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 ⌀ |
|---|---|---|---|---|---|
16k > Object Detection > Object Detection In Aerial Images | DOTA | ViT-B + RVSA-ORCN | https://arxiv.org/abs/2208.03987v4 | mAP | 81.01% |
16k > Object Detection > Object Detection In Aerial Images | DOTA | Oriented RCNN | https://arxiv.org/abs/2108.05699v1 | mAP | 80.87% |
16k > Object Detection > Object Detection In Aerial Images | DOTA | IMP+MTP(InternImage-XL) | https://arxiv.org/abs/2403.13430v2 | mAP | 80.77% |
16k > Object Detection > Object Detection In Aerial Images | DOTA | PP-YOLOE-R-x | https://arxiv.org/abs/2211.02386v1 | mAP | 80.73% |
16k > Object Detection > Object Detection In Aerial Images | DOTA | MAE+MTP(ViT-B+RVSA) | https://arxiv.org/abs/2403.13430v2 | mAP | 80.67% |
16k > Object Detection > Object Detection In Aerial Images | DOTA | KLD+R3Det | https://arxiv.org/abs/2106.01883v5 | mAP | 80.63% |
16k > Object Detection > Object Detection In Aerial Images | DOTA | DEA-Net | https://arxiv.org/abs/2112.06701v1 | mAP | 80.37% |
16k > Object Detection > Object Detection In Aerial Images | DOTA | GWD+R3Det | https://arxiv.org/abs/2101.11952v4 | mAP | 80.23% |
16k > Object Detection > Object Detection In Aerial Images | DOTA | ReDet | https://arxiv.org/abs/2103.07733v1 | mAP | 80.10% |
16k > Object Detection > Object Detection In Aerial Images | DOTA | LEGNet-S | https://arxiv.org/abs/2503.14012v1 | mAP | 80.03 |
16k > Object Detection > Object Detection In Aerial Images | DOTA | PP-YOLOE-R-l | https://arxiv.org/abs/2211.02386v1 | mAP | 80.02% |
16k > Object Detection > Object Detection In Aerial Images | DOTA | PP-YOLOE-R-m | https://arxiv.org/abs/2211.02386v1 | mAP | 79.71% |
16k > Object Detection > Object Detection In Aerial Images | DOTA | RBox | https://arxiv.org/abs/2203.15221v2 | mAP | 79.59% |
16k > Object Detection > Object Detection In Aerial Images | DOTA | S2A-Net | https://arxiv.org/abs/2008.09397v3 | mAP | 79.42% |
16k > Object Detection > Object Detection In Aerial Images | DOTA | PP-YOLOE-R-s | https://arxiv.org/abs/2211.02386v1 | mAP | 79.42% |
16k > Object Detection > Object Detection In Aerial Images | DOTA | LWGANet L2 | https://arxiv.org/abs/2501.10040v1 | mAP | 78.64 |
16k > Object Detection > Object Detection In Aerial Images | DOTA | DecoupleNet D2 | https://ieeexplore.ieee.org/document/10685518 | mAP | 78.04% |
16k > Object Detection > Object Detection In Aerial Images | DOTA | RDD | https://www.mdpi.com/2072-4292/12/19/3262 | mAP | 77.75% |
16k > Object Detection > Object Detection In Aerial Images | DOTA | RSP-ViTAEv2-S-FPN-ORCN | https://arxiv.org/abs/2204.02825v4 | mAP | 77.72% |
16k > Object Detection > Object Detection In Aerial Images | DOTA | Oriented RepPoints | https://arxiv.org/abs/2105.11111v4 | mAP | 77.63% |
16k > Object Detection > Object Detection In Aerial Images | DOTA | RIDet | https://arxiv.org/abs/2103.11636v3 | mAP | 77.62% |
16k > Object Detection > Object Detection In Aerial Images | DOTA | IMP-ViTAEv2-S-FPN-ORCN | https://arxiv.org/abs/2204.02825v4 | mAP | 77.38% |
16k > Object Detection > Object Detection In Aerial Images | DOTA | DCL | https://arxiv.org/abs/2011.09670v4 | mAP | 77.37% |
16k > Object Detection > Object Detection In Aerial Images | DOTA | EAutoDet | https://arxiv.org/abs/2203.10747v1 | mAP | 77.05% |
16k > Object Detection > Object Detection In Aerial Images | DOTA | DLA+S2A-Net | https://arxiv.org/abs/2012.04150v2 | mAP | 76.95% |
