视觉对话中视觉参考解析的双重注意网络
Dual Attention Networks for Visual Reference Resolution in Visual Dialog
摘要：
可视化对话（VisDial）是一个需要人工智能智能体回答一系列基于图像的问题的任务。与视觉问答（VQA）不同，这一系列问题应该能够从对话历史中捕获时间上下文，并利用视觉基础信息。一个被称为视觉参考解析的问题涉及这些挑战，要求智能体解决给定问题中的模糊参考，并在给定图像中找到参考。在本文中，我们提出了双注意力网络（DAN）的视觉参考分辨率。DAN由REFER和FIND两种注意网络组成。具体地说，REFER模块通过采用自我注意力机制来学习给定问题和对话历史之间的潜在关系。FIND模块获取图像特征和参考感知表示（即，REFER模块的输出）作为输入，并通过自下而上的注意力机制执行视觉基础。我们在VisDial v1.0和v0.9数据集上对我们的模型进行了定性和定量评估，结果表明DAN的性能明显优于以前的最先进模型。
英文摘要：
Visual dialog (VisDial) is a task which requires an AI agent to answer a series of questions grounded in an image. Unlike in visual question answering (VQA), the series of questions should be able to capture a temporal context from a dialog history and exploit visually-grounded information. A problem called visual reference resolution involves these challenges, requiring the agent to resolve ambiguous references in a given question and find the references in a given image. In this paper, we propose Dual Attention Networks (DAN) for visual reference resolution. DAN consists of two kinds of attention networks, REFER and FIND. Specifically, REFER module learns latent relationships between a given question and a dialog history by employing a self-attention mechanism. FIND module takes image features and reference-aware representations (i.e., the output of REFER module) as input, and performs visual grounding via bottom-up attention mechanism. We qualitatively and quantitatively evaluate our model on VisDial v1.0 and v0.9 datasets, showing that DAN outperforms the previous state-of-the-art model by a significant margin.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
