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 ⌀ |
|---|---|---|---|---|---|
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Indian Pines | WCNet | https://arxiv.org/abs/2504.10795v1 | Overall Accuracy | 99.87 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Indian Pines | SpectralNET | https://arxiv.org/abs/2104.00341v1 | Overall Accuracy | 99.86% |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Indian Pines | EKGNet | https://arxiv.org/abs/2504.13045v1 | Overall Accuracy | 99.84 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Indian Pines | FSKNet | https://arxiv.org/abs/2202.06458v1 | Overall Accuracy | 99.83% |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Indian Pines | HybridSN | https://arxiv.org/abs/1902.06701v3 | Overall Accuracy | 99.81% |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Indian Pines | STNet | https://arxiv.org/abs/2506.08324v1 | Overall Accuracy | 99.77 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Indian Pines | JigsawHSI | https://arxiv.org/abs/2206.02327v3 | Overall Accuracy | 99.74 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Indian Pines | MVNet | https://arxiv.org/abs/2507.04409v1 | Overall Accuracy | 99.74 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Indian Pines | SSDGL | https://arxiv.org/abs/2105.14327v1 | Overall Accuracy | 99.63% |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Indian Pines | SSDGL | https://arxiv.org/abs/2105.14327v1 | Kappa | 0.9958 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Indian Pines | A2S2K-ResNet | https://ieeexplore.ieee.org/document/9306920 | Overall Accuracy | 99.57 % |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Indian Pines | Recurrent 3D-CNN | https://arxiv.org/abs/1903.06258v2 | Overall Accuracy | 99.50% |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Indian Pines | A-SPN | https://ieeexplore.ieee.org/document/9325094 | Overall Accuracy | 99.24% |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Indian Pines | AMS-M2ESL | https://ieeexplore.ieee.org/abstract/document/10097620 | Overall Accuracy | 98.38±0.38% |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Indian Pines | AMS-M2ESL | https://ieeexplore.ieee.org/abstract/document/10097620 | OA@5%perclass | 98.38±0.38% |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Indian Pines | AMS-M2ESL | https://ieeexplore.ieee.org/abstract/document/10097620 | AA@5%perclass | 98.86±0.26% |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Indian Pines | AMS-M2ESL | https://ieeexplore.ieee.org/abstract/document/10097620 | Kappa@5%perclass | 0.9816±0.0043 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Indian Pines | CVSSN | http://dx.doi.org/10.1109/tcsvt.2022.3218284 | Overall Accuracy | 98.18±0.27% |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Indian Pines | CVSSN | http://dx.doi.org/10.1109/tcsvt.2022.3218284 | OA@10%perclass | 98.18±0.27% |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Indian Pines | CVSSN | http://dx.doi.org/10.1109/tcsvt.2022.3218284 | AA@10%perclass | 97.92±0.75% |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Indian Pines | CVSSN | http://dx.doi.org/10.1109/tcsvt.2022.3218284 | Kappa@10%perclass | 0.9792±0.0030 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Indian Pines | BASSNet | http://arxiv.org/abs/1612.00144v2 | Overall Accuracy | 96.77% |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Indian Pines | CNN-MRF | http://arxiv.org/abs/1705.00727v2 | Overall Accuracy | 96.12% |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Indian Pines | St-SS-pGRU | http://arxiv.org/abs/1810.12563v1 | Overall Accuracy | 