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 > Class-Incremental Semantic Segmentation > Overlapped 100-5 | ADE20K | RCNet-101 | https://arxiv.org/abs/2203.05402v1 | mIoU | 29.6 |
10-shot image generation > Semantic Segmentation > Class-Incremental Semantic Segmentation > Overlapped 100-5 | ADE20K | PLOP | https://arxiv.org/abs/2011.11390v3 | mIoU | 28.75 |
10-shot image generation > Semantic Segmentation > Class-Incremental Semantic Segmentation > Overlapped 100-5 | ADE20K | MiB | https://arxiv.org/abs/2011.11390v3 | mIoU | 25.96 |
10-shot image generation > Semantic Segmentation > Class-Incremental Semantic Segmentation > Overlapped 100-5 | ADE20K | ILT | https://arxiv.org/abs/1907.13372v4 | mIoU | 0.5 |
10-shot image generation > Semantic Segmentation > Class-Incremental Semantic Segmentation > Disjoint 10-1 | PASCAL VOC 2012 | SSUL-M | https://arxiv.org/abs/2106.11562v3 | mIoU | 53.50 |
10-shot image generation > Semantic Segmentation > Class-Incremental Semantic Segmentation > Disjoint 10-1 | PASCAL VOC 2012 | SSUL | https://arxiv.org/abs/2106.11562v3 | mIoU | 50.87 |
10-shot image generation > Semantic Segmentation > Class-Incremental Semantic Segmentation > Disjoint 10-1 | PASCAL VOC 2012 | RCNet-101 | https://arxiv.org/abs/2203.05402v1 | mIoU | 18.2 |
10-shot image generation > Semantic Segmentation > Class-Incremental Semantic Segmentation > Disjoint 10-1 | PASCAL VOC 2012 | SDR | https://arxiv.org/abs/2103.06342v3 | mIoU | 14.3 |
10-shot image generation > Semantic Segmentation > Class-Incremental Semantic Segmentation > Disjoint 10-1 | PASCAL VOC 2012 | PLOP | https://arxiv.org/abs/2011.11390v3 | mIoU | 8.4 |
10-shot image generation > Semantic Segmentation > Class-Incremental Semantic Segmentation > Disjoint 10-1 | PASCAL VOC 2012 | MiB | https://arxiv.org/abs/2002.00718v2 | mIoU | 6.9 |
10-shot image generation > Semantic Segmentation > Class-Incremental Semantic Segmentation > Disjoint 10-1 | PASCAL VOC 2012 | ILT | https://arxiv.org/abs/1907.13372v4 | mIoU | 5.4 |
10-shot image generation > Semantic Segmentation > Class-Incremental Semantic Segmentation > Disjoint 10-1 | PASCAL VOC 2012 | LWF | http://arxiv.org/abs/1606.09282v3 | mIoU | 4.3 |
10-shot image generation > Semantic Segmentation > Class-Incremental Semantic Segmentation > Overlapped 100-50 | ADE20K | MBS | https://arxiv.org/abs/2407.11859v1 | mIoU | 45.7 |
10-shot image generation > Semantic Segmentation > Class-Incremental Semantic Segmentation > Overlapped 100-50 | ADE20K | MiB+AWT | https://arxiv.org/abs/2210.07207v1 | mIoU | 35.6 |
10-shot image generation > Semantic Segmentation > Class-Incremental Semantic Segmentation > Overlapped 100-50 | ADE20K | RCNet-101 | https://arxiv.org/abs/2203.05402v1 | mIoU | 34.5 |
10-shot image generation > Semantic Segmentation > Class-Incremental Semantic Segmentation > Overlapped 100-50 | ADE20K | SSUL-M | https://arxiv.org/abs/2106.11562v3 | mIoU | 34.37 |
10-shot image generation > Semantic Segmentation > Class-Incremental Semantic Segmentation > Overlapped 100-50 | ADE20K | SSUL | https://arxiv.org/abs/2106.11562v3 | mIoU | 33.58 |
10-shot image generation > Semantic Segmentation > Class-Incremental Semantic Segmentation > Overlapped 100-50 | ADE20K | PLOP | https://arxiv.org/abs/2011.11390v3 | mIoU | 32.94 |
10-shot image generation > Semantic Segmentation > Class-Incremental Semantic Segmentation > Overlapped 100-50 | ADE20K | MiB | https://arxiv.org/abs/2011.11390v3 | mIoU | 32.79 |
10-shot image generation > Semantic Segmentation > Class-Incremental Semantic Segmentation > Overlapped 50-50 | ADE20K | MBS | https://arxiv.org/abs/2407.11859v1 | mIoU | 45.4 |
