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Pretrained Transformers from Language Models for Visual Encoding

Official code implementation for "Frozen Transformers in Language Models are Effective Visual Encoder Layers"

Ziqi Pang, Ziyang Xie*, Yunze Man*, Yu-Xiong Wang

If you find our paper or code helpful for your work, please consider cite by

@article{pang2023fozen,
  title={Frozen transformers in language models are effective visual encoder layers},
  author={Pang, Ziqi and Xie, Ziyang and Man, Yunze and Wang, Yu-Xiong},
  journal={arXiv preprint arXiv:2310.12973},
  year={2023}
}

News

01/16/2024: Papers accepted at ICLR 2024 (Spotlight). :tada:
11/01/2023: Code release for point cloud classification.
10/24/2023: Code release for action recognition.
10/19/2023: Paper is available on Arxiv with initial code release on image classification.

1. Introduction

This paper reveals an intriguing discovery: pretrained transformers from LLMs, despite being trained solely on textual data, are surprisingly strong encoders for purely visual tasks in the absence of language. Our exploration shows the potential of LLMs as general-purpose encoders for visual data, as opposed to the previous usages of either pure encoders for text embeddings or decoders for tokenized outputs.

Our approach is straightforward yet overlooked: incorporating a frozen transformer block from a pre-trained LLM as a general-purpose visual encoder layer, directly processing the visual tokens.

We intuitively illustrate our approach below (also Figure 1 in the paper). The procedure is as simple as three steps:

Extract a frozen LLM transformer block and append it on top of the original visual encoder.
Insert trainable linear layers before and after the added LLM block to align the feature dimensions.
Freeze the LLM transformer while optimizing the other modules as usual during training.

method

To explain the benefits of using frozen LLM transformers, we further propose information filtering hypothesis: the pre-trained LLM transformer blocks discern informative visual tokens and further amplify their contribution to the latent representation. This is supported empirically from our observation that feature activation concentrated better on relevant regions as below (also Figure 3 in the paper).

hypothesis

2. Getting Started with the Experiments

We examine our discovery on a wide range of tasks and release their code in different directories: