The invention provides a cross-layer multi-model feature fusion and convolutional decoding-based image description method, and relates to the field of computer vision and natural language processing.The method comprises the following steps: S1, obtaining an embedded vector sequence and a final text feature; S2, calculating to obtain a vision and text fusion matching attention vector; S3, adding and fusing the attention vector and the text feature vector sum; S4, generating a complete description sentence. Cross-layer multi-model feature fusion is used, and the loss of low-layer image featureinformation is effectively compensated; the model can effectively extract and store semantic information in a complex background image, has the capacity of processing long-sequence words, is more accurate in description of image content and richer in information expression, and is worthy of vigorous popularization.
Cross-layer multi-model feature fusion and convolutional decoding-based image description method