10-shot image generation > Semantic Segmentation > Class-Incremental Semantic Segmentation > Overlapped 50-50 | ADE20K | MiB+AWT | https://arxiv.org/abs/2210.07207v1 | mIoU | 33.5 |
10-shot image generation > Semantic Segmentation > Class-Incremental Semantic Segmentation > Overlapped 50-50 | ADE20K | RCNet-101 | https://arxiv.org/abs/2203.05402v1 | mIoU | 32.5 |
10-shot image generation > Semantic Segmentation > Class-Incremental Semantic Segmentation > Overlapped 50-50 | ADE20K | PLOP | https://arxiv.org/abs/2011.11390v3 | mIoU | 30.4 |
10-shot image generation > Semantic Segmentation > Class-Incremental Semantic Segmentation > Overlapped 50-50 | ADE20K | SSUL-M | https://arxiv.org/abs/2106.11562v3 | mIoU | 29.77 |
10-shot image generation > Semantic Segmentation > Class-Incremental Semantic Segmentation > Overlapped 50-50 | ADE20K | SSUL | https://arxiv.org/abs/2106.11562v3 | mIoU | 29.56 |
10-shot image generation > Semantic Segmentation > Class-Incremental Semantic Segmentation > Overlapped 50-50 | ADE20K | MiB | https://arxiv.org/abs/2011.11390v3 | mIoU | 29.31 |
10-shot image generation > Semantic Segmentation > Class-Incremental Semantic Segmentation > Overlapped 100-10 | ADE20K | MBS | https://arxiv.org/abs/2407.11859v1 | Mean IoU (test) | 44.5 |
10-shot image generation > Semantic Segmentation > Class-Incremental Semantic Segmentation > Overlapped 100-10 | ADE20K | SATS-M | https://arxiv.org/abs/2203.07667v3 | Mean IoU (test) | 35.45 |
10-shot image generation > Semantic Segmentation > Class-Incremental Semantic Segmentation > Overlapped 100-10 | ADE20K | MiB+AWT | https://arxiv.org/abs/2210.07207v1 | Mean IoU (test) | 33.2 |
10-shot image generation > Semantic Segmentation > Class-Incremental Semantic Segmentation > Overlapped 100-10 | ADE20K | RCNet-101 | https://arxiv.org/abs/2203.05402v1 | Mean IoU (test) | 32.1 |
10-shot image generation > Semantic Segmentation > Class-Incremental Semantic Segmentation > Overlapped 100-10 | ADE20K | PLOP | https://arxiv.org/abs/2011.11390v3 | Mean IoU (test) | 31.59 |
10-shot image generation > Semantic Segmentation > Class-Incremental Semantic Segmentation > Overlapped 100-10 | ADE20K | MiB | https://arxiv.org/abs/2011.11390v3 | Mean IoU (test) | 29.24 |
10-shot image generation > Semantic Segmentation > Class-Incremental Semantic Segmentation > Overlapped 5-3 | PASCAL VOC 2012 | MBS | https://arxiv.org/abs/2407.11859v1 | Mean IoU (test) | 78.1 |
10-shot image generation > Semantic Segmentation > Class-Incremental Semantic Segmentation > Overlapped 5-3 | PASCAL VOC 2012 | SATS-M | https://arxiv.org/abs/2203.07667v3 | Mean IoU (test) | 71.36 |
10-shot image generation > Semantic Segmentation > Class-Incremental Semantic Segmentation > Overlapped 5-3 | PASCAL VOC 2012 | SATS | https://arxiv.org/abs/2203.07667v3 | Mean IoU (test) | 67.36 |
10-shot image generation > Semantic Segmentation > Class-Incremental Semantic Segmentation > Overlapped 5-3 | PASCAL VOC 2012 | SSUL+AWT | https://arxiv.org/abs/2210.07207v1 | Mean IoU (test) | 57.1 |
10-shot image generation > Semantic Segmentation > Class-Incremental Semantic Segmentation > Overlapped 14-1 | Cityscapes | MiB+AWT | https://arxiv.org/abs/2210.07207v1 | mIoU | 46.9 |
10-shot image generation > Semantic Segmentation > UNET Segmentation | Munich Sentinel2 Crop Segmentation | Swin UNETR | https://www.spiedigitallibrary.org/conference-proceedings-of-spie/13072/1307208/Enhancing-crop-segmentation-in-satellite-image-time-series-with-transformer/10.1117/12.3023389.short#_=_ | Overall Accuracy | 95.26 |
