Contrastive Vision-Language Alignment Makes Efficient Instruction
  Learner
Lizhao Liu Xinyu Sun Tianhang Xiang Zhuangwei Zhuang Liuren Yin Mingkui Tan
We study the task of extending the large language model (LLM) into a
vision-language instruction-following model. This task is crucial but
challenging since the LLM is trained on text modality only, making it hard to
effectively digest the visual modality. To address this, existing methods
typically train a visual adapter to align the representation between a
pre-trained vision transformer (ViT) and the LLM by a generative image
captioning loss. However, we find that the generative objective can only
produce weak alignment for vision and language, making the aligned
vision-language model very hungry for the instruction fine-tuning data. In this
paper, we propose CG-VLM that applies both Contrastive and Generative alignment
objectives to effectively align the representation of ViT and LLM. Different
from image level and sentence level alignment in common contrastive learning
settings, CG-VLM aligns the image-patch level features and text-token level
embeddings, which, however, is very hard to achieve as no explicit grounding
patch-token relation provided in standard image captioning datasets. To address
this issue, we propose to maximize the averaged similarity between pooled
image-patch features and text-token embeddings. Extensive experiments
demonstrate that the proposed CG-VLM produces strong vision-language alignment
and is an efficient instruction learner. For example, using only 10%
instruction tuning data, we reach 95% performance of state-of-the-art method
LLaVA [29] on the zero-shot ScienceQA-Image benchmark.