90.35% |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Indian Pines | SSBC | https://doi.org/10.3390/rs16224270 | OA@10%perclass | 98.66% |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Indian Pines | SSBC | https://doi.org/10.3390/rs16224270 | AA@10%perclass | 97.16% |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Indian Pines | SSBC | https://doi.org/10.3390/rs16224270 | Kappa@10%perclass | 98.47% |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Indian Pines | SSBC | https://doi.org/10.3390/rs16224270 | F1@10%perclass | 97.61% |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Salinas Scene | HybridSN | https://arxiv.org/abs/1902.06701v3 | Overall Accuracy | 100% |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Salinas Scene | SpectralNET | https://arxiv.org/abs/2104.00341v1 | Overall Accuracy | 100% |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Salinas Scene | Deep Matrix Capsules | https://ieeexplore.ieee.org/document/10028853 | Overall Accuracy | 100% |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | CASI University of Houston | SSDGL | https://arxiv.org/abs/2105.14327v1 | Overall Accuracy | 95.36 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | CASI University of Houston | AMS-M2ESL | https://ieeexplore.ieee.org/abstract/document/10097620 | Overall Accuracy | 88.82±0.93% |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | CASI University of Houston | AMS-M2ESL | https://ieeexplore.ieee.org/abstract/document/10097620 | OA@disjoint | 88.82±0.93% |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | CASI University of Houston | AMS-M2ESL | https://ieeexplore.ieee.org/abstract/document/10097620 | AA@disjoint | 92.15±0.30% |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | CASI University of Houston | AMS-M2ESL | https://ieeexplore.ieee.org/abstract/document/10097620 | Kappa@disjoint | 0.8785±0.0101 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | CASI University of Houston | FPGA | https://arxiv.org/abs/2011.05670v1 | Overall Accuracy | 86.61 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | CASI University of Houston | FPGA | https://arxiv.org/abs/2011.05670v1 | Average Accuracy | 88.44 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | CASI University of Houston | FPGA | https://arxiv.org/abs/2011.05670v1 | Kappa | 0.8555 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | CASI University of Houston | CVSSN | http://dx.doi.org/10.1109/tcsvt.2022.3218284 | Overall Accuracy | 82.55±0.47% |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | CASI University of Houston | CVSSN | http://dx.doi.org/10.1109/tcsvt.2022.3218284 | OA@disjoint | 82.55±0.47% |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | CASI University of Houston | CVSSN | http://dx.doi.org/10.1109/tcsvt.2022.3218284 | AA@disjoint | 85.64±0.98% |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | CASI University of Houston | CVSSN | http://dx.doi.org/10.1109/tcsvt.2022.3218284 | Kappa@disjoint | 0.8115±0.0050 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Salinas | JigsawHSI | https://arxiv.org/abs/2206.02327v3 | OA@200 | 100.00 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Salinas | FPGA | https://arxiv.org/abs/2011.05670v1 | OA@200 | 99.92 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Salinas | FPGA | https://arxiv.org/abs/2011.05670v1 | AA@200 | 99.91 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Salinas | FPGA | https://arxiv.org/abs/2011.05670v1 | Kappa@200 | 0.9991 |
Hyperspectral Image Segmentation > Hyperspectral > Hyperspectral Image Classification | Salinas | FSKNet | https://arxiv.org/abs/2202.06458v1 | Overall Accuracy | 99.98% |
