Transformer is a promising neural network learner, and has achieved great success in
various machine learning tasks. Thanks to the recent prevalence of multimodal applications …

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

Deep learning techniques have led to remarkable breakthroughs in the field of object
detection and have spawned a lot of scene-understanding tasks in recent years. Scene …

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 …

Abstract Relational Language-Image Pre-training (RLIP) aims to align vision representations
with relational texts, thereby advancing the capability of relational reasoning in computer …

Scene graph generation (SGG) aims to abstract an image into a graph structure, by
representing objects as graph nodes and their relations as labeled edges. However, two …

A comprehensive representation of an image requires understanding objects and their
mutual relationship, especially in image-to-graph generation, eg, road network extraction …

The task of dynamic scene graph generation (SGG) from videos is complicated and
challenging due to the inherent dynamics of a scene, temporal fluctuation of model …

We are living in a three-dimensional space while moving forward through a fourth
dimension: time. To allow artificial intelligence to develop a comprehensive understanding …