Multiview Rank Pooling for 3D Object Recognition** 3D shape recognition via deep learning is drawing more and more attention due to huge industry interests. As 3D deep learning methods emerged, the viewbased approaches have gained considerable success in object classification. Most of these methods focus on designing a pooling scheme to aggregate CNN features of multiview images into a single compact one. However, these viewwise pooling techniques suffer from loss of visual information. To deal with this issue, an adaptive rank pooling layer is introduced in this paper. Unlike maxpooling which only considers the maximum or meanpooling that treats each element indiscriminately, the proposed pooling layer takes all the elements into account and dynamically adjusts their importances during the training. Experiments conducted on ModelNet40 and ModelNet10 shows both efficiency and accuracy gain when inserting such a layer into a baseline CNN architecture.

GradientBased Pooling for Convolutional Neural Networks Pooling layers are an important part of convolutional neural networks (CNNs). They reduce the dimensionality of feature maps and pass salient information to subsequent layers. In this paper, we introduce a novel gradientbased feature pooling method that can downsample feature maps while better preserving key information. This method considers the spatial gradient of the pixels within a pooling region as a key to select the most possible descriptive information in contrast to the current practice of existing methods that mostly rely on the pixel values. Extensive experiments on different benchmark image classification tasks and CNN architectures demonstrate that the proposed method achieves superior results over existing pooling approaches.