Explanatory Visual Question Answering | GQA-REX | VCIN | http://openaccess.thecvf.com//content/ICCV2023/html/Xue_Variational_Causal_Inference_Network_for_Explanatory_Visual_Question_Answering_ICCV_2023_paper.html | BLEU-4 | 58.65 |
Explanatory Visual Question Answering | GQA-REX | VCIN | http://openaccess.thecvf.com//content/ICCV2023/html/Xue_Variational_Causal_Inference_Network_for_Explanatory_Visual_Question_Answering_ICCV_2023_paper.html | METEOR | 41.57 |
Explanatory Visual Question Answering | GQA-REX | VCIN | http://openaccess.thecvf.com//content/ICCV2023/html/Xue_Variational_Causal_Inference_Network_for_Explanatory_Visual_Question_Answering_ICCV_2023_paper.html | ROUGE-L | 81.45 |
Explanatory Visual Question Answering | GQA-REX | VCIN | http://openaccess.thecvf.com//content/ICCV2023/html/Xue_Variational_Causal_Inference_Network_for_Explanatory_Visual_Question_Answering_ICCV_2023_paper.html | CIDEr | 519.23 |
Explanatory Visual Question Answering | GQA-REX | VCIN | http://openaccess.thecvf.com//content/ICCV2023/html/Xue_Variational_Causal_Inference_Network_for_Explanatory_Visual_Question_Answering_ICCV_2023_paper.html | SPICE | 54.63 |
Explanatory Visual Question Answering | GQA-REX | VCIN | http://openaccess.thecvf.com//content/ICCV2023/html/Xue_Variational_Causal_Inference_Network_for_Explanatory_Visual_Question_Answering_ICCV_2023_paper.html | Grounding | 77.33 |
Explanatory Visual Question Answering | GQA-REX | VCIN | http://openaccess.thecvf.com//content/ICCV2023/html/Xue_Variational_Causal_Inference_Network_for_Explanatory_Visual_Question_Answering_ICCV_2023_paper.html | GQA-val | 81.80 |
Explanatory Visual Question Answering | GQA-REX | VCIN | http://openaccess.thecvf.com//content/ICCV2023/html/Xue_Variational_Causal_Inference_Network_for_Explanatory_Visual_Question_Answering_ICCV_2023_paper.html | GQA-test | 60.61 |
Explanatory Visual Question Answering | GQA-REX | REX-LXMERT | https://arxiv.org/abs/2203.06107v1 | BLEU-4 | 54.79 |
Explanatory Visual Question Answering | GQA-REX | REX-LXMERT | https://arxiv.org/abs/2203.06107v1 | METEOR | 39.51 |
Explanatory Visual Question Answering | GQA-REX | REX-LXMERT | https://arxiv.org/abs/2203.06107v1 | ROUGE-L | 79.41 |
Explanatory Visual Question Answering | GQA-REX | REX-LXMERT | https://arxiv.org/abs/2203.06107v1 | CIDEr | 466.01 |
Explanatory Visual Question Answering | GQA-REX | REX-LXMERT | https://arxiv.org/abs/2203.06107v1 | SPICE | 49.98 |
Explanatory Visual Question Answering | GQA-REX | REX-LXMERT | https://arxiv.org/abs/2203.06107v1 | Grounding | 70.79 |
Explanatory Visual Question Answering | GQA-REX | REX-LXMERT | https://arxiv.org/abs/2203.06107v1 | GQA-val | 78.19 |
Explanatory Visual Question Answering | GQA-REX | REX-LXMERT | https://arxiv.org/abs/2203.06107v1 | GQA-test | 58.15 |
Explanatory Visual Question Answering | GQA-REX | REX-VisualBert | https://arxiv.org/abs/2203.06107v1 | BLEU-4 | 54.59 |
Explanatory Visual Question Answering | GQA-REX | REX-VisualBert | https://arxiv.org/abs/2203.06107v1 | METEOR | 39.22 |
Explanatory Visual Question Answering | GQA-REX | REX-VisualBert | https://arxiv.org/abs/2203.06107v1 | ROUGE-L | 78.56 |
Explanatory Visual Question Answering | GQA-REX | REX-VisualBert | https://arxiv.org/abs/2203.06107v1 | CIDEr | 464.20 |
Explanatory Visual Question Answering | GQA-REX | REX-VisualBert | https://arxiv.org/abs/2203.06107v1 | SPICE | 46.80 |
Explanatory Visual Question Answering | GQA-REX | REX-VisualBert | https://arxiv.org/abs/2203.06107v1 | Grounding | 67.95 |
Explanatory Visual Question Answering | GQA-REX | REX-VisualBert | https://arxiv.org/abs/2203.06107v1 | GQA-val | 66.16 |