10-shot image generation > Semantic Segmentation > UNET Segmentation | Munich Sentinel2 Crop Segmentation | UNet3D | https://www.spiedigitallibrary.org/conference-proceedings-of-spie/13072/1307208/Enhancing-crop-segmentation-in-satellite-image-time-series-with-transformer/10.1117/12.3023389.short#_=_ | Overall Accuracy | 94.73 |
10-shot image generation > Semantic Segmentation > UNET Segmentation | Munich Sentinel2 Crop Segmentation | 3D FPN with NDVI Loss | https://www.mdpi.com/2220-9964/10/7/483/htm | Overall Accuracy | 93.55 |
10-shot image generation > Semantic Segmentation > UNET Segmentation | Munich Sentinel2 Crop Segmentation | Sequential Recurrent Encoders | http://arxiv.org/abs/1802.02080v4 | Overall Accuracy | 89.60 |
10-shot image generation > Semantic Segmentation > UNET Segmentation | Munich Sentinel2 Crop Segmentation | DeepLabv3 3D | https://www.spiedigitallibrary.org/conference-proceedings-of-spie/13072/1307208/Enhancing-crop-segmentation-in-satellite-image-time-series-with-transformer/10.1117/12.3023389.short#_=_ | Overall Accuracy | 85.98 |
10-shot image generation > Semantic Segmentation > Polyp Segmentation | Kvasir-SEG | PVT-CASCADE | https://openaccess.thecvf.com/content/WACV2023/html/Rahman_Medical_Image_Segmentation_via_Cascaded_Attention_Decoding_WACV_2023_paper.html | mDice | 0.9258 |
10-shot image generation > Semantic Segmentation > Polyp Segmentation | Kvasir-SEG | PVT-CASCADE | https://openaccess.thecvf.com/content/WACV2023/html/Rahman_Medical_Image_Segmentation_via_Cascaded_Attention_Decoding_WACV_2023_paper.html | mIoU | 0.8776 |
10-shot image generation > Semantic Segmentation > Polyp Segmentation | Kvasir-SEG | KDAS | https://arxiv.org/abs/2312.08555v3 | mDice | 0.913 |
10-shot image generation > Semantic Segmentation > Polyp Segmentation | Kvasir-SEG | KDAS | https://arxiv.org/abs/2312.08555v3 | mIoU | 0.848 |
10-shot image generation > Semantic Segmentation > Polyp Segmentation | Kvasir-SEG | TGA-Net | https://arxiv.org/abs/2205.04280v1 | mDice | 0.8982 |
10-shot image generation > Semantic Segmentation > Polyp Segmentation | Kvasir-SEG | TGA-Net | https://arxiv.org/abs/2205.04280v1 | mIoU | 0.8330 |
10-shot image generation > Semantic Segmentation > Polyp Segmentation | Kvasir-SEG | PEFNet | https://arxiv.org/abs/2301.06673v2 | mDice | 0.8818 |
10-shot image generation > Semantic Segmentation > Polyp Segmentation | Kvasir-SEG | PEFNet | https://arxiv.org/abs/2301.06673v2 | mIoU | 0.8163 |
10-shot image generation > Semantic Segmentation > Polyp Segmentation | Kvasir-SEG | TransNetR | https://arxiv.org/abs/2303.07428v1 | mDice | 0.8706 |
10-shot image generation > Semantic Segmentation > Polyp Segmentation | Kvasir-SEG | TransNetR | https://arxiv.org/abs/2303.07428v1 | mIoU | 0.8016 |
10-shot image generation > Semantic Segmentation > Polyp Segmentation | Kvasir-SEG | ResUNet++ | https://arxiv.org/abs/1911.07067v1 | mDice | 0.8133 |
10-shot image generation > Semantic Segmentation > Polyp Segmentation | Kvasir-SEG | ResUNet++ | https://arxiv.org/abs/1911.07067v1 | mIoU | 0.7927 |
10-shot image generation > Semantic Segmentation > Polyp Segmentation | Kvasir-SEG | ResUNet | https://arxiv.org/abs/1911.07069v1 | mDice | 0.7877 |
10-shot image generation > Semantic Segmentation > Polyp Segmentation | Kvasir-SEG | SSFormer-S + PRN | https://arxiv.org/abs/2211.06560v3 | mIoU | 0.891 |
10-shot image generation > Semantic Segmentation > Polyp Segmentation | PolypGen | YOlO-SAM 2 | https://arxiv.org/abs/2409.09484v1 | Dice | 0.808 |
10-shot image generation > Semantic Segmentation > Polyp Segmentation | PolypGen | YOlO-SAM 2 | https://arxiv.org/abs/2409.09484v1 | mIoU | 0.678 |
