Beyond Text: Frozen Large Language Models in Visual Signal Comprehension

• URL: http://arxiv.org/abs/2403.07874v1
• Date: Tue, 12 Mar 2024 17:59:51 GMT
• Title: Beyond Text: Frozen Large Language Models in Visual Signal Comprehension
• Authors: Lei Zhu, Fangyun Wei, Yanye Lu
• Abstract summary: Vision-to-Language Tokenizer, abbreviated as V2T Tokenizer, transforms an image into a foreign language'' with the combined aid of an encoder-decoder, the LLM vocabulary, and a CLIP model. We undertake rigorous experiments to validate our method, encompassing understanding tasks like image recognition, image captioning, and visual question answering, as well as image denoising tasks like inpainting, outpainting, deblurring, and shift restoration.
• Score: 34.398976855955404
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work, we investigate the potential of a large language model (LLM) to directly comprehend visual signals without the necessity of fine-tuning on multi-modal datasets. The foundational concept of our method views an image as a linguistic entity, and translates it to a set of discrete words derived from the LLM's vocabulary. To achieve this, we present the Vision-to-Language Tokenizer, abbreviated as V2T Tokenizer, which transforms an image into a ``foreign language'' with the combined aid of an encoder-decoder, the LLM vocabulary, and a CLIP model. With this innovative image encoding, the LLM gains the ability not only for visual comprehension but also for image denoising and restoration in an auto-regressive fashion-crucially, without any fine-tuning. We undertake rigorous experiments to validate our method, encompassing understanding tasks like image recognition, image captioning, and visual question answering, as well as image denoising tasks like inpainting, outpainting, deblurring, and shift restoration. Code and models are available at https://github.com/zh460045050/V2L-Tokenizer.

Related papers

• ClawMachine: Fetching Visual Tokens as An Entity for Referring and Grounding [67.63933036920012]
  Existing methods, including proxy encoding and geometry encoding, incorporate additional syntax to encode the object's location. This study presents ClawMachine, offering a new methodology that notates an entity directly using the visual tokens. ClawMachine unifies visual referring and grounding into an auto-regressive format and learns with a decoder-only architecture.
  arXiv  Detail & Related papers  (2024-06-17T08:39:16Z)
• Language Model Beats Diffusion -- Tokenizer is Key to Visual Generation [122.63617171522316]
  Large Language Models (LLMs) are the dominant models for generative tasks in language. We introduce MAGVIT-v2, a video tokenizer designed to generate concise and expressive tokens for both videos and images.
  arXiv  Detail & Related papers  (2023-10-09T14:10:29Z)
• 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)
• SPAE: Semantic Pyramid AutoEncoder for Multimodal Generation with Frozen LLMs [124.29233620842462]
  We introduce SPAE for enabling frozen LLMs to perform both understanding and generation tasks involving non-linguistic modalities such as images or videos. The resulting lexical tokens capture both the semantic meaning and the fine-grained details needed for visual reconstruction. Our method marks the first successful attempt to enable a frozen LLM to generate image content while surpassing state-of-the-art performance in image understanding tasks, under the same setting, by over 25%.
  arXiv  Detail & Related papers  (2023-06-30T17:59:07Z)
• 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)
• Leveraging per Image-Token Consistency for Vision-Language Pre-training [52.825150269820696]
  Cross-modal masked language modeling (CMLM) is insufficient for vision-language pre-training. We propose EPIC (lEveraging Per Image-Token Consistency for vision-language pre-training) The proposed EPIC method is easily combined with pre-training methods.
  arXiv  Detail & Related papers  (2022-11-20T12:10:53Z)
• 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)
• Align before Fuse: Vision and Language Representation Learning with Momentum Distillation [52.40490994871753]
  We introduce a contrastive loss to representations BEfore Fusing (ALBEF) through cross-modal attention. We propose momentum distillation, a self-training method which learns from pseudo-targets produced by a momentum model. ALBEF achieves state-of-the-art performance on multiple downstream vision-language tasks.
  arXiv  Detail & Related papers  (2021-07-16T00:19:22Z)
• 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.