面向视觉常识推理的有向视觉连接

韩亚洪; 武阿明; 朱霖潮; 杨易

doi:10.1631/FITEE.2000722

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面向视觉常识推理的有向视觉连接

视觉知识专栏 | Updated：2022-05-19

- 面向视觉常识推理的有向视觉连接
  Enhanced Publication
- Visual commonsense reasoning with directional visual connections
- 信息与电子工程前沿（英文） 2021年22卷第5期页码：625-637
- Affiliations：
  
  College of Intelligence and Computing, Tianjin University, Tianjin 300350, China
  Tianjin Key Lab of Machine Learning, Tianjin University, Tianjin 300350, China
  School of Computer Science, University of Technology Sydney, Sydney 2007, Australia
- Author bio：
  
  Yahong HAN, E-mail: yahong@tju.edu.cn
- Funds：
  
  Project supported by the National Natural Science Foundation of China (Nos. 61876130 and 61932009)
- DOI：10.1631/FITEE.2000722
  中图分类号： TP181
- 纸质出版日期：2021-05，
  
  收稿日期：2020-12-25，
  
  修回日期：2021-04-22，
- Accepted：
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韩亚洪, 武阿明, 朱霖潮, 等. 面向视觉常识推理的有向视觉连接[J]. 信息与电子工程前沿（英文）, 2021,22(5):625-637.

YAHONG HAN, AMING WU, LINCHAO ZHU, et al. Visual commonsense reasoning with directional visual connections. [J]. Frontiers of information technology & electronic engineering, 2021, 22(5): 625-637.
韩亚洪, 武阿明, 朱霖潮, 等. 面向视觉常识推理的有向视觉连接[J]. 信息与电子工程前沿（英文）, 2021,22(5):625-637. DOI： 10.1631/FITEE.2000722.

YAHONG HAN, AMING WU, LINCHAO ZHU, et al. Visual commonsense reasoning with directional visual connections. [J]. Frontiers of information technology & electronic engineering, 2021, 22(5): 625-637. DOI： 10.1631/FITEE.2000722.

摘要

为推动认知层面视觉内容理解的研究，即基于视觉细节的深入理解做出精确推理，视觉常识推理的概念被提出。相比仅需模型正确回答问题的传统视觉问答，视觉常识推理不仅需要模型正确地回答问题，还需给出相应解释。最近关于人类认知的研究指出大脑认知可以看作局部神经元连接的全局动态集成，有助于解决特定的认知任务。受其启发，本文提出有向连接网络。通过使用问题和答案的语义来情景化视觉神经元从而动态重组神经元连接，以及借助方向信息增强推理能力，所提方法能有效实现视觉常识推理。具体地，首先开发一个GraphVLAD模块来捕捉能够充分表达视觉内容相关性的视觉神经元连接。然后提出一个情景化模型来融合视觉和文本表示。最后，基于情景化连接的输出设计有向连接来推断答案及对应解释，其中包含了ReasonVLAD模块。实验结果和可视化分析证明了所提方法的有效性。

Abstract

To boost research into cognition-level visual understanding

i.e.

making an accurate inference based on a thorough understanding of visual details

visual commonsense reasoning (VCR) has been proposed. Compared with traditional visual question answering which requires models to select correct answers

VCR requires models to select not only the correct answers

but also the correct rationales. Recent research into human cognition has indicated that brain function or cognition can be considered as a global and dynamic integration of local neuron connectivity

which is helpful in solving specific cognition tasks. Inspired by this idea

we propose a directional connective network to achieve VCR by dynamically reorganizing the visual neuron connectivity that is contextualized using the meaning of questions and answers and leveraging the directional information to enhance the reasoning ability. Specifically

we first develop a GraphVLAD module to capture visual neuron connectivity to fully model visual content correlations. Then

a contextualization process is proposed to fuse sentence representations with visual neuron representations. Finally

based on the output of contextualized connectivity

we propose directional connectivity to infer answers and rationales

which includes a ReasonVLAD module. Experimental results on the VCR dataset and visualization analysis demonstrate the effectiveness of our method.

关键词

视觉常识推理有向连接网络视觉神经元连接情景化连接有向连接

Keywords

Visual commonsense reasoningDirectional connective networkVisual neuron connectivityContextualized connectivityDirectional connectivity

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