This paper presents a simple and effective approach to solving the multi-label classification problem. The proposed approach leverages Transformer decoders to query the existence of a class label. The use of Transformer is rooted in the need of extracting local discriminative features adaptively for different labels, which is a strongly desired property due to the existence of multiple objects in one image. The built-in cross-attention module in the Transformer decoder offers an effective way to use label embeddings as queries to probe and pool class-related features from a feature map computed by a vision backbone for subsequent binary classifications. Compared with prior works, the new framework is simple, using standard Transformers and vision backbones, and effective, consistently outperforming all previous works on five multi-label classification data sets, including MS-COCO, PASCAL VOC, NUS-WIDE, and Visual Genome. Particularly, we establish $91.3\%$ mAP on MS-COCO. We hope its compact structure, simple implementation, and superior performance serve as a strong baseline for multi-label classification tasks and future studies. The code will be available soon at https://github.com/SlongLiu/query2labels.

本文提出了一种简单而有效的方法来解决多标签分类问题，该方法利用Transformer解码器查询类标签的存在，并使用视觉骨干计算的特征图来进行后续的二进制分类，相比于以前的工作，该方法更为简单有效，对于五个多标签分类数据集，包括MS-COCO，PASCAL VOC，NUS-WIDE和Visual Genome，始终优于以前的所有工作，我们在MS-COCO上建立了91.3％的mAP。

Query2Label：一种简单的Transformer多标签分类方法