Literatura científica selecionada sobre o tema "Visual question generation"
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Artigos de revistas sobre o assunto "Visual question generation"
Patil, Charulata, e Manasi Patwardhan. "Visual Question Generation". ACM Computing Surveys 53, n.º 3 (5 de julho de 2020): 1–22. http://dx.doi.org/10.1145/3383465.
Texto completo da fonteLiu, Hongfei, Jiali Chen, Wenhao Fang, Jiayuan Xie e Yi Cai. "Category-Guided Visual Question Generation (Student Abstract)". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 13 (26 de junho de 2023): 16262–63. http://dx.doi.org/10.1609/aaai.v37i13.26991.
Texto completo da fonteMi, Li, Syrielle Montariol, Javiera Castillo Navarro, Xianjie Dai, Antoine Bosselut e Devis Tuia. "ConVQG: Contrastive Visual Question Generation with Multimodal Guidance". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 5 (24 de março de 2024): 4207–15. http://dx.doi.org/10.1609/aaai.v38i5.28216.
Texto completo da fonteSarrouti, Mourad, Asma Ben Abacha e Dina Demner-Fushman. "Goal-Driven Visual Question Generation from Radiology Images". Information 12, n.º 8 (20 de agosto de 2021): 334. http://dx.doi.org/10.3390/info12080334.
Texto completo da fontePang, Wei, e Xiaojie Wang. "Visual Dialogue State Tracking for Question Generation". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 07 (3 de abril de 2020): 11831–38. http://dx.doi.org/10.1609/aaai.v34i07.6856.
Texto completo da fonteKamala, M. "Visual Question Generation from Remote Sensing Images Using Gemini API". International Journal for Research in Applied Science and Engineering Technology 12, n.º 3 (31 de março de 2024): 2924–29. http://dx.doi.org/10.22214/ijraset.2024.59537.
Texto completo da fonteKachare, Atul, Mukesh Kalla e Ashutosh Gupta. "Visual Question Generation Answering (VQG-VQA) using Machine Learning Models". WSEAS TRANSACTIONS ON SYSTEMS 22 (28 de junho de 2023): 663–70. http://dx.doi.org/10.37394/23202.2023.22.67.
Texto completo da fonteZhu, He, Ren Togo, Takahiro Ogawa e Miki Haseyama. "Diversity Learning Based on Multi-Latent Space for Medical Image Visual Question Generation". Sensors 23, n.º 3 (17 de janeiro de 2023): 1057. http://dx.doi.org/10.3390/s23031057.
Texto completo da fonteBoukhers, Zeyd, Timo Hartmann e Jan Jürjens. "COIN: Counterfactual Image Generation for Visual Question Answering Interpretation". Sensors 22, n.º 6 (14 de março de 2022): 2245. http://dx.doi.org/10.3390/s22062245.
Texto completo da fonteGuo, Zihan, Dezhi Han e Kuan-Ching Li. "Double-layer affective visual question answering network". Computer Science and Information Systems, n.º 00 (2020): 38. http://dx.doi.org/10.2298/csis200515038g.
Texto completo da fonteTeses / dissertações sobre o assunto "Visual question generation"
Bordes, Patrick. "Deep Multimodal Learning for Joint Textual and Visual Reasoning". Electronic Thesis or Diss., Sorbonne université, 2020. http://www.theses.fr/2020SORUS370.
Texto completo da fonteIn the last decade, the evolution of Deep Learning techniques to learn meaningful data representations for text and images, combined with an important increase of multimodal data, mainly from social network and e-commerce websites, has triggered a growing interest in the research community about the joint understanding of language and vision. The challenge at the heart of Multimodal Machine Learning is the intrinsic difference in semantics between language and vision: while vision faithfully represents reality and conveys low-level semantics, language is a human construction carrying high-level reasoning. One the one hand, language can enhance the performance of vision models. The underlying hypothesis is that textual representations contain visual information. We apply this principle to two Zero-Shot Learning tasks. In the first contribution on ZSL, we extend a common assumption in ZSL, which states that textual representations encode information about the visual appearance of objects, by showing that they also encode information about their visual surroundings and their real-world frequence. In a second contribution, we consider the transductive setting in ZSL. We propose a solution to the limitations of current transductive approaches, that assume that the visual space is well-clustered, which does not hold true when the number of unknown classes is high. On the other hand, vision can expand the capacities of language models. We demonstrate it by tackling Visual Question Generation (VQG), which extends the standard Question Generation task by using an image as complementary input, by using visual representations derived from Computer Vision
Chowdhury, Muhammad Iqbal Hasan. "Question-answering on image/video content". Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/205096/1/Muhammad%20Iqbal%20Hasan_Chowdhury_Thesis.pdf.
