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 (eg, social network …

Mapping the environment is a powerful technique for enabling autonomy through
localization and planning in robotics. This article seeks to provide a global overview of …

3D spatial perception is the problem of building and maintaining an actionable and
persistent representation of the environment in real-time using sensor data and prior …

Panoptic Scene Graph (PSG) is a challenging task in Scene Graph Generation (SGG) that
aims to create a more comprehensive scene graph representation using panoptic …

Abstract Panoptic Scene Graph generation (PSG) is a recently proposed task in image
scene understanding that aims to segment the image and extract triplets of subjects, objects …

While recent vision-and-language models (VLMs) like CLIP are a powerful tool for analyzing
text and images in a shared semantic space, they do not explicitly model the hierarchical …

Answering questions about complex situations in videos requires not only capturing of the
presence of actors, objects, and their relations, but also the evolution of these relationships …

Artificial intelligence (AI) is expected to be embodied in software agents, robots, and cyber-
physical systems that can understand the various contextual information of daily life in the …

Scene graph generation (SGG) models seek to detect relationships between objects in a
given image. One challenge in this area is the biased distribution of predicates in the dataset …

Hallucination issues persistently plagued current multimodal large language models
(MLLMs). While existing research primarily focuses on object-level or attribute-level …