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Models/D3Net

D3Net

Reported on 134 benchmarks across 7 tasks · 3 papers · 128 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Methodology106 results

2D ClassificationonMUSDB18
SDR (other)· uses extra data· 2020-10-05
5.37
best: 7.08 (Band-Split RNN (semi-sup.))
SOTA
D3Net: Densely connected multidilated DenseNet for music source separation arXiv:2010.01733
2D ClassificationonMUSDB18
SDR (vocals)· uses extra data· 2020-10-05
7.8
best: 10.47 (Band-Split RNN (semi-sup.))
SOTA
D3Net: Densely connected multidilated DenseNet for music source separation arXiv:2010.01733
3DonNJU2K
Average MAE· 2019-07-15
0.046
best: 0.023 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
3DonNJU2K
S-Measure· 2019-07-15
90
best: 93.7 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
3DonNJU2K
max E-Measure· 2019-07-15
93.9
best: 96.4 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
3DonNJU2K
max F-Measure· 2019-07-15
90
best: 94.6 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
3DonSTERE
Average MAE· 2019-07-15
0.046
best: 0.03 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
3DonSTERE
S-Measure· 2019-07-15
89.9
best: 92.3 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
3DonSTERE
max E-Measure· 2019-07-15
93.8
best: 95.2 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
3DonSTERE
max F-Measure· 2019-07-15
89.1
best: 92.9 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
3DonLFSD
Average MAE· 2019-07-15
0.095
best: 0.065 (UCNet-CVAE)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
3DonLFSD
S-Measure· 2019-07-15
82.5
best: 86.8 (UCNet-CVAE)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
3DonLFSD
max E-Measure· 2019-07-15
86.2
best: 90.6 (BTS-Net)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
3DonLFSD
max F-Measure· 2019-07-15
81
best: 87.4 (BTS-Net)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
3DonSIP
Average MAE· 2019-07-15
0.063
best: 0.032 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
3DonSIP
S-Measure· 2019-07-15
86
best: 91.5 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
3DonSIP
max E-Measure· 2019-07-15
90.9
best: 95 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
3DonSIP
max F-Measure· 2019-07-15
86.1
best: 93.8 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
3DonRGBD135
Average MAE· 2019-07-15
0.058
best: 0.042 (DASNet)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
3DonRGBD135
S-Measure· 2019-07-15
85.7
best: 88.5 (DASNet)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
3DonRGBD135
max E-Measure· 2019-07-15
91
best: 91.9 (BBS-Net)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
3DonRGBD135
max F-Measure· 2019-07-15
83.4
best: 88.1 (DASNet)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
3DonNLPR
Average MAE· 2019-07-15
0.03
best: 0.016 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
3DonNLPR
S-Measure· 2019-07-15
91.2
best: 94.2 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
3DonNLPR
max E-Measure· 2019-07-15
95.3
best: 97.1 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
3DonNLPR
max F-Measure· 2019-07-15
89.7
best: 93.9 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
2D ClassificationonNJU2K
Average MAE· 2019-07-15
0.046
best: 0.023 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
2D ClassificationonNJU2K
S-Measure· 2019-07-15
90
best: 93.7 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
2D ClassificationonNJU2K
max E-Measure· 2019-07-15
93.9
best: 96.4 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
2D ClassificationonNJU2K
max F-Measure· 2019-07-15
90
best: 94.6 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
2D ClassificationonSTERE
Average MAE· 2019-07-15
0.046
best: 0.03 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
2D ClassificationonSTERE
S-Measure· 2019-07-15
89.9
best: 92.3 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
2D ClassificationonSTERE
max E-Measure· 2019-07-15
93.8
best: 95.2 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
2D ClassificationonSTERE
max F-Measure· 2019-07-15
89.1
best: 92.9 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
2D ClassificationonLFSD
Average MAE· 2019-07-15
0.095
best: 0.065 (UCNet-CVAE)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
2D ClassificationonLFSD
S-Measure· 2019-07-15
82.5
best: 86.8 (UCNet-CVAE)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
2D ClassificationonLFSD
max E-Measure· 2019-07-15
86.2
best: 90.6 (BTS-Net)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
2D ClassificationonLFSD
max F-Measure· 2019-07-15
81
best: 87.4 (BTS-Net)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
2D ClassificationonSIP
Average MAE· 2019-07-15
0.063
best: 0.032 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
2D ClassificationonSIP
S-Measure· 2019-07-15
86
best: 91.5 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
2D ClassificationonSIP
max E-Measure· 2019-07-15
90.9
best: 95 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
2D ClassificationonSIP
max F-Measure· 2019-07-15
86.1
best: 93.8 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
2D ClassificationonRGBD135
Average MAE· 2019-07-15
0.058
best: 0.042 (DASNet)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
2D ClassificationonRGBD135
S-Measure· 2019-07-15
85.7
best: 88.5 (DASNet)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
2D ClassificationonRGBD135
max E-Measure· 2019-07-15
91
best: 91.9 (BBS-Net)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
2D ClassificationonRGBD135
max F-Measure· 2019-07-15
83.4
best: 88.1 (DASNet)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
2D ClassificationonNLPR
Average MAE· 2019-07-15
0.03
best: 0.016 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
2D ClassificationonNLPR
S-Measure· 2019-07-15
91.2
best: 94.2 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
2D ClassificationonNLPR
max E-Measure· 2019-07-15
95.3
best: 97.1 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
2D ClassificationonNLPR
max F-Measure· 2019-07-15
89.7
best: 93.9 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
2D Object DetectiononNJU2K
Average MAE· 2019-07-15
0.046
best: 0.023 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
2D Object DetectiononNJU2K
S-Measure· 2019-07-15
90
best: 93.7 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
2D Object DetectiononNJU2K
max E-Measure· 2019-07-15
93.9
best: 96.4 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
2D Object DetectiononNJU2K
max F-Measure· 2019-07-15
90
best: 94.6 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
2D Object DetectiononSTERE
