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 ⌀ |
|---|---|---|---|---|---|
Scene Text Recognition | ICDAR2013 | STAR-Net | http://www.bmva.org/bmvc/2016/papers/paper043/paper043.pdf | Accuracy | 89.1 |
Scene Text Recognition | ICDAR2013 | RARE | http://arxiv.org/abs/1603.03915v2 | Accuracy | 88.6 |
Scene Text Recognition | ICDAR2013 | CRNN | http://arxiv.org/abs/1507.05717v1 | Accuracy | 86.7 |
Scene Text Recognition | ICDAR2013 | CHAR | http://arxiv.org/abs/1406.2227v4 | Accuracy | 79.5 |
Sketch Recognition > Image to sketch recognition | MiniDomainNet | Crafting-Shifts(ResNet18) | https://arxiv.org/abs/2409.19774v1 | Accuracy | 57.51 |
Sketch Recognition > Image to sketch recognition | Im4Sketch | rBTE (ResNet101) | https://arxiv.org/abs/2202.13164v2 | Accuracy | 11.3 |
Sketch Recognition > Image to sketch recognition | Im4Sketch | ResNet101 | https://arxiv.org/abs/2202.13164v2 | Accuracy | 5.3 |
Sketch Recognition > Image to sketch recognition | PACS | Crafting-Shifts(ResNet18) | https://arxiv.org/abs/2409.19774v1 | Accuracy | 74.13 |
Sketch Recognition > Image to sketch recognition | PACS | rBTE (ResNet18) | https://arxiv.org/abs/2202.13164v2 | Accuracy | 70.6 |
Sketch Recognition > Image to sketch recognition | PACS | Crafting-Shifts(AlexNet) | https://arxiv.org/abs/2409.19774v1 | Accuracy | 68.5 |
Sketch Recognition > Image to sketch recognition | PACS | ITTA (ResNet18) | https://arxiv.org/abs/2304.04494v2 | Accuracy | 63.8 |
Sketch Recognition > Image to sketch recognition | PACS | XDED (ResNet18) | https://arxiv.org/abs/2211.14058v1 | Accuracy | 51.5 |
Sketch Recognition > Image to sketch recognition | PACS | SagNet (ResNet18) | https://arxiv.org/abs/1910.11645v4 | Accuracy | 40.7 |
Sketch Recognition > Image to sketch recognition | PACS | SelfReg (ResNet18) | https://arxiv.org/abs/2104.09841v1 | Accuracy | 33.71 |
Sketch Recognition > Image to sketch recognition | Sketchy | rBTE (ResNet101) | https://arxiv.org/abs/2202.13164v2 | Accuracy | 57.2 |
Sketch Recognition > Image to sketch recognition | Sketchy | ResNet101 | https://arxiv.org/abs/2202.13164v2 | Accuracy | 11.4 |
Video Classification | Multimodal PISA | Video | https://arxiv.org/abs/2101.04884v2 | Accuracy (%) | 73.95 |
Video Classification | MoB | VTN | https://arxiv.org/abs/2305.15551v1 | Accuracy | 77.85 |
Video Classification | MoB | I3D | https://arxiv.org/abs/2305.15551v1 | Accuracy | 72.11 |
Video Classification | MoB | ConvLSTM | https://arxiv.org/abs/2305.15551v1 | Accuracy | 69.71 |
Video Classification | Hockey Fight Detection Dataset | CNN+LSTM | https://ieeexplore.ieee.org/abstract/document/8852616 | 1:1 Accuracy | 98% |
Video Classification | Hockey Fight Detection Dataset | Structured Keypoint Pooling | https://arxiv.org/abs/2303.15270v1 | Accuracy | 99.5 |
Video Classification | Kinetics | Multigrid | https://arxiv.org/abs/1912.00998v2 | Top-1 | 77.6 |
Video Classification | Charades | Multigrid | https://arxiv.org/abs/1912.00998v2 | mAP | 38.2 |
Video Classification | YouTube-8M | DCGN (self-attention graph pooling) | https://arxiv.org/abs/1906.00377v1 | Hit@1 | 87.7 |
Video Classification | YouTube-8M | Hierarchical LSTM with MoE | http://arxiv.org/abs/1902.10640v1 | Hit@1 | 86.8 |
