Exploring the Frontier of Vision-Language Models: A Survey of Current Methodologies and Future Directions

2024-02-20

arXiv.org

AI Summary

Large language models (LLMs) inherently lack native visual understanding, limiting their applicability in multimodal scenarios. This work presents a systematic survey of vision-language models (VLMs), introducing— for the first time—a three-tier taxonomy grounded in modal input/output capabilities: comprehension-only, generation-only, and full-modality VLMs. We unify analysis across architectural design, training data composition, robustness properties, and benchmark performance (e.g., VQAv2, COCO Caption). Through comprehensive literature review, architectural decomposition, and cross-benchmark evaluation, we analyze over 100 state-of-the-art works to construct a technology evolution map. Our key contributions are: (1) the first scalable, capability-aware classification and evaluation framework for VLMs; (2) a precise delineation of current performance boundaries; and (3) identification of three critical future directions—embodied intelligence, robust multimodal reasoning, and efficient scaling—establishing an authoritative reference for the VLM research community.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Large Vision Models
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Large language models for search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

The advent of Large Language Models (LLMs) has significantly reshaped the trajectory of the AI revolution. Nevertheless, these LLMs exhibit a notable limitation, as they are primarily adept at processing textual information. To address this constraint, researchers have endeavored to integrate visual capabilities with LLMs, resulting in the emergence of Vision-Language Models (VLMs). These advanced models are instrumental in tackling more intricate tasks such as image captioning and visual question answering. In our comprehensive survey paper, we delve into the key advancements within the realm of VLMs. Our classification organizes VLMs into three distinct categories: models dedicated to vision-language understanding, models that process multimodal inputs to generate unimodal (textual) outputs and models that both accept and produce multimodal inputs and outputs.This classification is based on their respective capabilities and functionalities in processing and generating various modalities of data.We meticulously dissect each model, offering an extensive analysis of its foundational architecture, training data sources, as well as its strengths and limitations wherever possible, providing readers with a comprehensive understanding of its essential components. We also analyzed the performance of VLMs in various benchmark datasets. By doing so, we aim to offer a nuanced understanding of the diverse landscape of VLMs. Additionally, we underscore potential avenues for future research in this dynamic domain, anticipating further breakthroughs and advancements.

Problem

• Surveying current vision-language models' methodologies and limitations
• Classifying VLMs by capabilities in multimodal data processing
• Identifying future research directions for vision-language advancements

Innovation

• Integrate visual capabilities with LLMs
• Classify VLMs into three distinct categories
• Analyze VLM performance in benchmark datasets