  Co-salient object detection, with the target of detecting co-existed salient
objects among a group of images, is gaining popularity. Recent works use the
attention mechanism or extra information to aggregate common co-salient
features, leading to incomplete even incorrect responses for target objects. In
this paper, we aim to mine comprehensive co-salient features with democracy and
reduce background interference without introducing any extra information. To
achieve this, we design a democratic prototype generation module to generate
democratic response maps, covering sufficient co-salient regions and thereby
involving more shared attributes of co-salient objects. Then a comprehensive
prototype based on the response maps can be generated as a guide for final
prediction. To suppress the noisy background information in the prototype, we
propose a self-contrastive learning module, where both positive and negative
pairs are formed without relying on additional classification information.
Besides, we also design a democratic feature enhancement module to further
strengthen the co-salient features by readjusting attention values. Extensive
experiments show that our model obtains better performance than previous
state-of-the-art methods, especially on challenging real-world cases (e.g., for
CoCA, we obtain a gain of 2.0% for MAE, 5.4% for maximum F-measure, 2.3% for
maximum E-measure, and 3.7% for S-measure) under the same settings. Code will
be released soon.
