文档介绍：48 4 2022 4
第 卷 第 期 .School of Computer Science，Fudan University，Shanghai ，China；
2 200433
.Shanghai Key Laboratory of Data Science，Shanghai ，China）
【Abstract】As an important research direction in the field of computer vision，multi-label image classification is widely
used in recognition，detection，and other multi-label image classification methods cannot effectively
use label correlation information and the corresponding relationship between label semantics and image features，
resulting in poor classification new algorithm for multi-label image classification is using tag co-
occurrence information and tag prior knowledge to build a graph model，multi-scale attention is used to learn the target
in image features，and tag guided attention is used to fuse tag semantic features and image feature information to
integrate tag correlation and tag semantic information into model learning. On this basis，a dynamic graph model is
constructed based on the graph attention mechanism，and the label information graph model is dynamically updated and
learned to integrate the image and label information experimental results on a multi-