Video Classification | YouTube-8M | Hierarchical LSTM with MoE | http://arxiv.org/abs/1902.10640v1 | Global Average Precision | 81.1 |
Video Classification | YouTube-8M | Hierarchical LSTM with MoE | http://arxiv.org/abs/1902.10640v1 | mAP | 41.4 |
Video Classification | YouTube-8M | Mixture-of-2-Experts | http://arxiv.org/abs/1609.08675v1 | Hit@1 | 70.1 |
Video Classification | YouTube-8M | Mixture-of-2-Experts | http://arxiv.org/abs/1609.08675v1 | PERR | 29.1 |
Video Classification | YouTube-8M | Mixture-of-2-Experts | http://arxiv.org/abs/1609.08675v1 | Hit@5 | 84.8 |
Video Classification | Home Action Genome | Cooperative Ours (3rd-person) | https://arxiv.org/abs/2105.05226v1 | Accuracy (%) | 24.7 |
Video Classification | Breakfast | HERMES | https://arxiv.org/abs/2408.17443v3 | Accuracy (%) | 95.2 |
Video Classification | Breakfast | MA-LMM | https://arxiv.org/abs/2404.05726v2 | Accuracy (%) | 93.0 |
Video Classification | Breakfast | S5 | https://arxiv.org/abs/2303.14526v1 | Accuracy (%) | 90.7 |
Video Classification | Breakfast | TranS4mer | https://arxiv.org/abs/2212.14427v2 | Accuracy (%) | 90.27 |
Video Classification | Breakfast | D-Sprv. | https://arxiv.org/abs/2201.10990v3 | Accuracy (%) | 89.9 |
Video Classification | Breakfast | ViS4mer | https://arxiv.org/abs/2204.01692v3 | Accuracy (%) | 88.2 |
Video Classification | Breakfast | GHRM | http://openaccess.thecvf.com//content/CVPR2021/html/Zhou_Graph-Based_High-Order_Relation_Modeling_for_Long-Term_Action_Recognition_CVPR_2021_paper.html | Accuracy (%) | 75.5 |
Video Classification | Breakfast | Timeception | http://arxiv.org/abs/1812.01289v2 | Accuracy (%) | 71.3 |
Video Classification | Breakfast | VideoGraph | https://arxiv.org/abs/1905.05143v2 | Accuracy (%) | 69.5 |
Video Classification | COIN | HERMES | https://arxiv.org/abs/2408.17443v3 | Accuracy (%) | 93.5 |
Video Classification | COIN | MA-LMM | https://arxiv.org/abs/2404.05726v2 | Accuracy (%) | 93.2 |
Video Classification | COIN | S5 | https://arxiv.org/abs/2303.14526v1 | Accuracy (%) | 90.8 |
Video Classification | COIN | D-Sprv. | https://arxiv.org/abs/2201.10990v3 | Accuracy (%) | 90.0 |
Video Classification | COIN | TranS4mer | https://arxiv.org/abs/2212.14427v2 | Accuracy (%) | 89.3 |
Video Classification | COIN | ViS4mer | https://arxiv.org/abs/2204.01692v3 | Accuracy (%) | 88.4 |
Video Classification | COIN | TSN | http://arxiv.org/abs/1705.02953v1 | Accuracy (%) | 73.4 |
Video Classification | SRI-APPROVE Fine-Grained Video Classification | Multi-Label Prototypes Contrastive Learning | http://openaccess.thecvf.com//content/CVPR2023/html/Gupta_Class_Prototypes_Based_Contrastive_Learning_for_Classifying_Multi-Label_and_Fine-Grained_CVPR_2023_paper.html | AUPR | 88.4 |
Video Classification | Something-Something V1 | MSNet-R50En (ours) | https://arxiv.org/abs/2007.09933v1 | Top-5 Accuracy | 84 |
Video Classification | Something-Something V2 | MSNet-R50En (ours) | https://arxiv.org/abs/2007.09933v1 | Top-5 Accuracy | 91 |
Video Classification > Student Engagement Level Detection (Four Class Video Classification) | DAiSEE | Hybrid EfficientNet B7 + Bi-LSTM | https://ieeexplore.ieee.org/document/9893134 | 4-class test accuracy | 67.48% |
