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Models/ConvFormer

ConvFormer

Reported on 12 benchmarks across 4 tasks · 1 paper

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

Computer Vision6 results

3D Human Pose EstimationonMPI-INF-3DHP
AUC· 2023-04-04
69.8
best: 87.7 (TCPFormer (T=81))
ConvFormer: Parameter Reduction in Transformer Models for 3D Human Pose Estimation by Leveraging Dynamic Multi-Headed Convolutional Attention arXiv:2304.02147
3D Human Pose EstimationonMPI-INF-3DHP
MPJPE· 2023-04-04
53.6
best: 124.7 (VNect (Augm.))
ConvFormer: Parameter Reduction in Transformer Models for 3D Human Pose Estimation by Leveraging Dynamic Multi-Headed Convolutional Attention arXiv:2304.02147
3D Human Pose EstimationonMPI-INF-3DHP
PCK· 2023-04-04
96.4
best: 99.37 (LMT R152 384x384)
ConvFormer: Parameter Reduction in Transformer Models for 3D Human Pose Estimation by Leveraging Dynamic Multi-Headed Convolutional Attention arXiv:2304.02147
Pose EstimationonMPI-INF-3DHP
AUC· 2023-04-04
69.8
best: 87.7 (TCPFormer (T=81))
ConvFormer: Parameter Reduction in Transformer Models for 3D Human Pose Estimation by Leveraging Dynamic Multi-Headed Convolutional Attention arXiv:2304.02147
Pose EstimationonMPI-INF-3DHP
MPJPE· 2023-04-04
53.6
best: 124.7 (VNect (Augm.))
ConvFormer: Parameter Reduction in Transformer Models for 3D Human Pose Estimation by Leveraging Dynamic Multi-Headed Convolutional Attention arXiv:2304.02147
Pose EstimationonMPI-INF-3DHP
PCK· 2023-04-04
96.4
best: 99.37 (LMT R152 384x384)
ConvFormer: Parameter Reduction in Transformer Models for 3D Human Pose Estimation by Leveraging Dynamic Multi-Headed Convolutional Attention arXiv:2304.02147

Methodology3 results

3DonMPI-INF-3DHP
AUC· 2023-04-04
69.8
best: 87.7 (TCPFormer (T=81))
ConvFormer: Parameter Reduction in Transformer Models for 3D Human Pose Estimation by Leveraging Dynamic Multi-Headed Convolutional Attention arXiv:2304.02147
3DonMPI-INF-3DHP
MPJPE· 2023-04-04
53.6
best: 124.7 (VNect (Augm.))
ConvFormer: Parameter Reduction in Transformer Models for 3D Human Pose Estimation by Leveraging Dynamic Multi-Headed Convolutional Attention arXiv:2304.02147
3DonMPI-INF-3DHP
PCK· 2023-04-04
96.4
best: 99.37 (LMT R152 384x384)
ConvFormer: Parameter Reduction in Transformer Models for 3D Human Pose Estimation by Leveraging Dynamic Multi-Headed Convolutional Attention arXiv:2304.02147

Audio3 results

1 Image, 2*2 StitchionMPI-INF-3DHP
AUC· 2023-04-04
69.8
best: 87.7 (TCPFormer (T=81))
ConvFormer: Parameter Reduction in Transformer Models for 3D Human Pose Estimation by Leveraging Dynamic Multi-Headed Convolutional Attention arXiv:2304.02147
1 Image, 2*2 StitchionMPI-INF-3DHP
MPJPE· 2023-04-04
53.6
best: 124.7 (VNect (Augm.))
ConvFormer: Parameter Reduction in Transformer Models for 3D Human Pose Estimation by Leveraging Dynamic Multi-Headed Convolutional Attention arXiv:2304.02147
1 Image, 2*2 StitchionMPI-INF-3DHP
PCK· 2023-04-04
96.4
best: 99.37 (LMT R152 384x384)
ConvFormer: Parameter Reduction in Transformer Models for 3D Human Pose Estimation by Leveraging Dynamic Multi-Headed Convolutional Attention arXiv:2304.02147