We present a novel group collaborative learning framework (GCoNet) capable of
detecting co-salient objects in real time (16ms), by simultaneously mining
consensus representations at group level based on the two necessary criteria:
1) intra-group compactness to better formulate the consistency among co-salient
objects by capturing their inherent shared attributes using our novel group
affinity module; 2) inter-group separability to effectively suppress the
influence of noisy objects on the output by introducing our new group
collaborating module conditioning the inconsistent consensus. To learn a better
embedding space without extra computational overhead, we explicitly employ
auxiliary classification supervision. Extensive experiments on three
challenging benchmarks, i.e., CoCA, CoSOD3k, and Cosal2015, demonstrate that
our simple GCoNet outperforms 10 cutting-edge models and achieves the new
state-of-the-art. We demonstrate this paper's new technical contributions on a
number of important downstream computer vision applications including content
aware co-segmentation, co-localization based automatic thumbnails, etc.

本研究提出一种新的团体协作学习框架，名为 GCoNet，可以实时（16ms）检测协同显著对象，同时采用两个必要标准，即群体内紧凑性和群体间可分性来挖掘共识表征；此外，为了学习更好的嵌入空间，我们明确地采用辅助分类监督。实验证明，我们的 GCoNet 模型在多个重要的计算机视觉应用中表现出色。