Attention weight module and method for convolutional neural network

A convolutional neural network and attention technology, applied in the field of attention weight module, can solve the problems of image spatial information transformation not being robust, ignoring importance, etc., to enhance feature extraction ability, enhance learning ability, and improve feature The effect of extraction ability

Pending Publication Date: 2021-05-14
THE BOEING CO +1
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Problems solved by technology

The disadvantages of this technique are: (1) RAN ignores the importance of the relationship between channels and treats each feature map equally
[0011] SE-Net does not effectively process feature maps in the spat

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  • Attention weight module and method for convolutional neural network
  • Attention weight module and method for convolutional neural network
  • Attention weight module and method for convolutional neural network

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[0079]In order to better understand the present invention, the technical solutions in the embodiments of the present invention will be described in connection with the drawings in the embodiments of the present invention, and the embodiments described herein will be clearly understood. It is an embodiment of the invention, not all of the embodiments. Based on the embodiments in the present invention, those of ordinary skill in the art will belong to the scope of the invention in the present invention without making in the pre-creative labor premise.

[0080]It should be noted that the terms "of the present invention" and "include" and "having" and "having" and any variation in the above drawings are intended to cover the included, such as, including a series of steps or modules. Or the process, method, system, product, or device of the unit does not have to be limited to those steps or modules or units which are clearly listed, but may include other steps that are not clearly listed or...

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Abstract

The invention discloses an attention weight module and method for a convolutional neural network. The attention weight module comprises: an extraction unit configured to extract a feature vector from a feature map input to a convolutional neural network; a generation unit configured to feed the feature vector to the fully connected layer to generate an attention vector; and a weight allocation unit configured to allocate a weight to the feature map based on the attention vector. The attention weight block can extract more semantic information by considering the spatial attention weight and the channel relation weight at the same time, so that the convolutional neural network can have higher expression ability.

Description

technical field [0001] The invention relates to the field of image processing. Specifically, the present invention relates to attention weight modules and methods. Background technique [0002] In recent years, deep neural networks (deep learning) have been widely used in the field of pattern recognition, and many breakthroughs have been achieved, for example, using deep neural networks to achieve object detection, face recognition, semantic segmentation, etc. The achievement of these achievements is closely related to the ability of deep neural network to learn features with strong expressive ability (image representation method). Compared with the traditional manual feature extraction method, the manual method extracts the low-level features of the image by introducing expert knowledge, while the deep neural network is driven by data and can learn the hierarchical features of the image, including rich low-level features. (low-level), mid-level (mid-level), and high-level...

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Application Information

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IPC IPC(8): G06N3/04G06N3/08G06K9/46
CPCG06N3/08G06V10/454G06N3/048G06N3/045
Inventor 李永吴岳辛王伟刚叶翔张高鑫李婉婷刘莹施方李珂嘉
Owner THE BOEING CO
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