6 accepted papers
Visual Question Answering (VQA) necessitates models to reason effectively across visual and textual modalities. However, existing Large Vision-Language Models (LVLMs) often fall short in achieving human-like reasoning due to a lack of integrated commonsense knowledge, limiting their robustness and a…
Existing Multimodal Large Language Models (MLLMs) and Visual Language Pretrained Models (VLPMs) have shown remarkable performances in general Visual Question Answering (VQA). However, these models struggle with VQA questions that require external commonsense knowledge due to the challenges in genera…
Out-of-distribution (OOD) detection in multimodal contexts is essential for identifying deviations in different modalities, particularly for interactive dialogue systems in real-life interactions, where the systems are usually infeasible to deploy large language models (LLMs) to generate dialogue re…
Document Question Answering (QA) presents a challenge in understanding visually-rich documents (VRD), particularly with lengthy textual content. Existing studies primarily focus on real-world documents with sparse text, while challenges persist in comprehending the hierarchical semantic relations am…
Recognizing the layout of unstructured digital documents is crucial when parsing the documents into the structured, machine-readable format for downstream applications. Recent studies in Document Layout Analysis usually rely on visual cues to understand documents while ignoring other information, su…
Text-to-image multimodal tasks, generating/retrieving an image from a given text description, are extremely challenging tasks since raw text descriptions cover quite limited information in order to fully describe visually realistic images. We propose a new visual contextual text representation for t…