16k > Object Detection > Object Detection In Aerial Images | DOTA | GGHL | https://arxiv.org/abs/2109.12848v4 | mAP | 76.95% |
16k > Object Detection > Object Detection In Aerial Images | DOTA | SCRDet++ | https://arxiv.org/abs/2004.13316v2 | mAP | 76.81% |
16k > Object Detection > Object Detection In Aerial Images | DOTA | CFA | http://openaccess.thecvf.com//content/CVPR2021/html/Guo_Beyond_Bounding-Box_Convex-Hull_Feature_Adaptation_for_Oriented_and_Densely_Packed_CVPR_2021_paper.html | mAP | 76.67% |
16k > Object Detection > Object Detection In Aerial Images | DOTA | PolarDet | https://arxiv.org/abs/2010.08720v1 | mAP | 76.64% |
16k > Object Detection > Object Detection In Aerial Images | DOTA | RSP-ResNet-50-FPN-ORCN | https://arxiv.org/abs/2204.02825v4 | mAP | 76.50% |
16k > Object Detection > Object Detection In Aerial Images | DOTA | CSL | https://arxiv.org/abs/2003.05597v4 | mAP | 76.17% |
16k > Object Detection > Object Detection In Aerial Images | DOTA | RSP-Swin-T-FPN-ORCN | https://arxiv.org/abs/2204.02825v4 | mAP | 76.12% |
16k > Object Detection > Object Detection In Aerial Images | DOTA | APE | https://arxiv.org/abs/1906.09447v1 | mAP | 75.75 |
16k > Object Detection > Object Detection In Aerial Images | DOTA | BBAVectors | https://arxiv.org/abs/2008.07043v2 | mAP | 75.36% |
16k > Object Detection > Object Detection In Aerial Images | DOTA | TricubeNet | https://arxiv.org/abs/2104.11435v2 | mAP | 75.26% |
16k > Object Detection > Object Detection In Aerial Images | DOTA | Gliding Vertex | https://arxiv.org/abs/1911.09358v2 | mAP | 75.02% |
16k > Object Detection > Object Detection In Aerial Images | DOTA | RSDet | https://arxiv.org/abs/1911.08299v3 | mAP | 74.10% |
16k > Object Detection > Object Detection In Aerial Images | DOTA | CFC-NET | https://arxiv.org/abs/2101.06849v2 | mAP | 73.50% |
16k > Object Detection > Object Detection In Aerial Images | DOTA | DRN | https://arxiv.org/abs/2005.09973v2 | mAP | 73.23% |
16k > Object Detection > Object Detection In Aerial Images | DOTA | O2-DNet | https://arxiv.org/abs/1912.10694v3 | mAP | 72.8% |
16k > Object Detection > Object Detection In Aerial Images | DOTA | SCRDet | https://arxiv.org/abs/1811.07126v4 | mAP | 72.61% |
16k > Object Detection > Object Detection In Aerial Images | DOTA | RoI Transformer | http://arxiv.org/abs/1812.00155v1 | mAP | 69.56% |
16k > Object Detection > Object Detection In Aerial Images | DOTA | ICN | http://arxiv.org/abs/1807.02700v3 | mAP | 68.16% |
16k > Object Detection > Object Detection In Aerial Images | DOTA | Axis Learning | https://www.mdpi.com/2072-4292/12/6/908 | mAP | 65.98% |
16k > Object Detection > Object Detection In Aerial Images | DOTA | PIoU | https://arxiv.org/abs/2007.09584v1 | mAP | 60.5% |
16k > Object Detection > Object Detection In Aerial Images | DOTA | FR-O (DOTA) | https://arxiv.org/abs/1711.10398v3 | mAP | 52.93% |
16k > Object Detection > Object Detection In Aerial Images | VME & CDSI | DINO | https://arxiv.org/abs/2505.22353v1 | mAP50 | 86.5 |
16k > Object Detection > Object Detection In Aerial Images | DIOR | MAE+MTP(ViT-L+RVSA) | https://arxiv.org/abs/2403.13430v2 | AP50 | 81.1 |
16k > Object Detection > Object Detection In Aerial Images | DIOR | MAE+MTP(ViT-B+RVSA) | https://arxiv.org/abs/2403.13430v2 | AP50 | 79.4 |
16k > Object Detection > Object Detection In Aerial Images | DIOR | IMP+MTP(InternImage-XL) | https://arxiv.org/abs/2403.13430v2 | AP50 | 78.0 |
16k > Object Detection > Object Detection In Aerial Images | DIOR | SelectiveMAE+ViT-B | https://arxiv.org/abs/2406.11933v4 | AP50 | 77.80 |
