Language-Image Alignment with Fixed Text Encoders
Authors: name: Jingfeng Yang, name: Ziyang Wu, name: Yue Zhao, name: Yi Ma

Currently, the most dominant approach to establishing language-image
alignment is to pre-train text and image encoders jointly through contrastive
learning, such as CLIP and its variants. In this work, we question whether such
a costly joint training is necessary. In particular, we investigate if a
pre-trained fixed large language model (LLM) offers a good enough text encoder
to guide visual representation learning. That is, we propose to learn
Language-Image alignment with a Fixed Text encoder (LIFT) from an LLM by
training only the image encoder. Somewhat surprisingly, through comprehensive
benchmarking and ablation studies, we find that this much simplified framework
LIFT is highly effective and it outperforms CLIP in most scenarios that involve
compositional understanding and long captions, while achieving considerable
gains in computational efficiency. Our work takes a first step towards
systematically exploring how text embeddings from LLMs can guide visual
learning and suggests an alternative design choice for learning
language-aligned visual representations.