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⌀ |
|---|---|---|---|---|---|
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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