An Introduction to Vision-Language Modeling

• URL: http://arxiv.org/abs/2405.17247v1
• Date: Mon, 27 May 2024 15:01:23 GMT
• Title: An Introduction to Vision-Language Modeling
• Authors: Florian Bordes, Richard Yuanzhe Pang, Anurag Ajay, Alexander C. Li, Adrien Bardes, Suzanne Petryk, Oscar Mañas, Zhiqiu Lin, Anas Mahmoud, Bargav Jayaraman, Mark Ibrahim, Melissa Hall, Yunyang Xiong, Jonathan Lebensold, Candace Ross, Srihari Jayakumar, Chuan Guo, Diane Bouchacourt, Haider Al-Tahan, Karthik Padthe, Vasu Sharma, Hu Xu, Xiaoqing Ellen Tan, Megan Richards, Samuel Lavoie, Pietro Astolfi, Reyhane Askari Hemmat, Jun Chen, Kushal Tirumala, Rim Assouel, Mazda Moayeri, Arjang Talattof, Kamalika Chaudhuri, Zechun Liu, Xilun Chen, Quentin Garrido, Karen Ullrich, Aishwarya Agrawal, Kate Saenko, Asli Celikyilmaz, Vikas Chandra,
• Abstract summary: The vision-language model (VLM) applications will significantly impact our relationship with technology. We introduce what VLMs are, how they work, and how to train them. Although this work primarily focuses on mapping images to language, we also discuss extending VLMs to videos.
• Score: 128.6223984157515
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Following the recent popularity of Large Language Models (LLMs), several attempts have been made to extend them to the visual domain. From having a visual assistant that could guide us through unfamiliar environments to generative models that produce images using only a high-level text description, the vision-language model (VLM) applications will significantly impact our relationship with technology. However, there are many challenges that need to be addressed to improve the reliability of those models. While language is discrete, vision evolves in a much higher dimensional space in which concepts cannot always be easily discretized. To better understand the mechanics behind mapping vision to language, we present this introduction to VLMs which we hope will help anyone who would like to enter the field. First, we introduce what VLMs are, how they work, and how to train them. Then, we present and discuss approaches to evaluate VLMs. Although this work primarily focuses on mapping images to language, we also discuss extending VLMs to videos.

Related papers

• Can Vision Language Models Learn from Visual Demonstrations of Ambiguous Spatial Reasoning? [7.827653846113951]
  Large vision-language models (VLMs) have become state-of-the-art for many computer vision tasks. We propose a new benchmark we call Spatial Visual Ambiguity Tasks (SVAT) that challenges state-of-the-art VLMs to learn new visuospatial tasks in-context.
  arXiv  Detail & Related papers  (2024-09-25T16:45:02Z)
• How Well Can Vision Language Models See Image Details? [53.036922527685064]
  We introduce a pixel value prediction task to explore "How Well Can Vision Language Models See Image Details?" Our research reveals that incorporating pixel value prediction as one of the VLM pre-training tasks and vision encoder adaptation markedly boosts VLM performance on downstream image-language understanding tasks.
  arXiv  Detail & Related papers  (2024-08-07T17:59:40Z)
• InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks [92.03764152132315]
  We design a large-scale vision-language foundation model (InternVL), which scales up the vision foundation model to 6 billion parameters. This model can be broadly applied to and achieve state-of-the-art performance on 32 generic visual-linguistic benchmarks. It has powerful visual capabilities and can be a good alternative to the ViT-22B.
  arXiv  Detail & Related papers  (2023-12-21T18:59:31Z)
• Large Language Models are Visual Reasoning Coordinators [144.67558375045755]
  We propose a novel paradigm that coordinates multiple vision-language models for visual reasoning. We show that our instruction tuning variant, Cola-FT, achieves state-of-the-art performance on visual question answering. We also show that our in-context learning variant, Cola-Zero, exhibits competitive performance in zero and few-shot settings.
  arXiv  Detail & Related papers  (2023-10-23T17:59:31Z)
• Unified Language-Vision Pretraining in LLM with Dynamic Discrete Visual Tokenization [52.935150075484074]
  We introduce a well-designed visual tokenizer to translate the non-linguistic image into a sequence of discrete tokens like a foreign language. The resulting visual tokens encompass high-level semantics worthy of a word and also support dynamic sequence length varying from the image. This unification empowers LaVIT to serve as an impressive generalist interface to understand and generate multi-modal content simultaneously.
  arXiv  Detail & Related papers  (2023-09-09T03:01:38Z)
• VLMAE: Vision-Language Masked Autoencoder [21.97700040013084]
  We propose a vision-language masked autoencoder framework (VLMAE) for vision-language pre-training. VLMAE employs visual generative learning, facilitating the model to acquire fine-grained and unbiased features.
  arXiv  Detail & Related papers  (2022-08-19T14:39:18Z)
• Visually-Augmented Language Modeling [137.36789885105642]
  We propose a novel pre-training framework, named VaLM, to Visually-augment text tokens with retrieved relevant images for Language Modeling. With the visually-augmented context, VaLM uses a visual knowledge fusion layer to enable multimodal grounded language modeling. We evaluate the proposed model on various multimodal commonsense reasoning tasks, which require visual information to excel.
  arXiv  Detail & Related papers  (2022-05-20T13:41:12Z)

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.