- In section 3.1, the logic of extending HOGA from second order is not consistent with the extension from first order to second order; i.e., second order attention creates one more intermediate state U compared to the first order attention module. However, from the second order to higher order attention module, although intermediate states U0, U1 … are created, they are only part of the intermediate feature (Concatenating them will form U with full channel resolution). In this way, it seems we could regard the higher order attention module as a special form of second order attention module. - The paper does not clearly explain the intuition as to why different channel groups should have different attention mechanisms; i.e., in what specific way the network can benefit from the proposed channel group specific attention module. - Experiments are not solid enough: 1. There are no ablation studies on the effect of parameter numbers, so it is not clear whether the performance gain is due to the proposed approach or additional parameters. 2. Although there is good performance on imageNet classification with ResNet50/34/18, there are no results with larger models like ResNet101/152. 3. There are no results using strong object detection frameworks; the current SSD framework is relatively weak (e.g. Faster RCNN would be a stronger, more standard approach); it is not clear whether the improvements would be retained with a stronger base framework. - The proposed approach requires larger FLOPS compared to baselines; i.e., any performance gain requires large computation overhead (this is particularly pronounced in Table 3). - In Table 3 shows ResNet32/56 but L222 refers to ResNet34/50, which is confusing.
2. Although there is good performance on imageNet classification with ResNet50/34/18, there are no results with larger models like ResNet101/152.