We propose a new setting that relaxes the assumption in the conventional CoSOD setting by allowing the presence of \enquote{noisy images} which do not share the common 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 object into CoSOD models. It employs a Diverse Sampling Self-Supervised Learning (D$\text{S}^{3}$L) that, in addition to the provided supervised co-salient label, introduces additional self-supervised labels for 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：我们建议一个新的环境,通过允许存在不共享共同显要对象的 enquote{noisy 图像,放松常规COSOD 设置中的假设。我们称这个新设置为通用的通用共同天体探测(GCosOD)。我们建议采用基于通用的通用COSOD培训(GCT)的随机随机抽样战略,以在COSOD模型中提取缺乏共性对象的图象间缺乏共同对象的认识。它使用多种自我抽样学习(D$\ text{S%3}$L),除了提供受监督的共同天体标签外,还引入了额外的自监督图像标签(GoSODD)。此外,GCT中固有的随机抽样进程使得人们能够生成一个高品质的不确定性图,突出在COSOD模型中潜在的假阳性预测。为了评估COSOD模型的性能,我们建议采用两种新的测试数据集,即COCA-Comon和COCA-Zero,除了提供受监督的共同共同天体标签的标签之外,还增加了图像的自我监督的标签标签标签(没有共同天体物体);此外,根据GOS-CO-CODDODA的模型,我们提出的前的通用性能模型,在完全的性能试验中,部分的性能试验,在后期的模型中,在完全的性能试验中,在后期试验中,部分地试验。