Visual relationship detection aims to identify objects and their relationships in images. Prior
methods approach this task by adding separate relationship modules or decoders to existing …

The scene graph generation (SGG) task involves detecting objects within an image and
predicting predicates that represent the relationships between the objects. However, in SGG …

This work introduces an enhanced approach to generating scene graphs by incorporating
both a relationship hierarchy and commonsense knowledge. Specifically, we begin by …

This paper advances contextual image understanding within perspective-aware Ai (PAi), an
emerging paradigm in human–computer interaction that enables users to perceive and …

Recently, interpreting complex charts with logical reasoning have emerged as challenges
due to the development of vision-language models. A prior state-of-the-art (SOTA) model …

Visual-textual inconsistency (VTI) evaluation plays a crucial role in cleansing vision-
language data. Its main challenges stem from the high variety of image captioning datasets …

Multimodal LLMs have advanced vision-language tasks but still struggle with understanding
video scenes. To bridge this gap, Video Scene Graph Generation (VidSGG) has emerged to …

Video scene graph generation (VidSGG) has emerged as a transformative approach to
capturing and interpreting the intricate relationships among objects and their temporal …

Locating objects referred to in natural language poses a significant challenge for
autonomous agents. Existing CLIP-based open-vocabulary methods successfully perform …

Spatio-Temporal Scene Graphs (STSGs) provide a concise and expressive representation
of dynamic scenes by modelling objects and their evolving relationships over time. However …