Graph neural networks (GNNs), as a branch of deep learning in non-Euclidean space,
perform particularly well in various tasks that process graph structure data. With the rapid …

Human knowledge provides a formal understanding of the world. Knowledge graphs that
represent structural relations between entities have become an increasingly popular …

Spurred by advancements in scale, large language models (LLMs) have demonstrated the
ability to perform a variety of natural language processing (NLP) tasks zero-shot--ie, without …

With the rise of powerful pre-trained vision-language models like CLIP, it becomes essential
to investigate ways to adapt these models to downstream datasets. A recently proposed …

Abstract Out-of-distribution (OOD) detection refers to training the model on in-distribution (ID)
dataset to classify if the input images come from unknown classes. Considerable efforts …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

Open-world object detection, as a more general and challenging goal, aims to recognize
and localize objects described by arbitrary category names. The recent work GLIP …

Visual recognition is recently learned via either supervised learning on human-annotated
image-label data or language-image contrastive learning with webly-crawled image-text …

We aim at advancing open-vocabulary object detection, which detects objects described by
arbitrary text inputs. The fundamental challenge is the availability of training data. It is costly …

This paper provides an extensive overview of the use of knowledge graphs in the context of
Explainable Machine Learning. As of late, explainable AI has become a very active field of …