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

• URL: http://arxiv.org/abs/2404.07214v2
• Date: Fri, 12 Apr 2024 21:20:37 GMT
• Title: Exploring the Frontier of Vision-Language Models: A Survey of Current Methodologies and Future Directions
• Authors: Akash Ghosh, Arkadeep Acharya, Sriparna Saha, Vinija Jain, Aman Chadha,
• Abstract summary: Vision-Language Models (VLMs) are advanced models that can tackle more intricate tasks such as image captioning and visual question answering. 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. 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.
• Score: 11.786387517781328
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• RA-BLIP: Multimodal Adaptive Retrieval-Augmented Bootstrapping Language-Image Pre-training [55.54020926284334]
  Multimodal Large Language Models (MLLMs) have recently received substantial interest, which shows their emerging potential as general-purpose models for various vision-language tasks. Retrieval augmentation techniques have proven to be effective plugins for both LLMs and MLLMs. In this study, we propose multimodal adaptive Retrieval-Augmented Bootstrapping Language-Image Pre-training (RA-BLIP), a novel retrieval-augmented framework for various MLLMs.
  arXiv  Detail & Related papers  (2024-10-18T03:45:19Z)
• A Comprehensive Review of Multimodal Large Language Models: Performance and Challenges Across Different Tasks [74.52259252807191]
  Multimodal Large Language Models (MLLMs) address the complexities of real-world applications far beyond the capabilities of single-modality systems. This paper systematically sorts out the applications of MLLM in multimodal tasks such as natural language, vision, and audio.
  arXiv  Detail & Related papers  (2024-08-02T15:14:53Z)
• Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs [56.391404083287235]
  We introduce Cambrian-1, a family of multimodal LLMs (MLLMs) designed with a vision-centric approach. Our study uses LLMs and visual instruction tuning as an interface to evaluate various visual representations. We provide model weights, code, supporting tools, datasets, and detailed instruction-tuning and evaluation recipes.
  arXiv  Detail & Related papers  (2024-06-24T17:59:42Z)
• LLMs Meet Multimodal Generation and Editing: A Survey [89.76691959033323]
  This survey elaborates on multimodal generation and editing across various domains, comprising image, video, 3D, and audio. We summarize the notable advancements with milestone works in these fields and categorize these studies into LLM-based and CLIP/T5-based methods. We dig into tool-augmented multimodal agents that can leverage existing generative models for human-computer interaction.
  arXiv  Detail & Related papers  (2024-05-29T17:59:20Z)
• A Survey of Multimodal Large Language Model from A Data-centric Perspective [46.57232264950785]
  Multimodal large language models (MLLMs) enhance the capabilities of standard large language models by integrating and processing data from multiple modalities. Data plays a pivotal role in the development and refinement of these models.
  arXiv  Detail & Related papers  (2024-05-26T17:31:21Z)
• A Review of Multi-Modal Large Language and Vision Models [1.9685736810241874]
  Large Language Models (LLMs) have emerged as a focal point of research and application. Recently, LLMs have been extended into multi-modal large language models (MM-LLMs) This paper provides an extensive review of the current state of those LLMs with multi-modal capabilities as well as the very recent MM-LLMs.
  arXiv  Detail & Related papers  (2024-03-28T15:53:45Z)
• Multi-modal Auto-regressive Modeling via Visual Words [96.25078866446053]
  We propose the concept of visual tokens, which maps the visual features to probability distributions over Large Multi-modal Models' vocabulary. We further explore the distribution of visual features in the semantic space within LMM and the possibility of using text embeddings to represent visual information.
  arXiv  Detail & Related papers  (2024-03-12T14:58:52Z)
• The Revolution of Multimodal Large Language Models: A Survey [46.84953515670248]
  Multimodal Large Language Models (MLLMs) can seamlessly integrate visual and textual modalities. This paper provides a review of recent visual-based MLLMs, analyzing their architectural choices, multimodal alignment strategies, and training techniques.
  arXiv  Detail & Related papers  (2024-02-19T19:01:01Z)
• Large Language Models Meet Computer Vision: A Brief Survey [0.0]
  Large Language Models (LLMs) and Computer Vision (CV) have emerged as a pivotal area of research, driving significant advancements in the field of Artificial Intelligence (AI) This survey paper delves into the latest progressions in the domain of transformers, emphasizing their potential to revolutionize Vision Transformers (ViTs) and LLMs. The survey is concluded by highlighting open directions in the field, suggesting potential venues for future research and development.
  arXiv  Detail & Related papers  (2023-11-28T10:39:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.