  Due to the high similarity between camouflaged instances and the background,
the recently proposed camouflaged instance segmentation (CIS) faces challenges
in accurate localization and instance segmentation. To this end, inspired by
query-based transformers, we propose a unified query-based multi-task learning
framework for camouflaged instance segmentation, termed UQFormer, which builds
a set of mask queries and a set of boundary queries to learn a shared composed
query representation and efficiently integrates global camouflaged object
region and boundary cues, for simultaneous instance segmentation and instance
boundary detection in camouflaged scenarios. Specifically, we design a composed
query learning paradigm that learns a shared representation to capture object
region and boundary features by the cross-attention interaction of mask queries
and boundary queries in the designed multi-scale unified learning transformer
decoder. Then, we present a transformer-based multi-task learning framework for
simultaneous camouflaged instance segmentation and camouflaged instance
boundary detection based on the learned composed query representation, which
also forces the model to learn a strong instance-level query representation.
Notably, our model views the instance segmentation as a query-based direct set
prediction problem, without other post-processing such as non-maximal
suppression. Compared with 14 state-of-the-art approaches, our UQFormer
significantly improves the performance of camouflaged instance segmentation.
Our code will be available at https://github.com/dongbo811/UQFormer.
