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Generating Natural Language Explanations for Visual Question Answering using Scene Graphs and Visual Attention.

, , , and . CoRR, (2019)

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Can You Explain That? Lucid Explanations Help Human-AI Collaborative Image Retrieval., , , , and . HCOMP, page 153-161. AAAI Press, (2019)Modular Adaptation for Cross-Domain Few-Shot Learning., , , , , , and . CoRR, (2021)Hybrid Consistency Training with Prototype Adaptation for Few-Shot Learning., , , , and . CoRR, (2020)Hybrid Consistency Training with Prototype Adaptation for Few-Shot Learning., , , , and . CVPR Workshops, page 2725-2734. IEEE, (2022)Generating Natural Language Explanations for Visual Question Answering using Scene Graphs and Visual Attention., , , and . CoRR, (2019)A Study on Multimodal and Interactive Explanations for Visual Question Answering., , , , and . SafeAI@AAAI, volume 2560 of CEUR Workshop Proceedings, page 54-62. CEUR-WS.org, (2020)Sunny and Dark Outside?! Improving Answer Consistency in VQA through Entailed Question Generation., , , , and . EMNLP/IJCNLP (1), page 5859-5864. Association for Computational Linguistics, (2019)Lucid Explanations Help: Using a Human-AI Image-Guessing Game to Evaluate Machine Explanation Helpfulness., , , and . CoRR, (2019)Training Deep Spiking Neural Networks., , , and . CoRR, (2020)Knowing What VQA Does Not: Pointing to Error-Inducing Regions to Improve Explanation Helpfulness., , , , , , and . CoRR, (2021)