![]() Experiments on the large-scale Visual Commonsense Reasoning benchmark demonstrate the superior performance of our proposed modules on three tasks (improving 5% accuracy on Q->A, 3.5% on QA->R, 5.8% on Q->AR). Moreover, our HGL integrates a contextual voting module to exploit a long-range visual context for better global reasoning. Our HGL consists of a primal vision-to-answer heterogeneous graph (VAHG) module and a dual question-to-answer heterogeneous graph (QAHG) module to interactively refine reasoning paths for semantic agreement. ![]() In this paper, we propose a new Heterogeneous Graph Learning (HGL) framework for seamlessly integrating the intra-graph and inter-graph reasoning in order to bridge the vision and language domain. Xu Lu, Guang Han, Yu Pan, Guoyu Wang, Xiaoyuan Zhou, Xianhui Chen. degree in Mechanical Engineering from Virginia Tech in 2019 and his Bachelor's degree in Railway Vehicle Engineering from Tongji University (Shanghai, China) in 2015. Existing works either resort to a powerful end-to-end network that cannot produce interpretable reasoning paths or solely explore intra-relationship of visual objects (homogeneous graph) while ignoring the cross-domain semantic alignment among visual concepts and linguistic words. Xikai Wen, Fanghang Yu, Zhigang Gui, Yuqing Zhang, Xingyuan Hou, Lei Shan, Tao Wu. ![]() It poses great challenges over the proper semantic alignment between vision and linguistic domains and knowledge reasoning to generate persuasive reasoning paths. Visual commonsense reasoning task aims at leading the research field into solving cognition-level reasoning with the ability to predict correct answers and meanwhile providing convincing reasoning paths, resulting in three sub-tasks i.e., Q->A, QA->R and Q->AR. Hong Wang, Yu Chen, Martial Duchamp, Qingsheng Zeng, Xuewen Wang, Siu Hon Tsang, Peng Yu, Ting Yu, Edwin Hang Tong Teo, and Zheng Liu Large-area atomic layers of the charge-density-wave conductor TiSe2, Advanced Materials 2018, 30, 1704382. ![]()
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