In recent years, Knowledge Graphs (KGs) have played a crucial role in the development of
advanced knowledge-intensive applications, such as recommender systems and semantic …

Cross-modal matching, which aims to establish the correspondence between two different
modalities, is fundamental to a variety of tasks such as cross-modal retrieval and vision-and …

Profiting from large-scale training datasets, advances in neural architecture design and
efficient inference, joint embeddings have become the dominant approach for tackling cross …

This work studies the use of visual semantic representations to align entities in
heterogeneous knowledge graphs (KGs). Images are natural components of many existing …

Abstract Image-Text Retrieval (ITR) is challenging in bridging visual and lingual modalities.
Contrastive learning has been adopted by most prior arts. Except for limited amount of …

Entity alignment is critical for multiple knowledge graphs (KGs) integration. Although
researchers have made significant efforts to explore the relational embeddings between …

Building 3D scene graphs has recently emerged as a topic in scene representation for
several embodied AI applications to represent the world in a structured and rich manner …

Existing image-text matching approaches typically leverage triplet loss with online hard
negatives to train the model. For each image or text anchor in a training mini-batch, the …

Cross-modal metric learning is a prominent research topic that bridges the semantic
heterogeneity between vision and language. Existing methods frequently utilize simple …

In this work, we present a post-processing solution to address the hubness problem in cross-
modal retrieval, a phenomenon where a small number of gallery data points are frequently …