  Multi-label image classification is about predicting a set of class labels
that can be considered as orderless sequential data. Transformers process the
sequential data as a whole, therefore they are inherently good at set
prediction. The first vision-based transformer model, which was proposed for
the object detection task introduced the concept of object queries. Object
queries are learnable positional encodings that are used by attention modules
in decoder layers to decode the object classes or bounding boxes using the
region of interests in an image. However, inputting the same set of object
queries to different decoder layers hinders the training: it results in lower
performance and delays convergence. In this paper, we propose the usage of
primal object queries that are only provided at the start of the transformer
decoder stack. In addition, we improve the mixup technique proposed for
multi-label classification. The proposed transformer model with primal object
queries improves the state-of-the-art class wise F1 metric by 2.1% and 1.8%;
and speeds up the convergence by 79.0% and 38.6% on MS-COCO and NUS-WIDE
datasets respectively.
