  All instance perception tasks aim at finding certain objects specified by
some queries such as category names, language expressions, and target
annotations, but this complete field has been split into multiple independent
subtasks. In this work, we present a universal instance perception model of the
next generation, termed UNINEXT. UNINEXT reformulates diverse instance
perception tasks into a unified object discovery and retrieval paradigm and can
flexibly perceive different types of objects by simply changing the input
prompts. This unified formulation brings the following benefits: (1) enormous
data from different tasks and label vocabularies can be exploited for jointly
training general instance-level representations, which is especially beneficial
for tasks lacking in training data. (2) the unified model is
parameter-efficient and can save redundant computation when handling multiple
tasks simultaneously. UNINEXT shows superior performance on 20 challenging
benchmarks from 10 instance-level tasks including classical image-level tasks
(object detection and instance segmentation), vision-and-language tasks
(referring expression comprehension and segmentation), and six video-level
object tracking tasks. Code is available at
https://github.com/MasterBin-IIAU/UNINEXT.
