A Vision Check-up for Language Models

• URL: http://arxiv.org/abs/2401.01862v1
• Date: Wed, 3 Jan 2024 18:09:33 GMT
• Title: A Vision Check-up for Language Models
• Authors: Pratyusha Sharma, Tamar Rott Shaham, Manel Baradad, Stephanie Fu, Adrian Rodriguez-Munoz, Shivam Duggal, Phillip Isola, Antonio Torralba
• Abstract summary: We show how a preliminary visual representation learning system can be trained using models of text. Experiments on self-supervised visual representation learning highlight the potential to train vision models capable of making semantic assessments of natural images.
• Score: 61.852026871772914
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: What does learning to model relationships between strings teach large language models (LLMs) about the visual world? We systematically evaluate LLMs' abilities to generate and recognize an assortment of visual concepts of increasing complexity and then demonstrate how a preliminary visual representation learning system can be trained using models of text. As language models lack the ability to consume or output visual information as pixels, we use code to represent images in our study. Although LLM-generated images do not look like natural images, results on image generation and the ability of models to correct these generated images indicate that precise modeling of strings can teach language models about numerous aspects of the visual world. Furthermore, experiments on self-supervised visual representation learning, utilizing images generated with text models, highlight the potential to train vision models capable of making semantic assessments of natural images using just LLMs.

Related papers

• Sequential Modeling Enables Scalable Learning for Large Vision Models [120.91839619284431]
  We introduce a novel sequential modeling approach which enables learning a Large Vision Model (LVM) without making use of any linguistic data. We define a common format, "visual sentences", in which we can represent raw images and videos as well as annotated data sources.
  arXiv  Detail & Related papers  (2023-12-01T18:59:57Z)
• 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)
• GPT4Image: Can Large Pre-trained Models Help Vision Models on Perception Tasks? [51.22096780511165]
  We present a new learning paradigm in which the knowledge extracted from large pre-trained models are utilized to help models like CNN and ViT learn enhanced representations. We feed detailed descriptions into a pre-trained encoder to extract text embeddings with rich semantic information that encodes the content of images.
  arXiv  Detail & Related papers  (2023-06-01T14:02:45Z)
• Visually-Situated Natural Language Understanding with Contrastive Reading Model and Frozen Large Language Models [24.456117679941816]
  Contrastive Reading Model (Cream) is a novel neural architecture designed to enhance the language-image understanding capability of Large Language Models (LLMs) Our approach bridges the gap between vision and language understanding, paving the way for the development of more sophisticated Document Intelligence Assistants.
  arXiv  Detail & Related papers  (2023-05-24T11:59:13Z)
• 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)
• K-LITE: Learning Transferable Visual Models with External Knowledge [242.3887854728843]
  K-LITE (Knowledge-augmented Language-Image Training and Evaluation) is a strategy to leverage external knowledge to build transferable visual systems. In training, it enriches entities in natural language with WordNet and Wiktionary knowledge. In evaluation, the natural language is also augmented with external knowledge and then used to reference learned visual concepts.
  arXiv  Detail & Related papers  (2022-04-20T04:47:01Z)
• Learning Visual Representations with Caption Annotations [19.24013129952071]
  We propose a proxy task to learn visual representations over image-caption pairs. ICMLM consists in predicting masked words in captions by relying on visual cues. Our experiments confirm that image captions can be leveraged to inject global and localized semantic information into visual representations.
  arXiv  Detail & Related papers  (2020-08-04T08:04:16Z)

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.