  End-to-end paradigms significantly improve the accuracy of various
deep-learning-based computer vision models. To this end, tasks like object
detection have been upgraded by replacing non-end-to-end components, such as
removing non-maximum suppression by training with a set loss based on bipartite
matching. However, such an upgrade is not applicable to instance segmentation,
due to its significantly higher output dimensions compared to object detection.
In this paper, we propose an instance segmentation Transformer, termed ISTR,
which is the first end-to-end framework of its kind. ISTR predicts
low-dimensional mask embeddings, and matches them with ground truth mask
embeddings for the set loss. Besides, ISTR concurrently conducts detection and
segmentation with a recurrent refinement strategy, which provides a new way to
achieve instance segmentation compared to the existing top-down and bottom-up
frameworks. Benefiting from the proposed end-to-end mechanism, ISTR
demonstrates state-of-the-art performance even with approximation-based
suboptimal embeddings. Specifically, ISTR obtains a 46.8/38.6 box/mask AP using
ResNet50-FPN, and a 48.1/39.9 box/mask AP using ResNet101-FPN, on the MS COCO
dataset. Quantitative and qualitative results reveal the promising potential of
ISTR as a solid baseline for instance-level recognition. Code has been made
available at: https://github.com/hujiecpp/ISTR.
