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 ⌀ |
|---|---|---|---|---|---|
Zero-Shot Semantic Segmentation | COCO-Stuff | STRICT | https://arxiv.org/abs/2104.11692v1 | Inductive Setting hIoU | - |
Zero-Shot Semantic Segmentation | COCO-Stuff | SPNet | http://openaccess.thecvf.com/content_CVPR_2019/html/Xian_Semantic_Projection_Network_for_Zero-_and_Few-Label_Semantic_Segmentation_CVPR_2019_paper.html | Transductive Setting hIoU | 30.3 |
Zero-Shot Semantic Segmentation | COCO-Stuff | SPNet | http://openaccess.thecvf.com/content_CVPR_2019/html/Xian_Semantic_Projection_Network_for_Zero-_and_Few-Label_Semantic_Segmentation_CVPR_2019_paper.html | Inductive Setting hIoU | 14.0 |
Zero-Shot Semantic Segmentation | COCO-Stuff | CaGNet | https://arxiv.org/abs/2008.06893v1 | Transductive Setting hIoU | 19.5 |
Zero-Shot Semantic Segmentation | COCO-Stuff | CaGNet | https://arxiv.org/abs/2008.06893v1 | Inductive Setting hIoU | 18.2 |
Zero-Shot Semantic Segmentation | COCO-Stuff | ZS5 | https://arxiv.org/abs/1906.00817v2 | Transductive Setting hIoU | 16.2 |
Zero-Shot Semantic Segmentation | COCO-Stuff | ZS5 | https://arxiv.org/abs/1906.00817v2 | Inductive Setting hIoU | 15.0 |
Zero-Shot Semantic Segmentation | COCO-Stuff | DeOP | https://arxiv.org/abs/2304.01198v2 | Transductive Setting hIoU | - |
Zero-Shot Semantic Segmentation | COCO-Stuff | DeOP | https://arxiv.org/abs/2304.01198v2 | Inductive Setting hIoU | 38.2 |
Zero-Shot Semantic Segmentation | COCO-Stuff | ZegFormer | https://arxiv.org/abs/2112.07910v2 | Transductive Setting hIoU | - |
Zero-Shot Semantic Segmentation | COCO-Stuff | ZegFormer | https://arxiv.org/abs/2112.07910v2 | Inductive Setting hIoU | 33.2 |
Zero-Shot Semantic Segmentation | COCO-Stuff | SIGN | https://arxiv.org/abs/2108.12517v1 | Transductive Setting hIoU | - |
Zero-Shot Semantic Segmentation | COCO-Stuff | SIGN | https://arxiv.org/abs/2108.12517v1 | Inductive Setting hIoU | 20.9 |
Zero-Shot Semantic Segmentation | MESS | CAT-Seg-L | null | Mean IoU | 38.14 |
Zero-Shot Semantic Segmentation | MESS | CAT-Seg-H | null | Mean IoU | 35.66 |
Zero-Shot Semantic Segmentation | MESS | CAT-Seg-B | null | Mean IoU | 33.74 |
Zero-Shot Semantic Segmentation | MESS | SAN-L | null | Mean IoU | 30.06 |
Zero-Shot Semantic Segmentation | MESS | Grounded-SAM-L | null | Mean IoU | 29.05 |
Zero-Shot Semantic Segmentation | MESS | Grounded-SAM-H | null | Mean IoU | 28.78 |
Zero-Shot Semantic Segmentation | MESS | Grounded-SAM-B | null | Mean IoU | 28.52 |
Zero-Shot Semantic Segmentation | MESS | OVSeg-L | null | Mean IoU | 26.94 |
Zero-Shot Semantic Segmentation | MESS | SAN-B | null | Mean IoU | 26.74 |
Zero-Shot Semantic Segmentation | MESS | OpenSeeD-T | null | Mean IoU | 24.33 |
Zero-Shot Semantic Segmentation | MESS | ZSSeg-B | null | Mean IoU | 22.73 |
Zero-Shot Semantic Segmentation | MESS | X-Decoder-T | null | Mean IoU | 19.8 |
Zero-Shot Semantic Segmentation | MESS | ZegFormer-B | null | Mean IoU | 17.57 |
Single-Source Domain Generalization | PACS | Crafting-Shifts(ResNet18) | https://arxiv.org/abs/2409.19774v1 | Accuracy | 70.37 |
Single-Source Domain Generalization | PACS | MCL (ResNet18) | https://arxiv.org/abs/2304.03709v1 | Accuracy | 69.86 |
Single-Source Domain Generalization | PACS | ProRandConv (ResNet18) | https://arxiv.org/abs/2304.00424v1 | Accuracy | 68.88 |
