  Existing unified image segmentation models either employ a unified
architecture across multiple tasks but use separate weights tailored to each
dataset, or apply a single set of weights to multiple datasets but are limited
to a single task. In this paper, we introduce the Mixed-Query Transformer
(MQ-Former), a unified architecture for multi-task and multi-dataset image
segmentation using a single set of weights. To enable this, we propose a mixed
query strategy, which can effectively and dynamically accommodate different
types of objects without heuristic designs. In addition, the unified
architecture allows us to use data augmentation with synthetic masks and
captions to further improve model generalization. Experiments demonstrate that
MQ-Former can not only effectively handle multiple segmentation datasets and
tasks compared to specialized state-of-the-art models with competitive
performance, but also generalize better to open-set segmentation tasks,
evidenced by over 7 points higher performance than the prior art on the
open-vocabulary SeginW benchmark.
