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 this https URL

基于查询的多任务学习框架在伪装实例分割中表现出色，通过构建一组掩蔽查询和一组边界查询，有效地整合全局掩蔽对象区域和边界提示，实现了伪装场景中的实例分割和边界检测。

一种统一的基于查询的伪装实例分割范式

A Unified Query-based Paradigm for Camouflaged Instance Segmentation

One crucial challenge of real-world multilingual speech recognition is the
long-tailed distribution problem, where some resource-rich languages like
English have abundant training data, but a long tail of low-resource languages
have varying amounts of limited training data. To overcome the long-tail
problem, in this paper, we propose Adapt-and-Adjust (A2), a transformer-based
multi-task learning framework for end-to-end multilingual speech recognition.
The A2 framework overcomes the long-tail problem via three techniques: (1)
exploiting a pretrained multilingual language model (mBERT) to improve the
performance of low-resource languages; (2) proposing dual adapters consisting
of both language-specific and language-agnostic adaptation with minimal
additional parameters; and (3) overcoming the class imbalance, either by
imposing class priors in the loss during training or adjusting the logits of
the softmax output during inference. Extensive experiments on the CommonVoice
corpus show that A2 significantly outperforms conventional approaches.

本文提出了一种基于 Transformer 的多任务学习框架 Adapt-and-Adjust (A2)，通过利用预先训练好的多语种语言模型 (mBERT) 来提高低资源语言的性能、提出有着最小额外参数的语言特定和语言不可知适应器，以及在训练期间通过强制类先验或调整 softmax 输出的逻辑回归来克服类失衡。在 CommonVoice 数据集上的广泛实验表明，A2 明显优于传统方法。