Vilbert: Pretraining Task-agnostic Visiolinguistic Representations For Vision-and-language Tasks

Lu Jiasen, Batra Dhruv, Parikh Devi, Lee Stefan. Arxiv 2019

[Paper]    
Applications Attention Mechanism BERT Model Architecture Pretraining Methods Training Techniques Transformer

We present ViLBERT (short for Vision-and-Language BERT), a model for learning task-agnostic joint representations of image content and natural language. We extend the popular BERT architecture to a multi-modal two-stream model, pro-cessing both visual and textual inputs in separate streams that interact through co-attentional transformer layers. We pretrain our model through two proxy tasks on the large, automatically collected Conceptual Captions dataset and then transfer it to multiple established vision-and-language tasks – visual question answering, visual commonsense reasoning, referring expressions, and caption-based image retrieval – by making only minor additions to the base architecture. We observe significant improvements across tasks compared to existing task-specific models – achieving state-of-the-art on all four tasks. Our work represents a shift away from learning groundings between vision and language only as part of task training and towards treating visual grounding as a pretrainable and transferable capability.

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