#Multimodal Learning

This article examines multimodal learning facilitated by large language models (LLMs), focusing on diverse modalities such as text, vision, and audio. It underscores the role of open-source, research-supportive LLM backbones like LLaMA, Alpaca, and Bloom and reviews various learning techniques including fine-tuning and in-context learning. Examples of models such as OpenFlamingo and MiniGPT-4 are discussed alongside evaluation methods like MultiInstruct and POPE. The article highlights key research advancements from 2021 to 2023, offering insights into projects enhancing LLM visual and language processing capabilities. It also provides resources and guidelines for contributors to encourage ongoing exploration and progress in LLM-guided multimodal learning.

This objective overview examines how large language models (LLMs) integrate with graph data structures, exploring their applications in areas such as academic and e-commerce networks. It addresses whether LLMs' proven text-based reasoning extends to graph-based reasoning, impacting fields like network analysis and molecular modeling. Providing insights into predictors, encoders, and aspects such as data augmentation and prediction alignment, this repository continuously updates, becoming essential for understanding the evolving applications of LLMs in graph data.

Ferret is a comprehensive Multimodal Large Language Model (MLLM) designed for various referring and grounding tasks. Innovations include Hybrid Region Representation and Spatial-aware Visual Sampler. It comes with the GRIT Dataset for robust instruction tuning and the Ferret-Bench evaluation benchmark. Components like Ferret-UI and model checkpoints illustrate its proficiency in handling complex tasks, serving the research community within the scope of licensing agreements.

This survey systematically categorizes key diffusion model research papers, as detailed in the paper accepted by ACM Computing Surveys. The repository highlights innovations in sampling acceleration, noise schedule optimization, and handling data with intricate structures. It covers extensive applications in fields like computer vision and natural language processing, focusing on advancements such as text-to-image generation and anomaly detection. Additionally, the survey explores the integration of diffusion models with other generative models and their impact on multi-modal learning.