Recent advances in graph convolutional networks (GCNs), which mainly focus on how to
exploit information from different hops of neighbors in an efficient way, have brought …

This study carries out a systematic intrinsic evaluation of the semantic representations
learned by state-of-the-art pre-trained multimodal Transformers. These representations are …

Grounding language in vision is an active field of research seeking to construct cognitively
plausible word and sentence representations by incorporating perceptual knowledge from …

Multimodal embeddings aim to enrich the semantic information in neural representations of
language compared to text-only models. While different embeddings exhibit different …

For visual information processing, the derivation of meaningful low-level spatio-temporal
information is challenging. In line with human visualisation and perception in spatial …

Recent years have seen an exponential increase in unstructured data, primarily in the form
of text, images, and videos. Extracting useful features and trends from large-scale …

Due to the expeditious increase in the number of vehicles, there is an increase in the
number of road casualties even in a highly sophisticated roadway. This depicts the natural …

Semantic information is important in eye movement control. An important semantic influence
on gaze guidance relates to object-scene relationships: objects that are semantically …

Few-shot Learning (FSL) aims to classify new concepts from a small number of examples.
While there have been an increasing amount of work on few-shot object classification in the …

While the use of the distributional hypothesis has become popular in creating embedding for
text corpus, it is rarely used for generating the contextual (distributed) representation of …