16k > Object Detection > Object Detection In Aerial Images | HRSC2016 | CDLA-HOP | https://arxiv.org/abs/2407.03205v1 | mAP-07 | 90.89 |
16k > Object Detection > Object Detection In Aerial Images | HRSC2016 | CDLA-HOP | https://arxiv.org/abs/2407.03205v1 | mAP-12 | 98.77 |
16k > Object Detection > Object Detection In Aerial Images | HRSC2016 | STD+ViT-B | https://arxiv.org/abs/2308.10561v2 | mAP-07 | 90.67 |
16k > Object Detection > Object Detection In Aerial Images | HRSC2016 | STD+ViT-B | https://arxiv.org/abs/2308.10561v2 | mAP-12 | 98.55 |
16k > Object Detection > Object Detection In Aerial Images | HRSC2016 | LSKNet-S | https://arxiv.org/abs/2303.09030v2 | mAP-07 | 90.65 |
16k > Object Detection > Object Detection In Aerial Images | HRSC2016 | LSKNet-S | https://arxiv.org/abs/2303.09030v2 | mAP-12 | 98.46 |
16k > Object Detection > Object Detection In Aerial Images | HRSC2016 | Strip R-CNN | https://arxiv.org/abs/2501.03775v3 | mAP-07 | 90.6 |
16k > Object Detection > Object Detection In Aerial Images | HRSC2016 | Strip R-CNN | https://arxiv.org/abs/2501.03775v3 | mAP-12 | 98.70 |
16k > Object Detection > Object Detection In Aerial Images | HRSC2016 | RTMDet-R-tiny | https://arxiv.org/abs/2212.07784v2 | mAP-07 | 90.6 |
16k > Object Detection > Object Detection In Aerial Images | HRSC2016 | RTMDet-R-tiny | https://arxiv.org/abs/2212.07784v2 | mAP-12 | 97.10 |
16k > Object Detection > Object Detection In Aerial Images | HRSC2016 | RSP-ViTAEv2-S-FPN-ORCN | https://arxiv.org/abs/2204.02825v4 | mAP-07 | 90.4 |
16k > Object Detection > Object Detection In Aerial Images | HRSC2016 | IMP-ViTAEv2-S-FPN-ORCN | https://arxiv.org/abs/2204.02825v4 | mAP-07 | 90.4 |
16k > Object Detection > Object Detection In Aerial Images | HRSC2016 | RSP-ResNet-50-FPN-ORCN | https://arxiv.org/abs/2204.02825v4 | mAP-07 | 90.3 |
16k > Object Detection > Object Detection In Aerial Images | HRSC2016 | RSP-Swin-T-FPN-ORCN | https://arxiv.org/abs/2204.02825v4 | mAP-07 | 90.0 |
16k > Object Detection > Object Detection In Aerial Images | FAIR1M-2.0 | MAE+MTP(ViT-L+RVSA) | https://arxiv.org/abs/2403.13430v2 | mAP | 53.00 |
16k > Object Detection > Object Detection In Aerial Images | FAIR1M-2.0 | MAE+MTP(ViT-B+RVSA) | https://arxiv.org/abs/2403.13430v2 | mAP | 51.92 |
16k > Object Detection > Object Detection In Aerial Images | FAIR1M-2.0 | IMP+MTP(InternImage-XL) | https://arxiv.org/abs/2403.13430v2 | mAP | 50.93 |
16k > Object Detection > Object Detection In Aerial Images | xView | MAE+MTP(ViT-L+RVSA) | https://arxiv.org/abs/2403.13430v2 | AP50 | 19.4 |
16k > Object Detection > Object Detection In Aerial Images | xView | IMP+MTP(InternImage-XL) | https://arxiv.org/abs/2403.13430v2 | AP50 | 18.2 |
16k > Object Detection > Object Detection In Aerial Images | xView | MAE+MTP(ViT-B+RVSA) | https://arxiv.org/abs/2403.13430v2 | AP50 | 16.4 |
16k > Object Detection > Object Detection In Aerial Images | DOTA 1.0 | RTMDet-R-l | https://arxiv.org/abs/2212.07784v2 | mAP | 81.33% |
16k > Object Detection > Object Detection In Aerial Images | DOTA 1.0 | RTMDet-R-l (single scale) | https://arxiv.org/abs/2212.07784v2 | mAP | 80.16% |
16k > Object Detection > Object Detection In Aerial Images | DIOR-R | MAE+MTP(ViT-L+RVSA) | https://arxiv.org/abs/2403.13430v2 | mAP | 74.54 |
16k > Object Detection > Object Detection In Aerial Images | DIOR-R | ViT-G12X4 | https://arxiv.org/abs/2304.05215v4 | mAP | 73.60 |
16k > Object Detection > Object Detection In Aerial Images | DIOR-R | IMP+MTP(InternImage-XL) | https://arxiv.org/abs/2403.13430v2 | mAP | 72.17 |