Explanatory Visual Question Answering | GQA-REX | REX-VisualBert | https://arxiv.org/abs/2203.06107v1 | GQA-test | 57.77 |
Explanatory Visual Question Answering | GQA-REX | VQAE | http://arxiv.org/abs/1803.07464v2 | BLEU-4 | 42.56 |
Explanatory Visual Question Answering | GQA-REX | VQAE | http://arxiv.org/abs/1803.07464v2 | METEOR | 34.51 |
Explanatory Visual Question Answering | GQA-REX | VQAE | http://arxiv.org/abs/1803.07464v2 | ROUGE-L | 73.59 |
Explanatory Visual Question Answering | GQA-REX | VQAE | http://arxiv.org/abs/1803.07464v2 | CIDEr | 358.20 |
Explanatory Visual Question Answering | GQA-REX | VQAE | http://arxiv.org/abs/1803.07464v2 | SPICE | 40.39 |
Explanatory Visual Question Answering | GQA-REX | VQAE | http://arxiv.org/abs/1803.07464v2 | Grounding | 31.29 |
Explanatory Visual Question Answering | GQA-REX | VQAE | http://arxiv.org/abs/1803.07464v2 | GQA-val | 65.19 |
Explanatory Visual Question Answering | GQA-REX | VQAE | http://arxiv.org/abs/1803.07464v2 | GQA-test | 57.24 |
Explanatory Visual Question Answering | GQA-REX | EXP | https://arxiv.org/abs/1809.02805v2 | BLEU-4 | 42.45 |
Explanatory Visual Question Answering | GQA-REX | EXP | https://arxiv.org/abs/1809.02805v2 | METEOR | 34.46 |
Explanatory Visual Question Answering | GQA-REX | EXP | https://arxiv.org/abs/1809.02805v2 | ROUGE-L | 73.51 |
Explanatory Visual Question Answering | GQA-REX | EXP | https://arxiv.org/abs/1809.02805v2 | CIDEr | 357.10 |
Explanatory Visual Question Answering | GQA-REX | EXP | https://arxiv.org/abs/1809.02805v2 | SPICE | 40.35 |
Explanatory Visual Question Answering | GQA-REX | EXP | https://arxiv.org/abs/1809.02805v2 | Grounding | 33.52 |
Explanatory Visual Question Answering | GQA-REX | EXP | https://arxiv.org/abs/1809.02805v2 | GQA-val | 65.17 |
Explanatory Visual Question Answering | GQA-REX | EXP | https://arxiv.org/abs/1809.02805v2 | GQA-test | 56.92 |
Explanatory Visual Question Answering > FS-MEVQA | SME | MEAgent | https://dl.acm.org/doi/abs/10.1145/3664647.3681597 | BLEU-4 | 67.91 |
Explanatory Visual Question Answering > FS-MEVQA | SME | MEAgent | https://dl.acm.org/doi/abs/10.1145/3664647.3681597 | METEOR | 50.55 |
Explanatory Visual Question Answering > FS-MEVQA | SME | MEAgent | https://dl.acm.org/doi/abs/10.1145/3664647.3681597 | ROUGE-L | 79.41 |
Explanatory Visual Question Answering > FS-MEVQA | SME | MEAgent | https://dl.acm.org/doi/abs/10.1145/3664647.3681597 | CIDEr | 510.44 |
Explanatory Visual Question Answering > FS-MEVQA | SME | MEAgent | https://dl.acm.org/doi/abs/10.1145/3664647.3681597 | SPICE | 64.09 |
Explanatory Visual Question Answering > FS-MEVQA | SME | MEAgent | https://dl.acm.org/doi/abs/10.1145/3664647.3681597 | Detection | 29.09 |
Explanatory Visual Question Answering > FS-MEVQA | SME | MEAgent | https://dl.acm.org/doi/abs/10.1145/3664647.3681597 | ACC | 51.45 |
Explanatory Visual Question Answering > FS-MEVQA | SME | MEAgent | https://dl.acm.org/doi/abs/10.1145/3664647.3681597 | #Learning Samples (N) | 16 |
Explanatory Visual Question Answering > FS-MEVQA | SME | GPT-4-1106-Vision-Preview | https://arxiv.org/abs/2303.08774v5 | BLEU-4 | 45.51 |
Explanatory Visual Question Answering > FS-MEVQA | SME | GPT-4-1106-Vision-Preview | https://arxiv.org/abs/2303.08774v5 | METEOR | 35.17 |
Explanatory Visual Question Answering > FS-MEVQA | SME | GPT-4-1106-Vision-Preview | https://arxiv.org/abs/2303.08774v5 | ROUGE-L | 52.67 |
Explanatory Visual Question Answering > FS-MEVQA | SME | GPT-4-1106-Vision-Preview | https://arxiv.org/abs/2303.08774v5 | CIDEr | 269.68 |
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