Visual Large Language Models for Generalized and Specialized Applications (2501.02765v1)

Abstract: Visual-LLMs (VLM) have emerged as a powerful tool for learning a unified embedding space for vision and language. Inspired by LLMs, which have demonstrated strong reasoning and multi-task capabilities, visual LLMs (VLLMs) are gaining increasing attention for building general-purpose VLMs. Despite the significant progress made in VLLMs, the related literature remains limited, particularly from a comprehensive application perspective, encompassing generalized and specialized applications across vision (image, video, depth), action, and language modalities. In this survey, we focus on the diverse applications of VLLMs, examining their using scenarios, identifying ethics consideration and challenges, and discussing future directions for their development. By synthesizing these contents, we aim to provide a comprehensive guide that will pave the way for future innovations and broader applications of VLLMs. The paper list repository is available: https://github.com/JackYFL/awesome-VLLMs.

• The paper presents a comprehensive survey of VLLMs that merge visual perception and language understanding using instruction-tuning techniques.
• It details applications in generalized tasks like image captioning and VQA, as well as specialized domains such as medical imaging and remote sensing.
• The study discusses future directions and ethical challenges, emphasizing model efficiency, bias mitigation, and interpretability for safe deployment.

Visual LLMs for Generalized and Specialized Applications

This paper provides a comprehensive survey of Visual LLMs (VLLMs), a burgeoning field that integrates the capabilities of visual understanding with the reasoning and language proficiency of LLMs. The work highlights the current landscape of VLLMs, emphasizing their applications across generalized and specialized domains, and discusses future directions and ethical considerations pertinent to the development of these models.

Overview of VLLMs

The paper elaborates on the architecture and functionality of VLLMs, which aim to unify vision and language by leveraging instruction-tuning techniques from LLMs. These models use a vision encoder to process visual data, a connector to map visual tokens into a language space, and an LLM to generate language outputs. This architecture facilitates both generalized applications—such as image classification, object segmentation, and visual question answering (VQA)—and specialized applications in fields like medicine and remote sensing.

Generalized Applications

1. Image-to-Text Tasks: VLLMs have been successfully applied to image captioning, VQA, and visual dialogue, where they analyze images and generate descriptive text or answer questions about the visual content. The paper details several models like LLaVA and MiniGPT-4, which effectively use instruction-tuning datasets to enhance their visual and language comprehension capabilities.
2. Referring Expressions and Segmentation: Tasks such as Referring Expression Segmentation (RES) and Referring Expression Comprehension (REC) are explored, where models like LISA and GLaMM utilize advanced mechanisms to locate and describe objects based on textual queries. These tasks demonstrate the models' proficiency in integrating visual and linguistic information to perform complex segmentation tasks.
3. Text Recognition and Retrieval: VLLMs are adept at Optical Character Recognition (OCR), utilizing robust architectures to handle high-resolution, text-rich images. They are also employed in retrieval tasks, where they map and align visual and textual data to enhance information retrieval processes.

Specialized Applications

1. Remote Sensing and Medical Imaging: The paper illustrates VLLMs' capabilities in specialized domains. In remote sensing, models handle diverse image types and applications like RS captioning and VQA. In the medical field, VLLMs assist in diagnostic tasks by interpreting medical images and generating reports, showing promise in tasks like Medical VQA and report generation.
2. Science, Mathematics, and More: In educational and research applications, VLLMs tackle Science VQA and visual mathematics by interpreting scientific diagrams and complex mathematical visualizations, enhancing the learning and discovery process.

Core Challenges and Ethical Considerations

The paper identifies several challenges facing VLLMs, including biases in training data, efficiency constraints in training and inference, and issues related to interpretability and hallucinations. VLLMs often require large-scale datasets, which can inadvertently introduce gender, racial, and lingual biases. Additionally, the models demand significant computational resources, posing efficiency challenges.

Ethical considerations are paramount, especially concerning the impact of VLLMs on labor markets and potential misuse or biases in critical applications like autonomous driving or medical diagnosis. Ensuring data privacy and robust safety measures against adversarial attacks are also crucial aspects discussed in the paper.

Implications and Future Directions

The paper anticipates that VLLMs will play a transformative role in AI, with potential expansions into areas such as embodied AI and automated tool management. Future research is encouraged to focus on enhancing model efficiency, mitigating biases, and improving interpretability. Additionally, exploring complex reasoning capabilities and ensuring ethical model deployment will be crucial as these models continue to evolve.

VLLMs signify a pivotal advancement in multimodal AI systems, promising to enrich a wide array of applications by combining complex visual processing with advanced language capabilities. This paper provides a crucial foundation for understanding the current scope and future potential of VLLMs, guiding further research and innovation in the integration of vision and language.