Abstract 2D image understanding is a complex problem within computer vision, but it holds
the key to providing human-level scene comprehension. It goes further than identifying the …

Knowledge retrieval with multi-modal queries plays a crucial role in supporting knowledge-
intensive multi-modal applications. However, existing methods face challenges in terms of …

Visual question answering (VQA) often requires an understanding of visual concepts and
language semantics, which relies on external knowledge. Most existing methods exploit pre …

Visual question answering (VQA) lies at the intersection of language and vision research. It
functions as a building block for multimodal conversational AI and serves as a testbed for …

The domain of joint vision-language understanding, especially in the context of reasoning in
Visual Question Answering (VQA) models, has garnered significant attention in the recent …

Alternatively inferring on the visual facts and commonsense is fundamental for an advanced
visual question answering (VQA) system. This ability requires models to go beyond the …

In today's rapidly evolving digital landscape, the wealth of available information has
expanded beyond the boundaries of traditional text-based content. With the proliferation of …

In this paper, we propose a Retriever-Reader framework with Visual Entity Linking (RR-VEL)
for knowledge-based visual question answering. Given images and original questions, the …

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

There is a wide connection between linguistics and artificial intelligence (AI), including the
multimodal language matching. Multi-modal robots possess the capability to process various …