10-shot image generation > Semantic Segmentation > Polyp Segmentation | PolypGen | YOlO-SAM 2 | https://arxiv.org/abs/2409.09484v1 | Precision | 0.858 |
10-shot image generation > Semantic Segmentation > Polyp Segmentation | PolypGen | YOlO-SAM 2 | https://arxiv.org/abs/2409.09484v1 | Recall | 0.764 |
10-shot image generation > Semantic Segmentation > Polyp Segmentation | PolypGen | TransNetR | https://arxiv.org/abs/2303.07428v1 | Dice | 0.75 |
10-shot image generation > Semantic Segmentation > Flood extent forecasting | Global Flood forecasting | U-TAE | https://arxiv.org/abs/2107.07933v4 | F1 score | 0.77 |
10-shot image generation > Semantic Segmentation > Flood extent forecasting | Global Flood forecasting | LSTM U-Net | https://openaccess.thecvf.com/content_CVPRW_2019/papers/cv4gc/Rustowicz_Semantic_Segmentation_of_Crop_Type_in_Africa_A_Novel_Dataset_CVPRW_2019_paper | F1 score | 0.76 |
10-shot image generation > Semantic Segmentation > Flood extent forecasting | Global Flood forecasting | 3DConv U-Net | https://openaccess.thecvf.com/content_CVPRW_2019/papers/cv4gc/Rustowicz_Semantic_Segmentation_of_Crop_Type_in_Africa_A_Novel_Dataset_CVPRW_2019_paper | F1 score | 0.76 |
10-shot image generation > Semantic Segmentation > Flood extent forecasting | Global Flood forecasting | MaxViT U-Net | https://arxiv.org/abs/2305.08396v5 | F1 score | 0.75 |
10-shot image generation > Semantic Segmentation > Flood extent forecasting | Global Flood forecasting | logistic regression | https://arxiv.org/abs/2112.02447v2 | F1 score | 0.66 |
10-shot image generation > Semantic Segmentation > Speech Prompted Semantic Segmentation | ADE20K | DenseAV | https://arxiv.org/abs/2406.05629v1 | mAP | 48.7 |
10-shot image generation > Semantic Segmentation > Speech Prompted Semantic Segmentation | ADE20K | DenseAV | https://arxiv.org/abs/2406.05629v1 | mIoU | 36.8 |
10-shot image generation > Semantic Segmentation > Speech Prompted Semantic Segmentation | ADE20K | DAVENet | http://arxiv.org/abs/1804.01452v1 | mAP | 32.2 |
10-shot image generation > Semantic Segmentation > Speech Prompted Semantic Segmentation | ADE20K | DAVENet | http://arxiv.org/abs/1804.01452v1 | mIoU | 26.3 |
10-shot image generation > Semantic Segmentation > Speech Prompted Semantic Segmentation | ADE20K | CAVMAE | https://arxiv.org/abs/2210.07839v4 | mAP | 27.2 |
10-shot image generation > Semantic Segmentation > Speech Prompted Semantic Segmentation | ADE20K | CAVMAE | https://arxiv.org/abs/2210.07839v4 | mIoU | 19.9 |
10-shot image generation > Semantic Segmentation > Speech Prompted Semantic Segmentation | ADE20K | ImageBIND | https://arxiv.org/abs/2305.05665v2 | mAP | 20.2 |
10-shot image generation > Semantic Segmentation > Speech Prompted Semantic Segmentation | ADE20K | ImageBIND | https://arxiv.org/abs/2305.05665v2 | mIoU | 19.7 |
10-shot image generation > Semantic Segmentation > Sound Prompted Semantic Segmentation | ADE20K | DenseAV | https://arxiv.org/abs/2406.05629v1 | mAP | 32.7 |
10-shot image generation > Semantic Segmentation > Sound Prompted Semantic Segmentation | ADE20K | DenseAV | https://arxiv.org/abs/2406.05629v1 | mIoU | 24.7 |
10-shot image generation > Semantic Segmentation > Sound Prompted Semantic Segmentation | ADE20K | CAVMAE | https://arxiv.org/abs/2210.07839v4 | mAP | 26.0 |
10-shot image generation > Semantic Segmentation > Sound Prompted Semantic Segmentation | ADE20K | CAVMAE | https://arxiv.org/abs/2210.07839v4 | mIoU | 17.0 |
10-shot image generation > Semantic Segmentation > Sound Prompted Semantic Segmentation | ADE20K | ImageBIND | https://arxiv.org/abs/2305.05665v2 | mAP | 19.7 |
