Computer Science ›› 2025, Vol. 52 ›› Issue (1): 315-322.doi: 10.11896/jsjkx.231100107
• Artificial Intelligence • Previous Articles Next Articles
ZHAO Qian, GUO Bin, LIU Yubo, SUN Zhuo, WANG Hao, CHEN Mengqi
CLC Number:
[1]XU W,DAINOFF M J,GE L,et al.Transitioning to human interaction with AI systems:New challenges and opportunities for HCI professionals to enable human-centered AI[J].Interna-tional Journal of Human-Computer Interaction,2023,39(3):494-518. [2]YUSUF A A,FENG C,MAO X L.An analysis of graph con-volutional networks and recent datasets for visual question answering[J].Artificial Intelligence Review,2022,55(8):6277-6300. [3]LIN X,BERTASIUS G,WANG J,et al.Vx2text:End-to-endlearning of video-based text generation from multimodal inputs[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.Nashville:IEEE Press,2021:7005-7015. [4]WANG H Y,HUANG J Y,LEE W P.Integrating Scene Image and Conversational Text to Develop Human-Machine Dialogue[J].International Journal of Semantic Computing,2022,16(3):425-447. [5]SERBAN I V,SORDONI A,LOWE R,et al.A hierarchical latent variable encoder-decoder model for generating dialogues[C]//Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence.San Francisco:AAAI Press,2017:3295-3301. [6]SERBAN I V,SORDONI A,BENGIO Y,et al.Building end-to-end dialogue systems using generative hierarchical neural network models[C]//Proceedings of the Thirtieth AAAI Confe-rence on Artificial Intelligence.Phoenix:AAAI Press,2016:3776-3783. [7]WESTON J.Dialog-based language learning[C]//Proceedings of the 30th International Conference on Neural Information Processing Systems.Barcelona:AAAI Press,2016:829-837. [8]XING C,WU Y,WU W,et al.Hierarchical recurrent attention network for response generation[C]//Proceedings of the 32nd AAAI Conference on Artificial Intelligence.New Orleans:AAAI Press,2018:5610-5617. [9]ZHOU G,LUO P,CAO R,et al.Mechanism-aware neural machine for dialogue response generation[C]//Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence.San Francisco:AAAI Press,2017:3400-3406. [10]WU Y,WU W,XING C,et al.Sequential Matching Network:A New Architecture for Multi-turn Response Selection in Retrie-val-Based Chatbots[C]//Proceedings of the 55th Annual Mee-ting of the Association for Computational Linguistics.Vancouver:Association for Computational Linguistics,2017:496-505. [11]LIU X,ZHENG Y,DU Z,et al.GPT understands,too[J].ar-Xiv:2103.10385,2023. [12]NAZIR A,WANG Z.A Comprehensive Survey of ChatGPT:Advancements,Applications,Prospects,and Challenges[J].Metaradiology,2023,1(4):100022. [13]WU T,HE S,LIU J,et al.A brief overview of ChatGPT:The history,status quo and potential future development[J].IEEE/CAA Journal of Automatica Sinica,2023,10(5):1122-1136. [14]YU Y,KO H,CHOI J,et al.End-to-end concept word detection for video captioning,retrieval,and question answering[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Honolulu:IEEE Press,2017:3165-3173. [15]JANG Y,SONG Y,YU Y,et al.Tgif-qa:Toward spatio-temporal reasoning in visual question answering[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Honolulu:IEEE Press,2017:2758-2766. [16]GARCIA N,NAKASHIMA Y.Knowledge-based video question answering with unsupervised scene descriptions[C]//European Conference on Computer Vision.Glasgow:Springer,2020:581-598. [17]LE H,CHEN N,HOI S.Vgnmn:Video-grounded neural module networks for video-grounded dialogue systems[C]//Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies.Seattle:Association for Computational Linguistics,2022:3377-3393. [18]HAQUE M F,LIM H Y,KANG D S.Object detection based on VGG with ResNet network[C]//International Conference on Electronics,Information,and Communication(ICEIC).Auckland:IEEE Press,2019:1-3. [19]CARREIRA J,ZISSERMAN A.Quo vadis,action recognition? a new model and the kinetics dataset[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Honolulu:IEEE Press,2017:6299-6308. [20]CHURCH K W.Word2Vec[J].Natural Language Engineering,2017,23(1):155-162. [21]LAGLER K,SCHINDELEGGER M,BÖHM J,et al.GPT2:Empirical slant delay model for radio space geodetic techniques[J].Geophysical Research Letters,2013,40(6):1069-1073. [22]BHATTACHARJEE D,ZHANG T,SÜSSTRUNK S,et al.Mult:an end-to-end multitask learning transformer [C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.New Orleans:IEEE Press,2022:12031-12041. [23]YE M,YOU Q,MA F.QUALIFIER:Question-Guided Self-Attentive Multimodal Fusion Network for Audio Visual Scene-Aware Dialog[C]//Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Visison.Waikoloa:IEEE