A fundamental characteristic common to both human vision and natural language
is their compositional nature. Yet, despite the performance gains contributed
by large vision and language pretraining, recent investigations find that
most-if not all-our state-of-the-art vision-language models struggle at
compositionality. They are unable to distinguish between images of " a girl in
white facing a man in black" and "a girl in black facing a man in white".
Moreover, prior work suggests that compositionality doesn't arise with scale:
larger model sizes or training data don't help. This paper develops a new
iterated training algorithm that incentivizes compositionality. We draw on
decades of cognitive science research that identifies cultural transmission-the
need to teach a new generation-as a necessary inductive prior that incentivizes
humans to develop compositional languages. Specifically, we reframe
vision-language contrastive learning as the Lewis Signaling Game between a
vision agent and a language agent, and operationalize cultural transmission by
iteratively resetting one of the agent's weights during training. After every
iteration, this training paradigm induces representations that become "easier
to learn", a property of compositional languages: e.g. our model trained on
CC3M and CC12M improves standard CLIP by 4.7%, 4.0% respectfully in the
SugarCrepe benchmark.

通过引入一种新的迭代训练算法，以文化传播作为归纳先验，本研究以视觉代理和语言代理之间的 Lewis 信号游戏重新定义了视觉 - 语言对比学习，并通过在训练过程中迭代重置代理权重的方式实现文化传播，使得模型对于合成语言的特性变得更易于学习，达到了在 SugarCrepe 评测中相比标准 CLIP 模型分别提升 4.7% 和 4.0% 的效果。