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.

翻译：由于伪装实例与背景之间存在高度相似性，近期提出的伪装实例分割（CIS）面临着精准定位与实例分割的挑战。为此，受基于查询的Transformer启发，我们提出了一种面向伪装实例分割的统一查询式多任务学习框架——UQFormer。该框架通过构建掩码查询集与边界查询集，学习共享的复合查询表示，有效整合全局伪装目标区域与边界线索，从而实现伪装场景下的实例分割与实例边界检测同步执行。具体而言，我们设计了一种复合查询学习范式，通过所设计的多尺度统一学习Transformer解码器中掩码查询与边界查询的交叉注意力交互，学习能够捕获目标区域与边界特征的共享表示。随后，我们基于所学得的复合查询表示，提出了一种基于Transformer的多任务学习框架，可同步完成伪装实例分割与伪装实例边界检测，同时强制模型学习强实例级查询表示。值得注意的是，本模型将实例分割视为基于查询的直推式集合预测问题，无需非极大值抑制等后处理操作。与14种最先进方法相比，我们的UQFormer显著提升了伪装实例分割性能。相关代码将开源至https://github.com/dongbo811/UQFormer。