Single-Source Domain Generalization | PACS | CADA (ResNet18) | https://ieeexplore.ieee.org/document/10031006 | Accuracy | 68.41 |
Single-Source Domain Generalization | PACS | ITTA (ResNet18) | https://arxiv.org/abs/2304.04494v2 | Accuracy | 68.4 |
Single-Source Domain Generalization | PACS | XDED (ResNet18) | https://arxiv.org/abs/2211.14058v1 | Accuracy | 66.5 |
Single-Source Domain Generalization | PACS | ABA (ResNet18) | https://arxiv.org/abs/2307.09520v2 | Accuracy | 66.36 |
Single-Source Domain Generalization | PACS | GeoTexAug (ResNet18) | http://openaccess.thecvf.com//content/CVPR2022/html/Liu_Geometric_and_Textural_Augmentation_for_Domain_Gap_Reduction_CVPR_2022_paper.html | Accuracy | 65.0 |
Single-Source Domain Generalization | PACS | SagNet (ResNet18) | https://arxiv.org/abs/1910.11645v4 | Accuracy | 61.9 |
Single-Source Domain Generalization | PACS | SelfReg (ResNet18) | https://arxiv.org/abs/2104.09841v1 | Accuracy | 59.59 |
Single-Source Domain Generalization | Digits-five | Crafting-Shifts(LeNet) | https://arxiv.org/abs/2409.19774v1 | Accuracy | 82.61 |
Single-Source Domain Generalization | Digits-five | ProRandConv (LeNet) | https://arxiv.org/abs/2304.00424v1 | Accuracy | 81.35 |
Single-Source Domain Generalization | Digits-five | CADA (LeNet) | https://ieeexplore.ieee.org/document/10031006 | Accuracy | 80.56 |
Single-Source Domain Generalization | Digits-five | MCL (LeNet) | https://arxiv.org/abs/2304.03709v1 | Accuracy | 78.82 |
Single-Source Domain Generalization | Digits-five | MetaCNN (LeNet) | http://openaccess.thecvf.com//content/CVPR2022/html/Wan_Meta_Convolutional_Neural_Networks_for_Single_Domain_Generalization_CVPR_2022_paper.html | Accuracy | 78.76 |
Single-Source Domain Generalization | Digits-five | ABA (LeNet) | https://arxiv.org/abs/2307.09520v2 | Accuracy | 76.72 |
Single-Source Domain Generalization | Digits-five | L2D (LeNet) | https://arxiv.org/abs/2108.11726v3 | Accuracy | 74.46 |
Single-Source Domain Generalization > Photo to Rest Generalization | PACS | Crafting-Shifts(ResNet18) | https://arxiv.org/abs/2409.19774v1 | Accuracy | 65.85 |
Single-Source Domain Generalization > Photo to Rest Generalization | PACS | ProRandConv (ResNet18) | https://arxiv.org/abs/2304.00424v1 | Accuracy | 62.89 |
Single-Source Domain Generalization > Photo to Rest Generalization | PACS | Crafting-Shifts(AlexNet) | https://arxiv.org/abs/2409.19774v1 | Accuracy | 60.97 |
Single-Source Domain Generalization > Photo to Rest Generalization | PACS | MCL (ResNet18) | https://arxiv.org/abs/2304.03709v1 | Accuracy | 59.6 |
Single-Source Domain Generalization > Photo to Rest Generalization | PACS | ABA (ResNet18) | https://arxiv.org/abs/2307.09520v2 | Accuracy | 59.04 |
Single-Source Domain Generalization > Photo to Rest Generalization | PACS | MetaCNN (AlexNet) | http://openaccess.thecvf.com//content/CVPR2022/html/Wan_Meta_Convolutional_Neural_Networks_for_Single_Domain_Generalization_CVPR_2022_paper.html | Accuracy | 57.17 |
Single-Source Domain Generalization > Photo to Rest Generalization | PACS | CADA (ResNet18) | https://ieeexplore.ieee.org/document/10031006 | Accuracy | 56.65 |
Single-Source Domain Generalization > Photo to Rest Generalization | PACS | PACS (AlexNet) | https://arxiv.org/abs/2108.11726v3 | Accuracy | 55.24 |
