Graph neural networks provide a powerful toolkit for embedding real-world graphs into low-
dimensional spaces according to specific tasks. Up to now, there have been several surveys …

Graph Neural Networks (GNNs) have gained momentum in graph representation learning
and boosted the state of the art in a variety of areas, such as data mining (\emph {eg,} social …

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 …

Abstract Large-scale Vision-Language Models, such as CLIP, learn powerful image-text
representations that have found numerous applications, from zero-shot classification to text …

Abstract 3D visual grounding aims to find the object within point clouds mentioned by free-
form natural language descriptions with rich semantic cues. However, existing methods …

For robots to understand human instructions and perform meaningful tasks in the near
future, it is important to develop learned models that comprehend referential language to …

Visual grounding focuses on establishing fine-grained alignment between vision and natural
language, which has essential applications in multimodal reasoning systems. Existing …

Given an untrimmed video and a query sentence, cross-modal video moment retrieval aims
to rank a video moment from pre-segmented video moment candidates that best matches …

Abstract 3D visual grounding task has been explored with visual and language streams to
comprehend referential language for identifying targeted objects in 3D scenes. However …

Abstract 3D object grounding aims to locate the most relevant target object in a raw point
cloud scene based on a free-form language description. Understanding complex and …