Scene graph is a structured representation of a scene that can clearly express the objects,
attributes, and relationships between objects in the scene. As computer vision technology …

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 …

Existing research addresses scene graph generation (SGG)—a critical technology for scene
understanding in images—from a detection perspective, ie., objects are detected using …

Learning 3D human pose prior is essential to human-centered AI. Here, we present GFPose,
a versatile framework to model plausible 3D human poses for various applications. At the …

Different objects in the same scene are more or less related to each other, but only a limited
number of these relationships are noteworthy. Inspired by Detection Transformer, which …

Abstract Scene Graph Generation, which generally follows a regular encoder-decoder
pipeline, aims to first encode the visual contents within the given image and then parse them …

Abstract Current Scene Graph Generation (SGG) methods explore contextual information to
predict relationships among entity pairs. However, due to the diverse visual appearance of …

Abstract Scene Graph Generation (SGG) remains a challenging visual understanding task
due to its compositional property. Most previous works adopt a bottom-up two-stage or a …

Towards building comprehensive real-world visual perception systems, we propose and
study a new problem called panoptic scene graph generation (PVSG). PVSG is related to …

Scene graph generation (SGG) is designed to extract (subject, predicate, object) triplets in
images. Recent works have made a steady progress on SGG, and provide useful tools for …