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Improved Blending Attention Mechanism in visual question answering

Computer Systems Science & Engineering 2023年第10期47卷 1149-1161页

作者： Siyu Lu Yueming Ding Zhengtong Yin Mingzhe Liu Xuan Liu Wenfeng Zheng Lirong Yin School of Automation University of Electronic Science and Technology of ChinaChengdu610054China College of Resource and Environment Engineering Guizhou UniversityGuiyang550025China School of Data Science and Artificial Intelligence Wenzhou University of TechnologyWenzhou325000China School of Public Affairs and Administration University of Electronic Science and Technology of ChinaChengdu611731China Department of Geography and Anthropology Louisiana State UniversityBaton Rouge70803LAUSA

visual question answering(VQA)has attracted more and more attention in computer vision and natural language processing.Scholars are committed to studying how to better integrate image features and text features to achieve better results in VQA tasks.Analysis of all features may cause information redundancy and heavy computational burden.Attention mechanism is a wise way to solve this problem.However,using single attention mechanism may cause incomplete concern of features.This paper improves the attention mechanism method and proposes a hybrid attention mechanism that combines the spatial attention mechanism method and the channel attention mechanism method.In the case that the attention mechanism will cause the loss of the original features,a small portion of image features were added as compensation.For the attention mechanism of text features,a selfattention mechanism was introduced,and the internal structural features of sentences were strengthened to improve the overall model.The results show that attention mechanism and feature compensation add 6.1%accuracy to multimodal low-rank bilinear pooling network.

关键词： visual question answering spatial attention mechanism channel attention mechanism image feature processing text feature extraction

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visual Superordinate Abstraction for Robust Concept Learning

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Machine Intelligence Research 2023年第1期20卷 79-91页

作者： Qi Zheng Chao-Yue Wang Dadong Wang Da-Cheng Tao University of Sydney Sydney 2008Australia JD Explore Academy Beijing 100176China DATA61 Commonwealth Scientific and Industrial Research OrganisationSydney 2122Australia

Concept learning constructs visual representations that are connected to linguistic semantics, which is fundamental to vision-language tasks. Although promising progress has been made, existing concept learners are still vulnerable to attribute perturbations and out-of-distribution compositions during inference. We ascribe the bottleneck to a failure to explore the intrinsic semantic hierarchy of visual concepts, e.g., {red, blue,···} ∈“color” subspace yet cube ∈“shape”. In this paper, we propose a visual superordinate abstraction framework for explicitly modeling semantic-aware visual subspaces(i.e., visual superordinates). With only natural visual question answering data, our model first acquires the semantic hierarchy from a linguistic view and then explores mutually exclusive visual superordinates under the guidance of linguistic hierarchy. In addition, a quasi-center visual concept clustering and superordinate shortcut learning schemes are proposed to enhance the discrimination and independence of concepts within each visual superordinate. Experiments demonstrate the superiority of the proposed framework under diverse settings, which increases the overall answering accuracy relatively by 7.5% for reasoning with perturbations and 15.6% for compositional generalization tests.

关键词： Concept learning visual question answering weakly-supervised learning multi-modal learning curriculum learning

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