Visual Large Language Models for Generalized and Specialized Applications
Yifan Li, Zhixin Lai, Wentao Bao, Zhen Tan, Anh Dao, Kewei Sui, Jiayi Shen, Dong Liu, Huan Liu, Yu Kong·January 06, 2025
Summary
Vision Large Language Models (VLLMs) are advanced tools for unified vision and language processing, inspired by large language models. They excel in diverse applications across vision, action, and language modalities, offering generalized and specialized solutions. Despite significant progress, comprehensive application perspectives are limited. This survey explores VLLMs' diverse applications, ethical considerations, challenges, and future directions, aiming to guide future innovations and broader VLLM applications.
VLLMs process versatile instructions, generating human-aligned responses using a vision encoder, connector, and LLM decoder. They are categorized into vision-to-text, vision-to-action, and text-to-vision, advancing across diverse tasks, impacting society. Challenges include security, privacy, efficiency, interpretability, and complex reasoning. Future development focuses on addressing these challenges.
The text discusses advancements in image captioning, referring expression comprehension, and segmentation, highlighting models like LLaVA, MiniGPT-4, and mPLUG-Owl, used in domains such as general, medical, science, finance, and more. The taxonomy includes subcategories for text-to-image, text-to-3D, and text-to-video tasks. Models are trained using supervised fine-tuning on high-quality datasets, enhancing their ability to generate coherent textual descriptions from visual inputs.
The text also covers advancements in Latex class files, focusing on token-based detection, Optical Character Recognition (OCR), and retrieval tasks in Vision-Language Models (VLLMs). It discusses the challenges in bridging the semantic gap between sparse video content and abstract language information, with models like VideoBERT and LLMs built using large-scale pre-training on visual-text data.
VLLMs enhance autonomous driving and robotics, with models like DriveVLM, DiLu, and DriveGPT4 improving perception, planning, and prediction tasks. They process various visual inputs like images, videos, depth, and 3D data, generating actions for vehicles, robots, and software.
The text discusses advancements in text-to-vision applications, focusing on image synthesis, visual story generation, text-to-3D, and text-to-video. Key advancements include seamless image blending, coherent story sequences, detailed 3D model generation, and realistic video creation.
The text also covers advancements in dataset expansion, parameter-efficient fine-tuning, inference efficiency, and interpretability, highlighting the need for more VLLM-specific parameter-efficient fine-tuning methods, token reduction techniques, and KV cache optimization. The text emphasizes the importance of improving interpretability and explainability in VLLMs, addressing challenges such as hallucination and alignment with perception.
VLLMs are advancing across various domains, including medical, science, finance, remote sensing, and more, addressing challenges like color distribution, resolution, context, and scale in remote sensing. They are integrated for diagram and graph understanding, enhancing tasks like science VQA and science image captioning.
The text discusses advancements in using VLLMs for robotics and AI, focusing on tasks like navigation, manipulation, and instruction following. Key areas include multimodal large language models, open-source frameworks, and tools for enhancing AI's understanding and interaction with the physical world.
VLLMs are also advancing in multimodal understanding, with models like Honeybee, Vary, Internvl, Deepseek-vl, and others addressing captioning, retrieval, and understanding of remote sensing images, medical images, and scientific diagrams. The models are trained using large language models and tuned with instructions for tasks such as visual question answering, radiograph summarization, and mathematical reasoning.
The text summarizes recent advancements in adapting pre-trained models for video-language representation, video representation learning, large multimodal agents, and their applications in robotics, navigation, and tool learning. It also discusses surveys on vision-language-action models, multimodal agents, and evaluation benchmarks for multimodal agents.
VLLMs are advancing in multimodal chain-of-thought reasoning and prompting techniques, enhancing reasoning capabilities in large models. These studies explore various applications, such as detection, emotion understanding, facial expression recognition, and video anomaly detection, aiming to improve model interpretability and performance in open-world scenarios.
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