10-shot image generation > Semantic Segmentation > Sound Prompted Semantic Segmentation | ADE20K | ImageBIND | https://arxiv.org/abs/2305.05665v2 | mIoU | 20.5 |
10-shot image generation > Semantic Segmentation > Sound Prompted Semantic Segmentation | ADE20K | DAVENet | http://arxiv.org/abs/1804.01452v1 | mAP | 16.8 |
10-shot image generation > Semantic Segmentation > Sound Prompted Semantic Segmentation | ADE20K | DAVENet | http://arxiv.org/abs/1804.01452v1 | mIoU | 18.1 |
10-shot image generation > Semantic Segmentation > Text-Line Extraction | DIVA-HisDB | Semantic Seg Preprocessing | https://arxiv.org/abs/1906.11894v2 | Line IoU | 99.42 |
10-shot image generation > Semantic Segmentation > Text-Line Extraction | DIVA-HisDB | Semantic Seg Preprocessing | https://arxiv.org/abs/1906.11894v2 | Pixel IoU | 96.11 |
10-shot image generation > Semantic Segmentation > Hyperspectral Semantic Segmentation | Hyperspectral City | RU-Net | https://arxiv.org/abs/2409.11205v1 | Jaccard (Mean) | 43.33 |
10-shot image generation > Semantic Segmentation > Hyperspectral Semantic Segmentation | Hyperspectral City | RU-Net | https://arxiv.org/abs/2409.11205v1 | Avg. F1 | 53.26 |
10-shot image generation > Semantic Segmentation > Hyperspectral Semantic Segmentation | Hyperspectral City | RU-Net | https://arxiv.org/abs/2409.11205v1 | Accuracy | 87.63 |
10-shot image generation > Semantic Segmentation > Hyperspectral Semantic Segmentation | Hyperspectral City | RU-Net | https://arxiv.org/abs/2409.11205v1 | Average Accuracy | 54.14 |
10-shot image generation > Semantic Segmentation > Hyperspectral Semantic Segmentation | Hyperspectral City | DeepLabV3+ | https://arxiv.org/abs/2409.11205v1 | Jaccard (Mean) | 40.79 |
10-shot image generation > Semantic Segmentation > Hyperspectral Semantic Segmentation | Hyperspectral City | DeepLabV3+ | https://arxiv.org/abs/2409.11205v1 | Avg. F1 | 51.83 |
10-shot image generation > Semantic Segmentation > Hyperspectral Semantic Segmentation | Hyperspectral City | DeepLabV3+ | https://arxiv.org/abs/2409.11205v1 | Accuracy | 86.60 |
10-shot image generation > Semantic Segmentation > Hyperspectral Semantic Segmentation | Hyperspectral City | DeepLabV3+ | https://arxiv.org/abs/2409.11205v1 | Average Accuracy | 53.15 |
10-shot image generation > Semantic Segmentation > Hyperspectral Semantic Segmentation | Hyperspectral City | U-Net | https://arxiv.org/abs/2409.11205v1 | Jaccard (Mean) | 37.73 |
10-shot image generation > Semantic Segmentation > Hyperspectral Semantic Segmentation | Hyperspectral City | U-Net | https://arxiv.org/abs/2409.11205v1 | Avg. F1 | 48.18 |
10-shot image generation > Semantic Segmentation > Hyperspectral Semantic Segmentation | Hyperspectral City | U-Net | https://arxiv.org/abs/2409.11205v1 | Accuracy | 85.25 |
10-shot image generation > Semantic Segmentation > Hyperspectral Semantic Segmentation | Hyperspectral City | U-Net | https://arxiv.org/abs/2409.11205v1 | Average Accuracy | 48.62 |
10-shot image generation > Semantic Segmentation > Hyperspectral Semantic Segmentation | HSI-Drive v2.0 | RU-Net | https://arxiv.org/abs/2409.11205v1 | Accuracy | 96.08 |
10-shot image generation > Semantic Segmentation > Hyperspectral Semantic Segmentation | HSI-Drive v2.0 | RU-Net | https://arxiv.org/abs/2409.11205v1 | Average Accuracy | 79.82 |
10-shot image generation > Semantic Segmentation > Hyperspectral Semantic Segmentation | HSI-Drive v2.0 | RU-Net | https://arxiv.org/abs/2409.11205v1 | Avg. F1 | 82.34 |
10-shot image generation > Semantic Segmentation > Hyperspectral Semantic Segmentation | HSI-Drive v2.0 | RU-Net | https://arxiv.org/abs/2409.11205v1 | Jaccard (Mean) | 72.18 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.