text: Paper presents an interesting attention mechanism for fine-grained image classification. Introduction states that the method is simple and easy to understand. However, the presentation of the method is bit harder to follow. It is not clear to me if the attention modules are applied over all  pooling layers. How they are combined? Why use cross -correlation as the regulariser? Why not much stronger constraint such as orthogonality over elements of M in equation 1? What is the impact of this regularisation? Why use soft-max in equation 1? One may use a Sigmoid as well? Is it better to use soft-max? Equation 9 is not entirely clear to me. Undefined notations. In Table 2, why stop from AD= 2 and AW=2? What is the performance of AD=1, AW=1 with G? Why not perform this experiment over all 5 datasets? Is this performances, dataset specific? The method is compared against 5 datasets. Obtained results are quite good.