Recursive Visual Attention In Visual Dialog

Niu Yulei, Zhang Hanwang, Zhang Manli, Zhang Jianhong, Lu Zhiwu, Wen Ji-rong. Arxiv 2018

[Paper] [Code]    
Agentic Applications Attention Mechanism Has Code Model Architecture Multimodal Models Reinforcement Learning Transformer

Visual dialog is a challenging vision-language task, which requires the agent to answer multi-round questions about an image. It typically needs to address two major problems: (1) How to answer visually-grounded questions, which is the core challenge in visual question answering (VQA); (2) How to infer the co-reference between questions and the dialog history. An example of visual co-reference is: pronouns (\eg, they'') in the question (\eg,Are they on or off?’’) are linked with nouns (\eg, lamps'') appearing in the dialog history (\eg,How many lamps are there?’’) and the object grounded in the image. In this work, to resolve the visual co-reference for visual dialog, we propose a novel attention mechanism called Recursive Visual Attention (RvA). Specifically, our dialog agent browses the dialog history until the agent has sufficient confidence in the visual co-reference resolution, and refines the visual attention recursively. The quantitative and qualitative experimental results on the large-scale VisDial v0.9 and v1.0 datasets demonstrate that the proposed RvA not only outperforms the state-of-the-art methods, but also achieves reasonable recursion and interpretable attention maps without additional annotations. The code is available at \url{https://github.com/yuleiniu/rva}.

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