KD-VLP: Improving End-to-End Vision-and-Language Pretraining with Object Knowledge Distillation

• URL: http://arxiv.org/abs/2109.10504v1
• Date: Wed, 22 Sep 2021 03:38:05 GMT
• Title: KD-VLP: Improving End-to-End Vision-and-Language Pretraining with Object Knowledge Distillation
• Authors: Yongfei Liu, Chenfei Wu, Shao-yen Tseng, Vasudev Lal, Xuming He, Nan Duan
• Abstract summary: Self-supervised vision-and-language pretraining aims to learn transferable multi-modal representations from large-scale image-text data. We propose an object-aware end-to-end QF framework, which directly feeds image grid features from CNNs into the Transformer and learns the multi-modal representations jointly. To achieve that, we design two novel pretext tasks by taking object features and their semantic labels from external detectors as supervision.
• Score: 42.01427946204401
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Self-supervised vision-and-language pretraining (VLP) aims to learn transferable multi-modal representations from large-scale image-text data and to achieve strong performances on a broad scope of vision-language tasks after finetuning. Previous mainstream VLP approaches typically adopt a two-step strategy relying on external object detectors to encode images in a multi-modal Transformer framework, which suffer from restrictive object concept space, limited image context and inefficient computation. In this paper, we propose an object-aware end-to-end VLP framework, which directly feeds image grid features from CNNs into the Transformer and learns the multi-modal representations jointly. More importantly, we propose to perform object knowledge distillation to facilitate learning cross-modal alignment at different semantic levels. To achieve that, we design two novel pretext tasks by taking object features and their semantic labels from external detectors as supervision: 1.) Object-guided masked vision modeling task focuses on enforcing object-aware representation learning in the multi-modal Transformer; 2.) Phrase-region alignment task aims to improve cross-modal alignment by utilizing the similarities between noun phrases and object labels in the linguistic space. Extensive experiments on a wide range of vision-language tasks demonstrate the efficacy of our proposed framework, and we achieve competitive or superior performances over the existing pretraining strategies. The code is available in supplementary materials.

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