CLC number: TP181
On-line Access: 2021-05-17
Received: 2020-12-25
Revision Accepted: 2021-02-06
Crosschecked: 2021-04-22
Cited: 0
Clicked: 4271
Yahong Han, Aming Wu, Linchao Zhu, Yi Yang. Visual commonsense reasoning with directional visual connections[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(5): 625-637.
@article{title="Visual commonsense reasoning with directional visual connections",
author="Yahong Han, Aming Wu, Linchao Zhu, Yi Yang",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="22",
number="5",
pages="625-637",
year="2021",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2000722"
}
%0 Journal Article
%T Visual commonsense reasoning with directional visual connections
%A Yahong Han
%A Aming Wu
%A Linchao Zhu
%A Yi Yang
%J Frontiers of Information Technology & Electronic Engineering
%V 22
%N 5
%P 625-637
%@ 2095-9184
%D 2021
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2000722
TY - JOUR
T1 - Visual commonsense reasoning with directional visual connections
A1 - Yahong Han
A1 - Aming Wu
A1 - Linchao Zhu
A1 - Yi Yang
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 22
IS - 5
SP - 625
EP - 637
%@ 2095-9184
Y1 - 2021
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2000722
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.
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