CPC G16H 30/40 (2018.01) [G06F 18/214 (2023.01); G06F 18/2431 (2023.01); G06N 3/04 (2013.01); G06N 3/08 (2013.01); G06T 7/0012 (2013.01); G06T 7/70 (2017.01); G06V 10/454 (2022.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30096 (2013.01)] | 14 Claims |
1. A method of labeling a target in an image, comprising:
acquiring an image;
acquiring a first neural network, the first neural network comprises a multi-layer convolutional neural network and a fully connected layer, wherein each layer of the multi-layer convolutional neural network comprises a convolutional layer, an activation function layer and a down-sampling layer arranged successively;
processing the image by using the multi-layer convolutional neural network of the first neural network acquired so as to obtain a target position mask for the image; and
labeling the target in the image based on the target position mask,
wherein the processing the image by using the multi-layer convolutional neural network of the first neural network acquired so as to obtain a target position mask for the image comprises:
performing a large-value spatial position sampling on an output of the activation function layer in at least one layer of the first neural network;
mapping an output of a down-sampling layer associated with the activation function layer to a coordinate space for the image based on a result of the large-value spatial position sampling, so as to obtain a mapping matrix; and
obtaining the target position mask based on the mapping matrix.
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