Multi-label image classification with semantic information and upsampling

In the field of multi-label image classification, accurately identifying multiple relevant labels in an image is crucial for applications such as image understanding, automatic annotation, and intelligent search. However, traditional classification methods are often difficult to handle both the complex semantics and fine details of images. To this end, a method combining semantic information and up-sampling techniques is proposed. On the one hand, the graph attention network (GAT) is combined with the differentiable graph pooling module (DiffPool) to enhance the model's multi-faceted understanding of the semantic information of the image, and on the other hand, the lightweight up-sampling operator CARAFE is introduced to enhance the model's understanding of the details of the image. The method effectively improves the classification accuracy of multi-labeled images by fully utilizing the deep semantic features of the images and recovering the image details using the up-sampling technique.