Multimodal models that jointly process audio and language hold great promise in audio
understanding and are increasingly being adopted in the music domain. By allowing users …

The recent success of interleaved Large Multimodal Models (LMMs) in few-shot learning
suggests that in-context learning (ICL) with many examples can be promising for learning …

The rapid development of large language models (LLMs) has been witnessed in recent
years. Based on the powerful LLMs, multi-modal LLMs (MLLMs) extend the modality from …

Recently, rapid advancements in Multi-Modal In-Context Learning (MM-ICL) have achieved
notable success, which is capable of achieving superior performance across various tasks …

Multimodal Vision Language Models (VLMs) have emerged as a transformative technology
at the intersection of computer vision and natural language processing, enabling machines …

In this work, we propose a novel method (GLOV) enabling Large Language Models (LLMs)
to act as implicit Optimizers for Vision-Langugage Models (VLMs) to enhance downstream …

Recently, there has been a growing interest in Multimodal Large Language Models (MLLMs)
due to their remarkable potential in various tasks integrating different modalities, such as …

Sketching serves as a versatile tool for externalizing ideas, enabling rapid exploration and
visual communication that spans various disciplines. While artificial systems have driven …

Compositional Reasoning (CR) entails grasping the significance of attributes, relations, and
word order. Recent Vision-Language Models (VLMs), comprising a visual encoder and a …

As language models continue to scale, Large Language Models (LLMs) have exhibited
emerging capabilities in In-Context Learning (ICL), enabling them to solve language tasks …