Average MAE· 2019-07-15
0.046
best: 0.03 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
2D Object DetectiononSTERE
S-Measure· 2019-07-15
89.9
best: 92.3 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
2D Object DetectiononSTERE
max E-Measure· 2019-07-15
93.8
best: 95.2 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
2D Object DetectiononSTERE
max F-Measure· 2019-07-15
89.1
best: 92.9 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
2D Object DetectiononLFSD
Average MAE· 2019-07-15
0.095
best: 0.065 (UCNet-CVAE)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
2D Object DetectiononLFSD
S-Measure· 2019-07-15
82.5
best: 86.8 (UCNet-CVAE)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
2D Object DetectiononLFSD
max E-Measure· 2019-07-15
86.2
best: 90.6 (BTS-Net)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
2D Object DetectiononLFSD
max F-Measure· 2019-07-15
81
best: 87.4 (BTS-Net)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
2D Object DetectiononSIP
Average MAE· 2019-07-15
0.063
best: 0.032 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
2D Object DetectiononSIP
S-Measure· 2019-07-15
86
best: 91.5 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
2D Object DetectiononSIP
max E-Measure· 2019-07-15
90.9
best: 95 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
2D Object DetectiononSIP
max F-Measure· 2019-07-15
86.1
best: 93.8 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
2D Object DetectiononRGBD135
Average MAE· 2019-07-15
0.058
best: 0.042 (DASNet)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
2D Object DetectiononRGBD135
S-Measure· 2019-07-15
85.7
best: 88.5 (DASNet)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
2D Object DetectiononRGBD135
max E-Measure· 2019-07-15
91
best: 91.9 (BBS-Net)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
2D Object DetectiononRGBD135
max F-Measure· 2019-07-15
83.4
best: 88.1 (DASNet)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
2D Object DetectiononNLPR
Average MAE· 2019-07-15
0.03
best: 0.016 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
2D Object DetectiononNLPR
S-Measure· 2019-07-15
91.2
best: 94.2 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
2D Object DetectiononNLPR
max E-Measure· 2019-07-15
95.3
best: 97.1 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
2D Object DetectiononNLPR
max F-Measure· 2019-07-15
89.7
best: 93.9 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
16konNJU2K
Average MAE· 2019-07-15
0.046
best: 0.023 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
16konNJU2K
S-Measure· 2019-07-15
90
best: 93.7 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
16konNJU2K
max E-Measure· 2019-07-15
93.9
best: 96.4 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
16konNJU2K
max F-Measure· 2019-07-15
90
best: 94.6 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
16konSTERE
Average MAE· 2019-07-15
0.046
best: 0.03 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
16konSTERE
S-Measure· 2019-07-15
89.9
best: 92.3 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
16konSTERE
max E-Measure· 2019-07-15
93.8
best: 95.2 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
16konSTERE
max F-Measure· 2019-07-15
89.1
best: 92.9 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
16konLFSD
Average MAE· 2019-07-15
0.095
best: 0.065 (UCNet-CVAE)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
16konLFSD
S-Measure· 2019-07-15
82.5
best: 86.8 (UCNet-CVAE)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
16konLFSD
max E-Measure· 2019-07-15
86.2
best: 90.6 (BTS-Net)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
16konLFSD
max F-Measure· 2019-07-15
81
best: 87.4 (BTS-Net)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
16konSIP
Average MAE· 2019-07-15
0.063
best: 0.032 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
16konSIP
S-Measure· 2019-07-15
86
best: 91.5 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
16konSIP
max E-Measure· 2019-07-15
90.9
best: 95 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
16konSIP
max F-Measure· 2019-07-15
86.1
best: 93.8 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
16konRGBD135
Average MAE· 2019-07-15
0.058
best: 0.042 (DASNet)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
16konRGBD135
S-Measure· 2019-07-15
85.7
best: 88.5 (DASNet)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
16konRGBD135
max E-Measure· 2019-07-15
91
best: 91.9 (BBS-Net)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
16konRGBD135
max F-Measure· 2019-07-15
83.4
best: 88.1 (DASNet)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
16konNLPR
Average MAE· 2019-07-15
0.03
best: 0.016 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
16konNLPR
S-Measure· 2019-07-15
91.2
best: 94.2 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
16konNLPR
max E-Measure· 2019-07-15
95.3
best: 97.1 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
16konNLPR
max F-Measure· 2019-07-15
89.7
best: 93.9 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
2D ClassificationonMUSDB18
SDR (avg)· uses extra data· 2020-10-05
6.68
best: 9.2 (Sparse HT Demucs (fine tuned))
D3Net: Densely connected multidilated DenseNet for music source separation arXiv:2010.01733
2D ClassificationonMUSDB18
SDR (bass)· uses extra data· 2020-10-05
6.2
best: 10.47 (Sparse HT Demucs (fine tuned))
D3Net: Densely connected multidilated DenseNet for music source separation arXiv:2010.01733
2D ClassificationonMUSDB18
SDR (drums)· uses extra data· 2020-10-05
7.36
best: 10.83 (Sparse HT Demucs (fine tuned))
D3Net: Densely connected multidilated DenseNet for music source separation arXiv:2010.01733
2D ClassificationonMUSDB18
SDR (avg)· 2020-10-05
6.01
best: 9.2 (Sparse HT Demucs (fine tuned))
D3Net: Densely connected multidilated DenseNet for music source separation arXiv:2010.01733
2D ClassificationonMUSDB18
SDR (bass)· 2020-10-05
5.25
best: 10.47 (Sparse HT Demucs (fine tuned))
D3Net: Densely connected multidilated DenseNet for music source separation arXiv:2010.01733
2D ClassificationonMUSDB18
SDR (drums)· 2020-10-05
7.01
best: 10.83 (Sparse HT Demucs (fine tuned))
D3Net: Densely connected multidilated DenseNet for music source separation arXiv:2010.01733
2D ClassificationonMUSDB18
SDR (other)· 2020-10-05
4.53
best: 7.08 (Band-Split RNN (semi-sup.))
D3Net: Densely connected multidilated DenseNet for music source separation arXiv:2010.01733
2D ClassificationonMUSDB18
SDR (vocals)· 2020-10-05
7.24
best: 10.47 (Band-Split RNN (semi-sup.))
D3Net: Densely connected multidilated DenseNet for music source separation arXiv:2010.01733

