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Image-caption pretraining has been quite successfully used for downstreamvision tasks like zero-shot image classification and object detection. However,image-caption pretraining is still a hard problem -- it requires multipleconcepts (nouns) from captions to be aligned to several objects in images. Totackle this problem, we go to the roots -- the best learner, children. We takeinspiration from cognitive science studies dealing with children's languagelearning to propose a curriculum learning framework. The learning begins witheasy-to-align image caption pairs containing one concept per caption. Thedifficulty is progressively increased with each new phase by adding one moreconcept per caption. Correspondingly, the knowledge acquired in each learningphase is utilized in subsequent phases to effectively constrain the learningproblem to aligning one new concept-object pair in each phase. We show thatthis learning strategy improves over vanilla image-caption training in varioussettings -- pretraining from scratch, using a pretrained image or/andpretrained text encoder, low data regime etc.

Learning from Children: Improving Image-Caption Pretraining via Curriculum