2020 IEEE International Conference on Humanized Computing and Communication with Artificial Intelligence (HCCAI)最新文献

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2020 IEEE International Conference on Humanized Computing and Communication with Artificial Intelligence (HCCAI) Pub Date : 2020-09-01 DOI: 10.1109/HCCAI49649.2020.00009

Zanyar Zohourianshahzadi, J. Kalita

{"title":"Neural Twins Talk","authors":"Zanyar Zohourianshahzadi, J. Kalita","doi":"10.1109/HCCAI49649.2020.00009","DOIUrl":"https://doi.org/10.1109/HCCAI49649.2020.00009","url":null,"abstract":"Inspired by how the human brain employs more neural pathways when increasing the focus on a subject, we introduce a novel twin cascaded attention model that outperforms a state-of-the-art image captioning model that was originally implemented using one channel of attention for the visual grounding task. Visual grounding ensures the existence of words in the caption sentence that are grounded into a particular region in the input image. After a deep learning model is trained on visual grounding task, the model employs the learned patterns regarding the visual grounding and the order of objects in the caption sentences, when generating captions. We report the results of our experiments in three image captioning tasks on the COCO dataset. The results are reported using standard image captioning metrics to show the improvements achieved by our model over the previous image captioning model. The results gathered from our experiments suggest that employing more parallel attention pathways in a deep neural network leads to higher performance. Our implementation of NTT is publicly available at: https://github.com/zanyarz/NeuralTwinsTalk.","PeriodicalId":444855,"journal":{"name":"2020 IEEE International Conference on Humanized Computing and Communication with Artificial Intelligence (HCCAI)","volume":"20 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132899833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

The Task and Workslip Scheme for Personal Agent Systems 个人代理系统的任务和工作单计划

2020 IEEE International Conference on Humanized Computing and Communication with Artificial Intelligence (HCCAI) Pub Date : 2020-09-01 DOI: 10.1109/HCCAI49649.2020.00012

Tyson Kendon

引用次数: 0

The Impact of Explanations on AI Competency Prediction in VQA VQA中解释对人工智能能力预测的影响

2020 IEEE International Conference on Humanized Computing and Communication with Artificial Intelligence (HCCAI) Pub Date : 2020-07-02 DOI: 10.1109/HCCAI49649.2020.00010

Kamran Alipour, Arijit Ray, Xiaoyu Lin, J. Schulze, Yi Yao, Giedrius Burachas

{"title":"The Impact of Explanations on AI Competency Prediction in VQA","authors":"Kamran Alipour, Arijit Ray, Xiaoyu Lin, J. Schulze, Yi Yao, Giedrius Burachas","doi":"10.1109/HCCAI49649.2020.00010","DOIUrl":"https://doi.org/10.1109/HCCAI49649.2020.00010","url":null,"abstract":"Explainability is one of the key elements for building trust in AI systems. Among numerous attempts to make AI explainable, quantifying the effect of explanations remains a challenge in conducting human-AI collaborative tasks. Aside from the ability to predict the overall behavior of AI, in many applications, users need to understand an AI agent's competency in different aspects of the task domain. In this paper, we evaluate the impact of explanations on the user's mental model of AI agent competency within the task of visual question answering (VQA). We quantify users' understanding of competency, based on the correlation between the actual system performance and user rankings. We introduce an explainable VQA system that uses spatial and object features and is powered by the BERT language model. Each group of users sees only one kind of explanation to rank the competencies of the VQA model. The proposed model is evaluated through between-subject experiments to probe explanations' impact on the user's perception of competency. The comparison between two VQA models shows BERT based explanations and the use of object features improve the user's prediction of the model's competencies.","PeriodicalId":444855,"journal":{"name":"2020 IEEE International Conference on Humanized Computing and Communication with Artificial Intelligence (HCCAI)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128764151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

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