Modern day computer vision tasks requires efficient solution to problems such as image
recognition, natural language processing, object detection, object segmentation and …

Modern day computer vision tasks requires efficient solution to problems such as image
recognition, natural language processing, object detection, object segmentation and …

In this paper, we propose a Point Fractal Network (PF-Net), a novel learning-based
approach for precise and high-fidelity point cloud completion. Unlike existing point cloud …

In this paper, we propose 3D point-capsule networks, an auto-encoder designed to process
sparse 3D point clouds while preserving spatial arrangements of the input data. 3D capsule …

Abstract Objects are composed of a set of geometrically organized parts. We introduce an
unsupervised capsule autoencoder (SCAE), which explicitly uses geometric relationships …

Capsule Network is a promising concept in deep learning, yet its true potential is not fully
realized thus far, providing sub-par performance on several key benchmark datasets with …

The seen birds twitter, the running cars accompany with noise, etc. These naturally
audiovisual correspondences provide the possibilities to explore and understand the …

We propose a self-supervised capsule architecture for 3D point clouds. We compute capsule
decompositions of objects through permutation-equivariant attention, and self-supervise the …

Capsule networks have recently gained a great deal of interest as a new architecture of
neural networks that can be more robust to input perturbations than similar-sized CNNs …

The profound impact of deep learning and particularly of convolutional neural networks
(CNNs) in automatic image processing has been decisive for the progress and evolution of …