Transformer in computer vision has recently shown encouraging progress. In this work, we improve the original Pyramid Vision Transformer (PVTv1) by adding three improvement designs, which include (1) locally continuous features with convolutions , (2) position encodings with zero paddings , and (3) linear complexity attention layers with average pooling . With these simple modifications, our PVTv2 significantly improves PVTv1 on classification, detection, and segmentation. Moreover, PVTv2 achieves much better performance than recent works, including Swin Transformer , under ImageNet-1K pre-training . We hope this work will make state-of-the-art vision Transformer research more accessible. Code is available at URL .
Transformer in computer vision has recently shown encouraging progress. In this work, we improve the original Pyramid Vision Transformer (PVTv1) by adding three improvement designs, which include (1) overlapping patch embedding , (2) convolutional feed-forward networks , and (3) linear complexity attention layers . With these simple modifications, our PVTv2 significantly improves PVTv1 on classification, detection, and segmentation. Moreover, PVTv2 achieves better performance than recent works, including Swin Transformer . We hope this work will make state-of-the-art vision Transformer research more accessible. Code is available at URL .
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locally continuous features with convolutions
overlapping patch embedding
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position encodings with zero paddings
convolutional feed-forward networks
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with average pooling
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much
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, under ImageNet-1K pre-training