Interpretable Visual Understanding with Cognitive Attention Network

authored by

Xuejiao Tang, Wenbin Zhang, Yi Yu, Kea Turner, Tyler Derr, Mengyu Wang, Eirini Ntoutsi

Abstract

While image understanding on recognition-level has achieved remarkable advancements, reliable visual scene understanding requires comprehensive image understanding on recognition-level but also cognition-level, which calls for exploiting the multi-source information as well as learning different levels of understanding and extensive commonsense knowledge. In this paper, we propose a novel Cognitive Attention Network (CAN) for visual commonsense reasoning to achieve interpretable visual understanding. Specifically, we first introduce an image-text fusion module to fuse information from images and text collectively. Second, a novel inference module is designed to encode commonsense among image, query and response. Extensive experiments on large-scale Visual Commonsense Reasoning (VCR) benchmark dataset demonstrate the effectiveness of our approach. The implementation is publicly available at github.com/tanjatang/CAN.

External Organisation(s)

Carnegie Mellon University
Research Organization of Information and Systems National Institute of Informatics
University of South Florida
Vanderbilt University
Harvard University
Freie Universität Berlin (FU Berlin)

Type

Conference contribution

Pages

555-568

No. of pages

14

Publication date

2021

Publication status

Published

Peer reviewed

Yes

ASJC Scopus subject areas

Theoretical Computer Science, Computer Science(all)

Electronic version(s)

https://doi.org/10.48550/arXiv.2108.02924 (Access: Open)
https://doi.org/10.1007/978-3-030-86362-3_45 (Access: Closed)