Knowledge-based Visual Question Answering (KVQA) tasks require answering questions
about images using extensive background knowledge. Despite significant advancements …

Current multimodal Large Language Models (MLLMs) suffer from``hallucination'',
occasionally generating responses that are not grounded in the input images. To tackle this …

While large pre-trained visual-language models have shown promising results on traditional
visual question answering benchmarks, it is still challenging for them to answer complex …

Visual question answering (VQA) tasks, often performed by visual language model (VLM),
face challenges with long-tail knowledge. Recent retrieval-augmented VQA (RA-VQA) …

In recent times, Vision-Language Models (VLMs) have been trained under two predominant
paradigms. Generative training has enabled Multimodal Large Language Models (MLLMs) …

How humans can efficiently and effectively acquire images has always been a perennial
question. A typical solution is text-to-image retrieval from an existing database given the text …

With the rapid advancement of multimodal information retrieval, increasingly complex
retrieval tasks have emerged. Existing methods predominately rely on task-specific fine …

Multimodal LLMs (MLLMs) are the natural extension of large language models to handle
multimodal inputs, combining text and image data. They have recently garnered attention …

Multimodal Large Language Models (MLLMs) have demonstrated great zero-shot
performance on visual question answering (VQA). However, when it comes to knowledge …

The quest to develop artificial intelligence systems capable of handling intricate tasks has
propelled the prominence of deep learning, particularly since 2016, when neural network …