In the last few years, the deep learning (DL) computing paradigm has been deemed the
Gold Standard in the machine learning (ML) community. Moreover, it has gradually become …

In recent times, the machine learning (ML) community has recognized the deep learning
(DL) computing model as the Gold Standard. DL has gradually become the most widely …

Deep convolutional neural networks, assisted by architectural design strategies, make
extensive use of data augmentation techniques and layers with a high number of feature …

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 …

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

In many real-world problems, collecting a large number of labeled samples is infeasible.
Few-shot learning (FSL) is the dominant approach to address this issue, where the objective …

Representation learning with small labeled data have emerged in many problems, since the
success of deep neural networks often relies on the availability of a huge amount of labeled …

Abstract Deep Supervised Salient Object Detection (SSOD) excessively relies on large-
scale annotated pixel-level labels which consume intensive labour acquiring high quality …

Features that are equivariant to a larger group of symmetries have been shown to be more
discriminative and powerful in recent studies. However, higher-order equivariant features …