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Simultaneous image patch attention and pruning for patch selective transformer
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AI
DOI：10.1016/j.imavis.2024.105239.png)
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Abstract
En 中文
Vision transformer models provide superior performance compared to convolutional neural networks for various computer vision tasks but require increased computational overhead with large datasets. This paper proposes a patch selective vision transformer that effectively selects patches to reduce computational costs while simultaneously extracting global and local self-representative patch information to maintain performance. The interpatch attention in the transformer encoder emphasizes meaningful features by capturing the inter-patch relationships of features, and dynamic patch pruning is applied to the attentive patches using a learnable soft threshold that measures the maximum multi-head importance scores. The proposed patch attention and pruning method provides constraints to exploit dominant feature maps in conjunction with self-attention, thus avoiding the propagation of noisy or irrelevant information. The proposed patch-selective transformer also helps to address computer vision problems such as scale, background clutter, and partial occlusion, resulting in a lightweight and general-purpose vision transformer suitable for mobile devices.
Keywords:
Patch pruning
Patch emphasis
Attentive patch selection
Vision transformer
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