  Transformers have been widely used in numerous vision problems especially for
visual recognition and detection. Detection transformers are the first fully
end-to-end learning systems for object detection, while vision transformers are
the first fully transformer-based architecture for image classification. In
this paper, we integrate Vision and Detection Transformers (ViDT) to construct
an effective and efficient object detector. ViDT introduces a reconfigured
attention module to extend the recent Swin Transformer to be a standalone
object detector, followed by a computationally efficient transformer decoder
that exploits multi-scale features and auxiliary techniques essential to boost
the detection performance without much increase in computational load. In
addition, we extend it to ViDT+ to support joint-task learning for object
detection and instance segmentation. Specifically, we attach an efficient
multi-scale feature fusion layer and utilize two more auxiliary training
losses, IoU-aware loss and token labeling loss. Extensive evaluation results on
the Microsoft COCO benchmark dataset demonstrate that ViDT obtains the best AP
and latency trade-off among existing fully transformer-based object detectors,
and its extended ViDT+ achieves 53.2AP owing to its high scalability for large
models. The source code and trained models are available at
https://github.com/naver-ai/vidt.
