This paper proposes a novel network module to exploit global (non-local) correlations in the feature map for improving ConvNets. The authors focus on the weakness of the non-local (NL) module [31] that the correlations across channels are less taken into account, and then formulate the compact generalized non-local (CGNL) module to remedy the issue through summarizing the previous methods of NL and bilinear pooling [14] in a unified manner. The CGNL is evaluated on thorough experiments for action and fine-grained classification tasks, exhibiting promising performance competitive to the state-of-the-arts. Positives: The paper is well organized and easy to follow. The generalized formulation (8,9) to unify bilinear pooling and non-local module is theoretically sound.