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 …

Abstract Vision-Language Pre-training (VLP) models have shown promising capabilities in
grounding natural language in image data, facilitating a broad range of cross-modal tasks …

Unbiased SGG has achieved significant progress over recent years. However, almost all
existing SGG models have overlooked the ground-truth annotation qualities of prevailing …

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 …

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 …

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 …

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

Abstract Scene Graph Generation (SGG) aims to detect all the visual relation triplets< sub,
pred, obj> in a given image. With the emergence of various advanced techniques for better …