Image description generation method, model training method, device and storage medium

A computing device, comprising one or more processors, memory coupled to the one or more processors and a plurality of programs stored in the memory that, when executed by the one or more processors, cause the computing device to perform a plurality of operations comprising:

• obtaining a target image;
• generating a first and a second global feature vectors and a first and a second label vector sets of the target image;
• generating a first and a second multi-mode feature vectors of the target image through a matching model, wherein the matching model is a model obtained through training according to a training image and reference image description information of the training image;
• and
• applying the first and the second multi-mode feature vectors, the first and the second global feature vectors, and the first and the second label vector sets to a computing model, to obtain target image description information, wherein the computing model is a model obtained through training according to image description information of the training image and the reference image description information, further including:
• splicing the first and second multi-mode feature vectors output by the matching model and the first and second label vector sets output by the feature extraction model, to obtain a first spliced vector and a second spliced vector;
• and
• inputting the first and second spliced vectors and the first and second global feature vectors to the computing model, to obtain the target image description information.

A non-transitory computer readable storage medium storing a plurality of machine readable instructions in connection with a computing device having one or more processors, wherein the plurality of machine readable instructions, when executed by the one or more processors, cause the computing device to perform a plurality of operations including:

• obtaining a target image;
• generating a first and a second global feature vectors and a first and a second label vector sets of the target image;
• generating a first and a second multi-mode feature vectors of the target image through a matching model, wherein the matching model is a model obtained through training according to a training image and reference image description information of the training image;
• and
• applying the first and the second multi-mode feature vectors, the first and the second global feature vectors, and the first and the second label vector sets to a computing model, to obtain target image description information, wherein the computing model is a model obtained through training according to image description information of the training image and the reference image description information, further including:
• splicing the first and second multi-mode feature vectors output by the matching model and the first and second label vector sets output by the feature extraction model, to obtain a first spliced vector and a second spliced vector;
• and
• inputting the first and second spliced vectors and the first and second global feature vectors to the computing model, to obtain the target image description information.