Long-CLIP: Unlocking the Long-Text Capability of CLIP
Beichen Zhang Pan Zhang Xiaoyi Dong Yuhang Zang Jiaqi Wang
Contrastive Language-Image Pre-training (CLIP) has been the cornerstone for
zero-shot classification, text-image retrieval, and text-image generation by
aligning image and text modalities. Despite its widespread adoption, a
significant limitation of CLIP lies in the inadequate length of text input. The
length of the text token is restricted to 77, and an empirical study shows the
actual effective length is even less than 20. This prevents CLIP from handling
detailed descriptions, limiting its applications for image retrieval and
text-to-image generation with extensive prerequisites. To this end, we propose
Long-CLIP as a plug-and-play alternative to CLIP that supports long-text input,
retains or even surpasses its zero-shot generalizability, and aligns the CLIP
latent space, making it readily replace CLIP without any further adaptation in
downstream frameworks. Nevertheless, achieving this goal is far from
straightforward, as simplistic fine-tuning can result in a significant
degradation of CLIP's performance. Moreover, substituting the text encoder with
a language model supporting longer contexts necessitates pretraining with vast
amounts of data, incurring significant expenses. Accordingly, Long-CLIP
introduces an efficient fine-tuning solution on CLIP with two novel strategies
designed to maintain the original capabilities, including (1) a
knowledge-preserved stretching of positional embedding and (2) a primary
component matching of CLIP features. With leveraging just one million extra
long text-image pairs, Long-CLIP has shown the superiority to CLIP for about
20% in long caption text-image retrieval and 6% in traditional text-image
retrieval tasks, e.g., COCO and Flickr30k. Furthermore, Long-CLIP offers
enhanced capabilities for generating images from detailed text descriptions by
replacing CLIP in a plug-and-play manner.