Computer Vision24 results

Object DetectiononNJU2K
Average MAE· 2019-07-15
0.046
best: 0.023 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
Object DetectiononNJU2K
S-Measure· 2019-07-15
90
best: 93.7 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
Object DetectiononNJU2K
max E-Measure· 2019-07-15
93.9
best: 96.4 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
Object DetectiononNJU2K
max F-Measure· 2019-07-15
90
best: 94.6 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
Object DetectiononSTERE
Average MAE· 2019-07-15
0.046
best: 0.03 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
Object DetectiononSTERE
S-Measure· 2019-07-15
89.9
best: 92.3 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
Object DetectiononSTERE
max E-Measure· 2019-07-15
93.8
best: 95.2 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
Object DetectiononSTERE
max F-Measure· 2019-07-15
89.1
best: 92.9 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
Object DetectiononLFSD
Average MAE· 2019-07-15
0.095
best: 0.065 (UCNet-CVAE)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
Object DetectiononLFSD
S-Measure· 2019-07-15
82.5
best: 86.8 (UCNet-CVAE)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
Object DetectiononLFSD
max E-Measure· 2019-07-15
86.2
best: 90.6 (BTS-Net)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
Object DetectiononLFSD
max F-Measure· 2019-07-15
81
best: 87.4 (BTS-Net)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
Object DetectiononSIP
Average MAE· 2019-07-15
0.063
best: 0.032 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
Object DetectiononSIP
S-Measure· 2019-07-15
86
best: 91.5 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
Object DetectiononSIP
max E-Measure· 2019-07-15
90.9
best: 95 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
Object DetectiononSIP
max F-Measure· 2019-07-15
86.1
best: 93.8 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
Object DetectiononRGBD135
Average MAE· 2019-07-15
0.058
best: 0.042 (DASNet)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
Object DetectiononRGBD135
S-Measure· 2019-07-15
85.7
best: 88.5 (DASNet)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
Object DetectiononRGBD135
max E-Measure· 2019-07-15
91
best: 91.9 (BBS-Net)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
Object DetectiononRGBD135
max F-Measure· 2019-07-15
83.4
best: 88.1 (DASNet)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
Object DetectiononNLPR
Average MAE· 2019-07-15
0.03
best: 0.016 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
Object DetectiononNLPR
S-Measure· 2019-07-15
91.2
best: 94.2 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
Object DetectiononNLPR
max E-Measure· 2019-07-15
95.3
best: 97.1 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781
Object DetectiononNLPR
max F-Measure· 2019-07-15
89.7
best: 93.9 (DFormer-L)
SOTA
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks arXiv:1907.06781