Single-Source Domain Generalization > Photo to Rest Generalization | MiniDomainNet | Crafting-Shifts(ResNet18) | https://arxiv.org/abs/2409.19774v1 | Accuracy | 57.35 |
Text-based Person Retrieval with Noisy Correspondence | ICFG-PEDES | RDE | https://arxiv.org/abs/2308.09911v3 | Rank 1 | 66.54 |
Text-based Person Retrieval with Noisy Correspondence | ICFG-PEDES | RDE | https://arxiv.org/abs/2308.09911v3 | Rank-5 | 81.70 |
Text-based Person Retrieval with Noisy Correspondence | ICFG-PEDES | RDE | https://arxiv.org/abs/2308.09911v3 | Rank-10 | 86.70 |
Text-based Person Retrieval with Noisy Correspondence | ICFG-PEDES | RDE | https://arxiv.org/abs/2308.09911v3 | mAP | 39.08 |
Text-based Person Retrieval with Noisy Correspondence | ICFG-PEDES | RDE | https://arxiv.org/abs/2308.09911v3 | mINP | 7.55 |
Text-based Person Retrieval with Noisy Correspondence | ICFG-PEDES | DECL | https://dl.acm.org/doi/abs/10.1145/3503161.3547922 | Rank 1 | 61.95 |
Text-based Person Retrieval with Noisy Correspondence | ICFG-PEDES | DECL | https://dl.acm.org/doi/abs/10.1145/3503161.3547922 | Rank-5 | 78.36 |
Text-based Person Retrieval with Noisy Correspondence | ICFG-PEDES | DECL | https://dl.acm.org/doi/abs/10.1145/3503161.3547922 | Rank-10 | 83.88 |
Text-based Person Retrieval with Noisy Correspondence | ICFG-PEDES | DECL | https://dl.acm.org/doi/abs/10.1145/3503161.3547922 | mAP | 36.08 |
Text-based Person Retrieval with Noisy Correspondence | ICFG-PEDES | DECL | https://dl.acm.org/doi/abs/10.1145/3503161.3547922 | mINP | 6.25 |
Text-based Person Retrieval with Noisy Correspondence | ICFG-PEDES | IRRA | https://arxiv.org/abs/2303.12501v1 | Rank 1 | 60.76 |
Text-based Person Retrieval with Noisy Correspondence | ICFG-PEDES | IRRA | https://arxiv.org/abs/2303.12501v1 | Rank-5 | 78.26 |
Text-based Person Retrieval with Noisy Correspondence | ICFG-PEDES | IRRA | https://arxiv.org/abs/2303.12501v1 | Rank-10 | 84.01 |
Text-based Person Retrieval with Noisy Correspondence | ICFG-PEDES | IRRA | https://arxiv.org/abs/2303.12501v1 | mAP | 35.87 |
Text-based Person Retrieval with Noisy Correspondence | ICFG-PEDES | IRRA | https://arxiv.org/abs/2303.12501v1 | mINP | 6.80 |
Text-based Person Retrieval with Noisy Correspondence | ICFG-PEDES | CLIP-C | https://arxiv.org/abs/2103.00020v1 | Rank 1 | 55.25 |
Text-based Person Retrieval with Noisy Correspondence | ICFG-PEDES | CLIP-C | https://arxiv.org/abs/2103.00020v1 | Rank-5 | 74.76 |
Text-based Person Retrieval with Noisy Correspondence | ICFG-PEDES | CLIP-C | https://arxiv.org/abs/2103.00020v1 | Rank-10 | 81.32 |
Text-based Person Retrieval with Noisy Correspondence | ICFG-PEDES | CLIP-C | https://arxiv.org/abs/2103.00020v1 | mAP | 31.09 |
Text-based Person Retrieval with Noisy Correspondence | ICFG-PEDES | CLIP-C | https://arxiv.org/abs/2103.00020v1 | mINP | 4.94 |
Text-based Person Retrieval with Noisy Correspondence | ICFG-PEDES | IVT | https://arxiv.org/abs/2208.08608v2 | Rank 1 | 50.21 |
Text-based Person Retrieval with Noisy Correspondence | ICFG-PEDES | IVT | https://arxiv.org/abs/2208.08608v2 | Rank-5 | 69.14 |
Text-based Person Retrieval with Noisy Correspondence | ICFG-PEDES | IVT | https://arxiv.org/abs/2208.08608v2 | Rank-10 | 76.18 |
