Capsule Networks With Residual Pose Routing.

Capsule networks (CapsNets) have been known difficult to develop a deeper architecture, which is desirable for high performance in the deep learning era, due to the complex capsule routing algorithms. In this article, we present a simple yet effective capsule routing algorithm, which is presented by a residual pose routing. Specifically, the higher-layer capsule pose is achieved by an identity mapping on the adjacently lower-layer capsule pose.

FAFuse: A Four-Axis Fusion framework of CNN and Transformer for medical image segmentation.

Medical image segmentation is crucial for accurate diagnosis and treatment in the medical field. In recent years, convolutional neural networks (CNNs) and Transformers have been frequently adopted as network architectures in medical image segmentation. The convolution operation is limited in modeling long-range dependencies because it can only extract local information through the limited receptive field.

Disentangled Capsule Routing for Fast Part-Object Relational Saliency.

Recently, the Part-Object Relational (POR) saliency underpinned by the Capsule Network (CapsNet) has been demonstrated to be an effective modeling mechanism to improve the saliency detection accuracy. However, it is widely known that the current capsule routing operations have huge computational complexity, which seriously limited the usability of the POR saliency models in real-time applications. To this end, this paper takes an early step towards a fast POR saliency inference by proposing a novel disentangled part-object relational network.

Learning Discriminative Cross-Modality Features for RGB-D Saliency Detection.

How to explore useful information from depth is the key success of the RGB-D saliency detection methods. While the RGB and depth images are from different domains, a modality gap will lead to unsatisfactory results for simple feature concatenation. Towards better performance, most methods focus on bridging this gap and designing different cross-modal fusion modules for features, while ignoring explicitly extracting some useful consistent information from them.