Generalised Co-Salient Object Detection

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