We propose a new setting that relaxes an assumption in the conventional Co-Salient Object Detection (CoSOD) setting by allowing the presence of "noisy images" which do not show the shared co-salient object. We call this new setting Generalised Co-Salient Object Detection (GCoSOD). We propose a novel random sampling based Generalised CoSOD Training (GCT) strategy to distill the awareness of inter-image absence of co-salient objects into CoSOD models. It employs a Diverse Sampling Self-Supervised Learning (DS3L) that, in addition to the provided supervised co-salient label, introduces additional self-supervised labels for noisy images (being null, that no co-salient object is present). Further, the random sampling process inherent in GCT enables the generation of a high-quality uncertainty map highlighting potential false-positive predictions at instance level. To evaluate the performance of CoSOD models under the GCoSOD setting, we propose two new testing datasets, namely CoCA-Common and CoCA-Zero, where a common salient object is partially present in the former and completely absent in the latter. Extensive experiments demonstrate that our proposed method significantly improves the performance of CoSOD models in terms of the performance under the GCoSOD setting as well as the model calibration degrees.

翻译：我们提出了一种新的设定，放宽了传统共显著目标检测（CoSOD）设定中的假设，允许存在不包含共享共显著目标的“噪声图像”。我们将这一新设定称为广义共显著目标检测（GCoSOD）。我们提出了一种基于随机采样的新型广义共显著目标检测训练（GCT）策略，旨在将图像间共显著目标缺失的意识融入CoSOD模型。该策略采用了多样化采样自监督学习（DS3L），在提供的监督共显著标签之外，为噪声图像引入额外的自监督标签（即空标签，表示不存在共显著目标）。此外，GCT中固有的随机采样过程能够生成高质量的置信度图，在实例级别突出显示潜在的错误阳性预测。为了在GCoSOD设定下评估CoSOD模型的性能，我们提出了两个新的测试数据集，即CoCA-Common和CoCA-Zero，其中前者部分包含共同显著目标，而后者完全不包含。大量实验表明，我们提出的方法在GCoSOD设定下的性能以及模型校准程度上显著提升了CoSOD模型的表现。