Video Classification > Student Engagement Level Detection (Four Class Video Classification) | DAiSEE | Hybrid EfficientNet B7 + LSTM | https://ieeexplore.ieee.org/document/9893134 | 4-class test accuracy | 66.39% |
Video Classification > Student Engagement Level Detection (Four Class Video Classification) | DAiSEE | Hybrid EfficientNet B7 + TCN | https://ieeexplore.ieee.org/document/9893134 | 4-class test accuracy | 64.67% |
Procedure Step Recognition | IndustReal | B3 | https://arxiv.org/abs/2310.17323v1 | F1 | 0.883 |
Procedure Step Recognition | IndustReal | B3 | https://arxiv.org/abs/2310.17323v1 | POS | 0.797 |
Procedure Step Recognition | IndustReal | B3 | https://arxiv.org/abs/2310.17323v1 | Delay (seconds) | 22.4 |
Procedure Step Recognition | IndustReal | B3 - Synthetic Only | https://arxiv.org/abs/2310.17323v1 | F1 | 0.597 |
Procedure Step Recognition | IndustReal | B3 - Synthetic Only | https://arxiv.org/abs/2310.17323v1 | POS | 0.734 |
Procedure Step Recognition | IndustReal | B3 - Synthetic Only | https://arxiv.org/abs/2310.17323v1 | Delay (seconds) | 49.5 |
Zero-Shot Semantic Segmentation | PASCAL VOC | OTSeg+ | https://arxiv.org/abs/2403.14183v2 | Transductive Setting hIoU | 94.4 |
Zero-Shot Semantic Segmentation | PASCAL VOC | OTSeg+ | https://arxiv.org/abs/2403.14183v2 | Inductive Setting hIoU | 87.4 |
Zero-Shot Semantic Segmentation | PASCAL VOC | OTSeg | https://arxiv.org/abs/2403.14183v2 | Transductive Setting hIoU | 94.2 |
Zero-Shot Semantic Segmentation | PASCAL VOC | OTSeg | https://arxiv.org/abs/2403.14183v2 | Inductive Setting hIoU | 84.5 |
Zero-Shot Semantic Segmentation | PASCAL VOC | CLIP-RC | http://openaccess.thecvf.com//content/CVPR2024/html/Zhang_Exploring_Regional_Clues_in_CLIP_for_Zero-Shot_Semantic_Segmentation_CVPR_2024_paper.html | Transductive Setting hIoU | 93.0 |
Zero-Shot Semantic Segmentation | PASCAL VOC | CLIP-RC | http://openaccess.thecvf.com//content/CVPR2024/html/Zhang_Exploring_Regional_Clues_in_CLIP_for_Zero-Shot_Semantic_Segmentation_CVPR_2024_paper.html | Inductive Setting hIoU | 88.4 |
Zero-Shot Semantic Segmentation | PASCAL VOC | ZegCLIP | https://arxiv.org/abs/2212.03588v3 | Transductive Setting hIoU | 91.1 |
Zero-Shot Semantic Segmentation | PASCAL VOC | ZegCLIP | https://arxiv.org/abs/2212.03588v3 | Inductive Setting hIoU | 84.3 |
Zero-Shot Semantic Segmentation | PASCAL VOC | MaskCLIP+ | https://arxiv.org/abs/2112.01071v2 | Transductive Setting hIoU | 87.4 |
Zero-Shot Semantic Segmentation | PASCAL VOC | MaskCLIP+ | https://arxiv.org/abs/2112.01071v2 | Inductive Setting hIoU | - |
Zero-Shot Semantic Segmentation | PASCAL VOC | FreeSeg | https://arxiv.org/abs/2209.13558v2 | Transductive Setting hIoU | 86.9 |
Zero-Shot Semantic Segmentation | PASCAL VOC | FreeSeg | https://arxiv.org/abs/2209.13558v2 | Inductive Setting hIoU | - |
Zero-Shot Semantic Segmentation | PASCAL VOC | zsseg | https://arxiv.org/abs/2112.14757v2 | Transductive Setting hIoU | 79.3 |
Zero-Shot Semantic Segmentation | PASCAL VOC | zsseg | https://arxiv.org/abs/2112.14757v2 | Inductive Setting hIoU | 77.5 |
Zero-Shot Semantic Segmentation | PASCAL VOC | STRICT | https://arxiv.org/abs/2104.11692v1 | Transductive Setting hIoU | 49.8 |
Zero-Shot Semantic Segmentation | PASCAL VOC | CaGNet | https://arxiv.org/abs/2008.06893v1 | Transductive Setting hIoU | 43.7 |
