Existing information retrieval (IR) models often assume a homogeneous format, limiting their
applicability to diverse user needs, such as searching for images with text descriptions …

State-of-the-art retrieval models typically address a straightforward search scenario, where
retrieval tasks are fixed (eg, finding a passage to answer a specific question) and only a …

Recent advancements in image-text matching have been notable, yet prevailing models
predominantly cater to broad queries and struggle with accommodating fine-grained query …

The integration of Retrieval-Augmented Generation (RAG) with Multimodal Large Language
Models (MLLMs) has expanded the scope of multimodal query resolution. However, current …

State-of-the-art retrieval models typically address a straightforward search scenario, where
retrieval tasks are fixed (eg, finding a passage to answer a specific question) and only a …

Multimodal retrieval is becoming increasingly vital as media platforms often feature content
combining text and images. This is especially prevalent in long-form, information-rich …