Over the past few years, the computer vision domain has evolved and made a revolutionary
transition from human-engineered features to automated features to address challenging …

Learning object-centric representations of complex scenes is a promising step towards
enabling efficient abstract reasoning from low-level perceptual features. Yet, most deep …

Capsule networks emerged as a promising alternative to convolutional neural networks for
learning object-centric representations. The idea is to explicitly model part-whole hierarchies …

In humans, Attention is a core property of all perceptual and cognitive operations. Given our
limited ability to process competing sources, attention mechanisms select, modulate, and …

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

Zero-shot learning (ZSL) aims to learn models that can recognize unseen image semantics
based on the training of data with seen semantics. Recent studies either leverage the global …

The success of deep convolutional neural networks builds on top of the learning of effective
convolution operations, capturing a hierarchy of structured features via filtering, activation …

Records in a table are represented by a collection of heterogeneous scalar features.
Previous work often made predictions for records in a paradigm that processed each feature …

Fake media is spreading like wildfire all over the internet as a result of the great
advancement in deepfake creation tools and the huge interest researchers and corporations …

Abstract Few-shot Knowledge Graph Completion (FKGC) has recently attracted significant
research interest due to its ability to expand few-shot relation coverage in Knowledge …