Text-based Person Retrieval with Noisy Correspondence | ICFG-PEDES | IVT | https://arxiv.org/abs/2208.08608v2 | mAP | 34.72 |
Text-based Person Retrieval with Noisy Correspondence | ICFG-PEDES | IVT | https://arxiv.org/abs/2208.08608v2 | mINP | 8.77 |
Text-based Person Retrieval with Noisy Correspondence | ICFG-PEDES | SSAN | https://arxiv.org/abs/2107.12666v2 | Rank 1 | 40.57 |
Text-based Person Retrieval with Noisy Correspondence | ICFG-PEDES | SSAN | https://arxiv.org/abs/2107.12666v2 | Rank-5 | 62.58 |
Text-based Person Retrieval with Noisy Correspondence | ICFG-PEDES | SSAN | https://arxiv.org/abs/2107.12666v2 | Rank-10 | 71.53 |
Text-based Person Retrieval with Noisy Correspondence | ICFG-PEDES | SSAN | https://arxiv.org/abs/2107.12666v2 | mAP | 20.93 |
Text-based Person Retrieval with Noisy Correspondence | ICFG-PEDES | SSAN | https://arxiv.org/abs/2107.12666v2 | mINP | 2.22 |
Text-based Person Retrieval with Noisy Correspondence | RSTPReid | RDE | https://arxiv.org/abs/2308.09911v3 | Rank 1 | 64.45 |
Text-based Person Retrieval with Noisy Correspondence | RSTPReid | RDE | https://arxiv.org/abs/2308.09911v3 | Rank 10 | 90.00 |
Text-based Person Retrieval with Noisy Correspondence | RSTPReid | RDE | https://arxiv.org/abs/2308.09911v3 | Rank 5 | 83.50 |
Text-based Person Retrieval with Noisy Correspondence | RSTPReid | RDE | https://arxiv.org/abs/2308.09911v3 | mAP | 49.78 |
Text-based Person Retrieval with Noisy Correspondence | RSTPReid | RDE | https://arxiv.org/abs/2308.09911v3 | mINP | 27.43 |
Text-based Person Retrieval with Noisy Correspondence | RSTPReid | DECL | https://dl.acm.org/doi/abs/10.1145/3503161.3547922 | Rank 1 | 61.75 |
Text-based Person Retrieval with Noisy Correspondence | RSTPReid | DECL | https://dl.acm.org/doi/abs/10.1145/3503161.3547922 | Rank 10 | 86.90 |
Text-based Person Retrieval with Noisy Correspondence | RSTPReid | DECL | https://dl.acm.org/doi/abs/10.1145/3503161.3547922 | Rank 5 | 80.70 |
Text-based Person Retrieval with Noisy Correspondence | RSTPReid | DECL | https://dl.acm.org/doi/abs/10.1145/3503161.3547922 | mAP | 47.70 |
Text-based Person Retrieval with Noisy Correspondence | RSTPReid | DECL | https://dl.acm.org/doi/abs/10.1145/3503161.3547922 | mINP | 26.07 |
Text-based Person Retrieval with Noisy Correspondence | RSTPReid | IRRA | https://arxiv.org/abs/2303.12501v1 | Rank 1 | 58.75 |
Text-based Person Retrieval with Noisy Correspondence | RSTPReid | IRRA | https://arxiv.org/abs/2303.12501v1 | Rank 10 | 88.25 |
Text-based Person Retrieval with Noisy Correspondence | RSTPReid | IRRA | https://arxiv.org/abs/2303.12501v1 | Rank 5 | 81.90 |
Text-based Person Retrieval with Noisy Correspondence | RSTPReid | IRRA | https://arxiv.org/abs/2303.12501v1 | mAP | 46.38 |
Text-based Person Retrieval with Noisy Correspondence | RSTPReid | IRRA | https://arxiv.org/abs/2303.12501v1 | mINP | 24.78 |
Text-based Person Retrieval with Noisy Correspondence | RSTPReid | CLIP-C | https://arxiv.org/abs/2103.00020v1 | Rank 1 | 54.45 |
Text-based Person Retrieval with Noisy Correspondence | RSTPReid | CLIP-C | https://arxiv.org/abs/2103.00020v1 | Rank 10 | 86.70 |
Text-based Person Retrieval with Noisy Correspondence | RSTPReid | CLIP-C | https://arxiv.org/abs/2103.00020v1 | Rank 5 | 77.80 |
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