16k > Object Detection > Object Detection In Aerial Images | DIOR-R | MAE+MTP(ViT-B+RVSA) | https://arxiv.org/abs/2403.13430v2 | mAP | 71.29 |
16k > Object Detection > Object Detection In Aerial Images | DIOR-R | ViTAE-B + RVSA-ORCN | https://arxiv.org/abs/2208.03987v4 | mAP | 71.05 |
16k > Object Detection > Object Detection In Aerial Images | DIOR-R | ViT-B + RVSA-ORCN | https://arxiv.org/abs/2208.03987v4 | mAP | 70.85 |
16k > Object Detection > Object Detection In Aerial Images | DIOR-R | LWGANet L2 | https://arxiv.org/abs/2501.10040v1 | mAP | 68.53 |
16k > Object Detection > Object Detection In Aerial Images | DIOR-R | LEGNet-S | https://arxiv.org/abs/2503.14012v1 | mAP | 68.40 |
16k > Object Detection > Object Detection In Aerial Images | DIOR-R | DecoupleNet D2 | https://ieeexplore.ieee.org/document/10685518 | mAP | 67.08 |
16k > Object Detection > Video Object Detection | USC-GRAD-STDdb | SLTnet FPN-X101 | https://www.sciencedirect.com/science/article/abs/pii/S0262885621000846 | AP 0.5 | 44.9 |
16k > Object Detection > Video Object Detection | USC-GRAD-STDdb | SLTnet FPN-X101 | https://www.sciencedirect.com/science/article/abs/pii/S0262885621000846 | AP | 16.6 |
16k > Object Detection > Video Object Detection | EPIC-KITCHENS-55 | Ours (Faster RCNN) | https://arxiv.org/abs/2308.04770v1 | mAP@.5 | 41.7 |
16k > Object Detection > Video Object Detection | Waymo Open Dataset | null | https://arxiv.org/abs/2308.04770v1 | AP | 59.28 |
16k > Object Detection > Video Object Detection | EPIC KITCHENS-seen splits | Temporal ROI Align | https://arxiv.org/abs/2109.03495v2 | mAP | 42.2 |
16k > Object Detection > Video Object Detection | ImageNet VID | YOLOV++ | https://arxiv.org/abs/2407.19650v1 | MAP | 93.2 |
16k > Object Detection > Video Object Detection | ImageNet VID | DiffusionVID (Swin-B) | https://doi.org/10.1109/ACCESS.2023.3328341 | MAP | 92.5 |
16k > Object Detection > Video Object Detection | ImageNet VID | Ours (Def. DETR + SwinB) | https://arxiv.org/abs/2308.04770v1 | MAP | 91.3 |
16k > Object Detection > Video Object Detection | ImageNet VID | VSTAM | https://ieeexplore.ieee.org/document/9798833 | MAP | 91.1 |
16k > Object Detection > Video Object Detection | ImageNet VID | TGBFormer (Swin B) | https://arxiv.org/abs/2503.13903v2 | MAP | 90.3 |
16k > Object Detection > Video Object Detection | ImageNet VID | TransVOD (Swin Base) | https://arxiv.org/abs/2201.05047v4 | MAP | 90.1 |
16k > Object Detection > Video Object Detection | ImageNet VID | PTSEFormer (ResNet-101) | https://arxiv.org/abs/2209.02242v1 | MAP | 88.1 |
16k > Object Detection > Video Object Detection | ImageNet VID | Ours (Def. DETR + R101) | https://arxiv.org/abs/2308.04770v1 | MAP | 87.9 |
16k > Object Detection > Video Object Detection | ImageNet VID | YOLOV | https://arxiv.org/abs/2208.09686v2 | MAP | 87.5 |
16k > Object Detection > Video Object Detection | ImageNet VID | Ours (Faster RCNN + R101) | https://arxiv.org/abs/2308.04770v1 | MAP | 87.2 |
16k > Object Detection > Video Object Detection | ImageNet VID | DiffusionVID (ResNet-101) | https://doi.org/10.1109/ACCESS.2023.3328341 | MAP | 87.1 |
16k > Object Detection > Video Object Detection | ImageNet VID | DAFA-F (ResNeXt-101) | https://doi.org/10.1109/ACCESS.2022.3203399 | MAP | 85.9 |
16k > Object Detection > Video Object Detection | ImageNet VID | ClipVID | https://arxiv.org/abs/2308.07737v1 | MAP | 85.8 |
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