Music10 results

Music Source SeparationonMUSDB18
SDR (other)· uses extra data· 2020-10-05
5.37
best: 7.08 (Band-Split RNN (semi-sup.))
SOTA
D3Net: Densely connected multidilated DenseNet for music source separation arXiv:2010.01733
Music Source SeparationonMUSDB18
SDR (vocals)· uses extra data· 2020-10-05
7.8
best: 10.47 (Band-Split RNN (semi-sup.))
SOTA
D3Net: Densely connected multidilated DenseNet for music source separation arXiv:2010.01733
Music Source SeparationonMUSDB18
SDR (avg)· uses extra data· 2020-10-05
6.68
best: 9.2 (Sparse HT Demucs (fine tuned))
D3Net: Densely connected multidilated DenseNet for music source separation arXiv:2010.01733
Music Source SeparationonMUSDB18
SDR (bass)· uses extra data· 2020-10-05
6.2
best: 10.47 (Sparse HT Demucs (fine tuned))
D3Net: Densely connected multidilated DenseNet for music source separation arXiv:2010.01733
Music Source SeparationonMUSDB18
SDR (drums)· uses extra data· 2020-10-05
7.36
best: 10.83 (Sparse HT Demucs (fine tuned))
D3Net: Densely connected multidilated DenseNet for music source separation arXiv:2010.01733
Music Source SeparationonMUSDB18
SDR (avg)· 2020-10-05
6.01
best: 9.2 (Sparse HT Demucs (fine tuned))
D3Net: Densely connected multidilated DenseNet for music source separation arXiv:2010.01733
Music Source SeparationonMUSDB18
SDR (bass)· 2020-10-05
5.25
best: 10.47 (Sparse HT Demucs (fine tuned))
D3Net: Densely connected multidilated DenseNet for music source separation arXiv:2010.01733
Music Source SeparationonMUSDB18
SDR (drums)· 2020-10-05
7.01
best: 10.83 (Sparse HT Demucs (fine tuned))
D3Net: Densely connected multidilated DenseNet for music source separation arXiv:2010.01733
Music Source SeparationonMUSDB18
SDR (other)· 2020-10-05
4.53
best: 7.08 (Band-Split RNN (semi-sup.))
D3Net: Densely connected multidilated DenseNet for music source separation arXiv:2010.01733
Music Source SeparationonMUSDB18
SDR (vocals)· 2020-10-05
7.24
best: 10.47 (Band-Split RNN (semi-sup.))
D3Net: Densely connected multidilated DenseNet for music source separation arXiv:2010.01733

Natural Language Processing4 results

Image CaptioningonNr3D
BLEU-4· 2021-12-02
20.7
best: 29.29 (3D CoCa)
SOTA
D3Net: A Unified Speaker-Listener Architecture for 3D Dense Captioning and Visual Grounding arXiv:2112.01551
Image CaptioningonNr3D
CIDEr· 2021-12-02
33.85
best: 52.84 (3D CoCa)
SOTA
D3Net: A Unified Speaker-Listener Architecture for 3D Dense Captioning and Visual Grounding arXiv:2112.01551
Image CaptioningonNr3D
METEOR· 2021-12-02
23.13
best: 25.6 (BiCA)
SOTA
D3Net: A Unified Speaker-Listener Architecture for 3D Dense Captioning and Visual Grounding arXiv:2112.01551
Image CaptioningonNr3D
ROUGE-L· 2021-12-02
53.38
best: 56.43 (3D CoCa)
SOTA
D3Net: A Unified Speaker-Listener Architecture for 3D Dense Captioning and Visual Grounding arXiv:2112.01551