Co-salient Object Detection (CoSOD) endeavors to replicate the human visual system's capacity to recognize common and salient objects within a collection of images. Despite recent advancements in deep learning models, these models still rely on training with well-annotated CoSOD datasets. The exploration of training-free zero-shot CoSOD frameworks has been limited. In this paper, taking inspiration from the zero-shot transfer capabilities of foundational computer vision models, we introduce the first zero-shot CoSOD framework that harnesses these models without any training process. To achieve this, we introduce two novel components in our proposed framework: the group prompt generation (GPG) module and the co-saliency map generation (CMP) module. We evaluate the framework's performance on widely-used datasets and observe impressive results. Our approach surpasses existing unsupervised methods and even outperforms fully supervised methods developed before 2020, while remaining competitive with some fully supervised methods developed before 2022.

翻译：共显著目标检测（CoSOD）旨在复现人类视觉系统从图像集合中识别共同且显著目标的能力。尽管深度学习模型近期取得了进展，但这些模型仍依赖于使用标注完善的CoSOD数据集进行训练。关于无训练过程的零样本CoSOD框架的探索十分有限。本文受基础计算机视觉模型零样本迁移能力的启发，首次提出一种利用这类模型且无需任何训练过程的零样本CoSOD框架。为实现该目标，我们在所提出的框架中引入了两个新模块：组提示生成（GPG）模块和共显著图生成（CMP）模块。我们在广泛使用的数据集上评估了该框架的性能，观察到令人印象深刻的结果。我们的方法不仅超越了现有无监督方法，甚至优于2020年前开发的完全监督方法，同时与部分2022年前开发的完全监督方法相比仍具竞争力。