The traditional definition of co-salient object detection (CoSOD) task is to segment the common salient objects in a group of relevant images. This definition is based on an assumption of group consensus consistency that is not always reasonable in the open-world setting, which results in robustness issue in the model when dealing with irrelevant images in the inputting image group under the open-word scenarios. To tackle this problem, we introduce a group selective exchange-masking (GSEM) approach for enhancing the robustness of the CoSOD model. GSEM takes two groups of images as input, each containing different types of salient objects. Based on the mixed metric we designed, GSEM selects a subset of images from each group using a novel learning-based strategy, then the selected images are exchanged. To simultaneously consider the uncertainty introduced by irrelevant images and the consensus features of the remaining relevant images in the group, we designed a latent variable generator branch and CoSOD transformer branch. The former is composed of a vector quantised-variational autoencoder to generate stochastic global variables that model uncertainty. The latter is designed to capture correlation-based local features that include group consensus. Finally, the outputs of the two branches are merged and passed to a transformer-based decoder to generate robust predictions. Taking into account that there are currently no benchmark datasets specifically designed for open-world scenarios, we constructed three open-world benchmark datasets, namely OWCoSal, OWCoSOD, and OWCoCA, based on existing datasets. By breaking the group-consistency assumption, these datasets provide effective simulations of real-world scenarios and can better evaluate the robustness and practicality of models.

为提高共显著目标检测模型的鲁棒性，在开放世界场景下处理无关图像时引入了一种群组选择性交换掩模(GSEM)方法。GSEM采用两组图像作为输入，每组包含不同类型的显著对象，并使用一种基于学习的策略选择每组中的子集图像，并进行交换。为同时考虑不相关图像引入的不确定性和群组中其余相关图像的一致特征，设计了潜变量生成器分支和共显著目标检测转换器分支，最终通过基于Transformer的解码器生成鲁棒预测结果。构建了三个开放世界基准数据集OWCoSal、OWCoSOD和OWCoCA来评估模型的鲁棒性和实用性。

面向开放世界的共同显著目标检测与生成不确定性感知的群体选择性交换遮罩