#Vision-Language

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 collection provides a wide range of papers on contrastive self-supervised learning, useful for scholars and industry professionals. Regular updates ensure coverage of various topics such as topic modeling, vision-language representation, 3D medical image analysis, and multimodal sentiment analysis. Each paper entry includes links to the paper and code, if available, facilitating access to cutting-edge methods and experimental setups. Well-suited for those aiming to enhance their understanding of recent progress in contrastive learning, this collection serves as an essential reference for its comprehensive scope and pertinence.

DeepSeek-VL is an open-source model optimized for real-world vision-language applications, adept at handling complex multimodal data such as logical diagrams and natural images. Available in various sizes, it caters to both academic and commercial research needs. Explore its features through a demo on Hugging Face, licensed under MIT and available in both base and chat versions.

The platform presents a comprehensive overview of the application of multimodal large language models (MLLMs) in remote sensing. It includes carefully curated resources like model architectures, training processes, datasets, and evaluation benchmarks. The site continuously updates to reflect the latest in the field, emphasizing intelligent agents, instruction tuning, and the vision-language interface, while documenting contributions from numerous researchers to provide the latest on remote sensing MLLMs.