http://arxiv.org/abs/1808.00897v1 | Frame (fps) | 65.5 |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | BiSeNet | http://arxiv.org/abs/1808.00897v1 | mIoU | 74.7% |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | BiSeNet | http://arxiv.org/abs/1808.00897v1 | Frame (fps) | 65.5 |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | PP-LiteSeg-B1 | https://arxiv.org/abs/2204.02681v1 | mIoU | 73.9% |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | PP-LiteSeg-B1 | https://arxiv.org/abs/2204.02681v1 | Frame (fps) | 195.3(1080Ti) |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | SegBlocks-RN18 (t=0.4) | https://arxiv.org/abs/2011.12025v2 | mIoU | 73.8% |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | SegBlocks-RN18 (t=0.4) | https://arxiv.org/abs/2011.12025v2 | Frame (fps) | 48.6 (1080Ti) |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | STDC2-50 | https://arxiv.org/abs/2104.13188v1 | mIoU | 73.4% |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | STDC2-50 | https://arxiv.org/abs/2104.13188v1 | Frame (fps) | 188.6 |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | BiSeNet V2 | https://arxiv.org/abs/2004.02147v1 | mIoU | 72.6% |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | BiSeNet V2 | https://arxiv.org/abs/2004.02147v1 | Time (ms) | 6.4 |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | BiSeNet V2 | https://arxiv.org/abs/2004.02147v1 | Frame (fps) | 156 |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | PP-LiteSeg-T1 | https://arxiv.org/abs/2204.02681v1 | mIoU | 72.0% |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | PP-LiteSeg-T1 | https://arxiv.org/abs/2204.02681v1 | Frame (fps) | 273.6(1080Ti) |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | STDC1-50 | https://arxiv.org/abs/2104.13188v1 | mIoU | 71.9% |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | STDC1-50 | https://arxiv.org/abs/2104.13188v1 | Frame (fps) | 250.4(1080Ti) |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | FRRN | http://arxiv.org/abs/1611.08323v2 | mIoU | 71.8% |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | FRRN | http://arxiv.org/abs/1611.08323v2 | Time (ms) | 469 |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | FRRN | http://arxiv.org/abs/1611.08323v2 | Frame (fps) | 2.1 |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | FasterSeg | null | mIoU | 71.5% |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | FasterSeg | null | Time (ms) | 6.1 |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | FasterSeg | null | Frame (fps) | 163.9 |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | LDFNet | https://arxiv.org/abs/1809.09077v3 | mIoU | 71.3% |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | LDFNet | https://arxiv.org/abs/1809.09077v3 | Frame (fps) | 18.4 (1080Ti) |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | ESNet | https://arxiv.org/abs/1906.09826v1 | mIoU | 70.7% |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | ESNet | https://arxiv.org/abs/1906.09826v1 | Time (ms) | 16 |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | ESNet | https://arxiv.org/abs/1906.09826v1 | Frame (fps) | 63 |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | LEDNet | https://arxiv.org/abs/1905.02423v3 | mIoU | 70.6% |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | LEDNet | https://arxiv.org/abs/1905.02423v3 | Time (ms) | 14 |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | LEDNet | https://arxiv.org/abs/1905.02423v3 | Frame (fps) | 71 |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | ICNet | http://arxiv.org/abs/1704.08545v2 | mIoU | 70.6% |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | ICNet | http://arxiv.org/abs/1704.08545v2 | Time (ms) | 33 |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | ICNet | http://arxiv.org/abs/1704.08545v2 | Frame (fps) | 30.3 |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | BiSeNet(Xception39) | http://arxiv.org/abs/1808.00897v1 | mIoU | 68.4% |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | BiSeNet(Xception39) | http://arxiv.org/abs/1808.00897v1 | Time (ms) | 9.5 |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | BiSeNet(Xception39) | http://arxiv.org/abs/1808.00897v1 | Frame (fps) | 105.8 |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | Template-Based-NAS-arch1 | https://arxiv.org/abs/1904.02365v2 | mIoU | 67.8% |
10-shot image generation > Semantic Segmentation > Real-Time Semantic Segmentation | Cityscapes test | Template-Based-NAS-arch1 | https://arxiv.org/abs/1904.02365v2 | Time (ms) | 97 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.