  Vision-language pre-training like CLIP has shown promising performance on
various downstream tasks such as zero-shot image classification and image-text
retrieval. Most of the existing CLIP-alike works usually adopt relatively large
image encoders like ResNet50 and ViT, while the lightweight counterparts are
rarely discussed. In this paper, we propose a multi-level interaction paradigm
for training lightweight CLIP models. Firstly, to mitigate the problem that
some image-text pairs are not strictly one-to-one correspondence, we improve
the conventional global instance-level alignment objective by softening the
label of negative samples progressively. Secondly, a relaxed bipartite matching
based token-level alignment objective is introduced for finer-grained alignment
between image patches and textual words. Moreover, based on the observation
that the accuracy of CLIP model does not increase correspondingly as the
parameters of text encoder increase, an extra objective of masked language
modeling (MLM) is leveraged for maximizing the potential of the shortened text
encoder. In practice, an auxiliary fusion module injecting unmasked image
embedding into masked text embedding at different network stages is proposed
for enhancing the MLM. Extensive experiments show that without introducing
additional computational cost during inference, the proposed method achieves a
higher performance on multiple downstream tasks.