Press,2022:248-256. [24]HORI C,ALAMRI H,WANG J,et al.End-to-end audio visualscene-aware dialog using multimodal attention-based video features[C]//ICASSP 2019-2019 IEEE International Conference on Acoustics,Speech and Signal Processing(ICASSP).Brighton:IEEE Press,2019:2352-2356. [25]LE H,SAHOO D,CHEN N F,et al.Multimodal transformernetworks for end-to-end video-grounded dialogue systems[C]//Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics.Florence:Association for Computational Linguistics,2019:5612-5623. [26]LE H,SAHOO D,CHEN N,et al.BiST:Bi-directional Spatio-Temporal Reasoning for Video-Grounded Dialogues[C]//Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing(EMNLP).Stroudsburg:Association for Computational Linguistics,2020:1846-1859. [27]GENG S,GAO P,CHATTERJEE M,et al.Dynamic graph representation learning for video dialog via multi-modal shuffled transformers[C]//Proceedings of the AAAI Conference on Artificial Intelligence.California:AAAI Press,2021:1415-1423. [28]PAPINENI K,ROUKOS S,WARD T,et al.Bleu:a method for automatic evaluation of machine translation[C]//Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics.Philadelphia:Association for Computational Linguistics,2002:311-318. [29]LIN C Y.Rouge:A package for automatic evaluation of summaries[C]//Proceedings of theWorkshop on Text Summarization Branches Out.Barcelona:Springer,2004:74-81. [30]BANERJEE S,LAVIE A.METEOR:An automatic metric forMT evaluation with improved correlation with human judgments[C]//Proceedings of the acl Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization.Ann Arbor:Association for Computational Linguistics,2005:65-72. [31]VEDANTAM R,LAWRENCE ZITNICK C,PARIKH D.Ci-der:Consensus-based image description evaluation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Boston:IEEE Press,2015:4566-4575. |
[1] | ZHANG Haoyan, DUAN Liguo, WANG Qinchen, GAO Hao. Long Text Multi-entity Sentiment Analysis Based on Multi-task Joint Training [J]. Computer Science, 2024, 51(6): 309-316. |
[2] | LIU Zeyu, LIU Jianwei. Video and Image Salient Object Detection Based on Multi-task Learning [J]. Computer Science, 2024, 51(4): 217-228. |
[3] | ZHANG Xue, TIAN Lan, ZENG Ming, LIU Junhui, ZONG Shaoguo. Multitask Classification Algorithm of ECG Signals Based on Radient Magnitude Direction Adjustment [J]. Computer Science, 2024, 51(12): 174-180. |
[4] | FU Mingrui, LI Weijiang. Multi-task Emotion-Cause Pair Extraction Method Based on Position-aware Interaction Network [J]. Computer Science, 2024, 51(11A): 231000086-9. |
[5] | WANG Kunyang, LIU Yang, YE Ning, ZHANG Kai. Road Extraction from Complex Urban Remote Sensing Images Based on Multi-task Learning [J]. Computer Science, 2024, 51(11A): 240300095-8. |
[6] | XU Bei, XU Peng. Emotion Elicited Question Generation Model in Dialogue Scenarios [J]. Computer Science, 2024, 51(11): 265-272. |
[7] | ZHANG Xiaoyun, ZHAO Hui. Study on Multi-task Student Emotion Recognition Methods Based on Facial Action Units [J]. Computer Science, 2024, 51(10): 105-111. |
[8] | LUO Huilan, YE Ju. Study of Multi-task Learning with Joint Semantic Segmentation and Depth Estimation [J]. Computer Science, 2023, 50(6A): 220100111-10. |
[9] | ZHEN Tiange, SONG Mingyang, JING Liping. Incorporating Multi-granularity Extractive Features for Keyphrase Generation [J]. Computer Science, 2023, 50(4): 181-187. |
[10] | DU Li-jun, TANG Xi-lu, ZHOU Jiao, CHEN Yu-lan, CHENG Jian. Alzheimer's Disease Classification Method Based on Attention Mechanism and Multi-task Learning [J]. Computer Science, 2022, 49(6A): 60-65. |
[11] | ZHAO Kai, AN Wei-chao, ZHANG Xiao-yu, WANG Bin, ZHANG Shan, XIANG Jie. Intracerebral Hemorrhage Image Segmentation and Classification Based on Multi-taskLearning of Shared Shallow Parameters [J]. Computer Science, 2022, 49(4): 203-208. |
[12] | YANG Xiao-yu, YIN Kang-ning, HOU Shao-qi, DU Wen-yi, YIN Guang-qiang. Person Re-identification Based on Feature Location and Fusion [J]. Computer Science, 2022, 49(3): 170-178. |
[13] | ZHENG Shun-yuan, HU Liang-xiao, LYU Xiao-qian, SUN Xin, ZHANG Sheng-ping. Edge Guided Self-correction Skin Detection [J]. Computer Science, 2022, 49(11): 141-147. |
[14] | SONG Long-ze, WAN Huai-yu, GUO Sheng-nan, LIN You-fang. Multi-task Spatial-Temporal Graph Convolutional Network for Taxi Idle Time Prediction [J]. Computer Science, 2021, 48(7): 112-117. |
[15] | LIU Xiao-long, HAN Fang, WANG Zhi-jie. Joint Question Answering Model Based on Knowledge Representation [J]. Computer Science, 2021, 48(6): 241-245. |
|