Zero-Shot Semantic Segmentation | PASCAL VOC | 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 | 38.8 |
Zero-Shot Semantic Segmentation | PASCAL VOC | ZS5 | https://arxiv.org/abs/1906.00817v2 | Transductive Setting hIoU | 33.8 |
Zero-Shot Semantic Segmentation | PASCAL VOC | DeOp | https://arxiv.org/abs/2304.01198v2 | Transductive Setting hIoU | - |
Zero-Shot Semantic Segmentation | PASCAL VOC | DeOp | https://arxiv.org/abs/2304.01198v2 | Inductive Setting hIoU | 80.8 |
Zero-Shot Semantic Segmentation | PASCAL VOC | ZegFormer | https://arxiv.org/abs/2112.07910v2 | Transductive Setting hIoU | - |
Zero-Shot Semantic Segmentation | PASCAL VOC | ZegFormer | https://arxiv.org/abs/2112.07910v2 | Inductive Setting hIoU | 73.3 |
Zero-Shot Semantic Segmentation | ADE20K-847 | MAFT | null | unseen mIoU | 8.7 |
Zero-Shot Semantic Segmentation | COCO-Stuff | OTSeg+ | https://arxiv.org/abs/2403.14183v2 | Transductive Setting hIoU | 49.8 |
Zero-Shot Semantic Segmentation | COCO-Stuff | OTSeg+ | https://arxiv.org/abs/2403.14183v2 | Inductive Setting hIoU | 41.5 |
Zero-Shot Semantic Segmentation | COCO-Stuff | CLIP-RC | http://openaccess.thecvf.com//content/CVPR2024/html/Zhang_Exploring_Regional_Clues_in_CLIP_for_Zero-Shot_Semantic_Segmentation_CVPR_2024_paper.html | Transductive Setting hIoU | 49.7 |
Zero-Shot Semantic Segmentation | COCO-Stuff | CLIP-RC | http://openaccess.thecvf.com//content/CVPR2024/html/Zhang_Exploring_Regional_Clues_in_CLIP_for_Zero-Shot_Semantic_Segmentation_CVPR_2024_paper.html | Inductive Setting hIoU | 41.2 |
Zero-Shot Semantic Segmentation | COCO-Stuff | OTSeg | https://arxiv.org/abs/2403.14183v2 | Transductive Setting hIoU | 49.5 |
Zero-Shot Semantic Segmentation | COCO-Stuff | OTSeg | https://arxiv.org/abs/2403.14183v2 | Inductive Setting hIoU | 41.4 |
Zero-Shot Semantic Segmentation | COCO-Stuff | ZegCLIP | https://arxiv.org/abs/2212.03588v3 | Transductive Setting hIoU | 48.5 |
Zero-Shot Semantic Segmentation | COCO-Stuff | ZegCLIP | https://arxiv.org/abs/2212.03588v3 | Inductive Setting hIoU | 40.8 |
Zero-Shot Semantic Segmentation | COCO-Stuff | MVP-SEG+ | https://arxiv.org/abs/2304.06957v1 | Transductive Setting hIoU | 45.5 |
Zero-Shot Semantic Segmentation | COCO-Stuff | MVP-SEG+ | https://arxiv.org/abs/2304.06957v1 | Inductive Setting hIoU | - |
Zero-Shot Semantic Segmentation | COCO-Stuff | FreeSeg | https://arxiv.org/abs/2209.13558v2 | Transductive Setting hIoU | 45.3 |
Zero-Shot Semantic Segmentation | COCO-Stuff | FreeSeg | https://arxiv.org/abs/2209.13558v2 | Inductive Setting hIoU | - |
Zero-Shot Semantic Segmentation | COCO-Stuff | MaskCLIP+ | https://arxiv.org/abs/2112.01071v2 | Transductive Setting hIoU | 45.0 |
Zero-Shot Semantic Segmentation | COCO-Stuff | MaskCLIP+ | https://arxiv.org/abs/2112.01071v2 | Inductive Setting hIoU | - |
Zero-Shot Semantic Segmentation | COCO-Stuff | zsseg | https://arxiv.org/abs/2112.14757v2 | Transductive Setting hIoU | 41.5 |
Zero-Shot Semantic Segmentation | COCO-Stuff | zsseg | https://arxiv.org/abs/2112.14757v2 | Inductive Setting hIoU | 36.3 |
Zero-Shot Semantic Segmentation | COCO-Stuff | STRICT | https://arxiv.org/abs/2104.11692v1 | Transductive Setting hIoU | 34.8 |
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