Texto completo da fonteTestoni, Alberto. "Asking Strategic and Informative Questions in Visual Dialogue Games: Strengths and Weaknesses of Neural Generative Models". Doctoral thesis, Università degli studi di Trento, 2023. https://hdl.handle.net/11572/370672.
Texto completo da fonteWei, Min-Chia, e 魏敏家. "Evaluation of Visual Question Generation With Captions". Thesis, 2017. http://ndltd.ncl.edu.tw/handle/65t4uu.
Texto completo da fonte國立臺灣大學
資訊工程學研究所
106
Over the last few years, there have been many types of research in the vision and language community. There are many popular topics, for example, image captions, video transcription, question answering about images or videos, Image-Grounded Conversation(IGC) and Visual Question Generation(VQG). In this thesis, we focus on question generation about images. Because of the popularity of image on social media, people always upload an image with some descriptions, we think that maybe image captions can help Artificial Intelligence (AI) to learn to ask more natural questions. We proposed new pipeline models for fusing both visual and textual features, do experiments on different models and compare the prediction questions. In our results of experiments, the captions are definitely useful for visual question generation.
Anderson, Peter James. "Vision and Language Learning: From Image Captioning and Visual Question Answering towards Embodied Agents". Phd thesis, 2018. http://hdl.handle.net/1885/164018.
Texto completo da fonteLivros sobre o assunto "Visual question generation"
Dadyan, Eduard. Modern programming technologies. The C#language. Volume 1. For novice users. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1196552.
Texto completo da fonteNowell Smith, David. W. S. Graham. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9780192842909.001.0001.
Texto completo da fonteBuchner, Helmut. Evoked potentials. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199688395.003.0015.
Texto completo da fonteFox, Kieran C. R. Neural Origins of Self-Generated Thought. Editado por Kalina Christoff e Kieran C. R. Fox. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780190464745.013.1.
Texto completo da fonteBrantingham, Patricia L., Paul J. Brantingham, Justin Song e Valerie Spicer. Advances in Visualization for Theory Testing in Environmental Criminology. Editado por Gerben J. N. Bruinsma e Shane D. Johnson. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780190279707.013.37.
Texto completo da fonteGover, K. E. Art and Authority. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198768692.001.0001.
Texto completo da fonteCampbell, Kenneth L. Western Civilization in a Global Context: Prehistory to the Enlightenment. Bloomsbury Publishing Plc, 2015. http://dx.doi.org/10.5040/9781474275491.
Texto completo da fonteContreras, Ayana. Energy Never Dies. University of Illinois Press, 2021. http://dx.doi.org/10.5622/illinois/9780252044069.001.0001.
Texto completo da fonteCapítulos de livros sobre o assunto "Visual question generation"
Wu, Qi, Peng Wang, Xin Wang, Xiaodong He e Wenwu Zhu. "Visual Question Generation". In Visual Question Answering, 189–97. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0964-1_13.
Texto completo da fonteChen, Feng, Jiayuan Xie, Yi Cai, Tao Wang e Qing Li. "Difficulty-Controllable Visual Question Generation". In Web and Big Data, 332–47. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85896-4_26.
Texto completo da fonteXu, Feifei, Yingchen Zhou, Zheng Zhong e Guangzhen Li. "Object Category-Based Visual Dialog for Effective Question Generation". In Computational Visual Media, 316–31. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-2092-7_16.
Texto completo da fonteZhang, Junjie, Qi Wu, Chunhua Shen, Jian Zhang, Jianfeng Lu e Anton van den Hengel. "Goal-Oriented Visual Question Generation via Intermediate Rewards". In Computer Vision – ECCV 2018, 189–204. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01228-1_12.
Texto completo da fonteNahar, Shrey, Shreya Naik, Niti Shah, Saumya Shah e Lakshmi Kurup. "Automated Question Generation and Answer Verification Using Visual Data". In Studies in Computational Intelligence, 99–114. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-38445-6_8.
Texto completo da fonteUehara, Kohei, Antonio Tejero-De-Pablos, Yoshitaka Ushiku e Tatsuya Harada. "Visual Question Generation for Class Acquisition of Unknown Objects". In Computer Vision – ECCV 2018, 492–507. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01258-8_30.
Texto completo da fonteChai, Zi, Xiaojun Wan, Soyeon Caren Han e Josiah Poon. "Visual Question Generation Under Multi-granularity Cross-Modal Interaction". In MultiMedia Modeling, 255–66. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-27077-2_20.
Texto completo da fonteSalewski, Leonard, A. Sophia Koepke, Hendrik P. A. Lensch e Zeynep Akata. "CLEVR-X: A Visual Reasoning Dataset for Natural Language Explanations". In xxAI - Beyond Explainable AI, 69–88. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04083-2_5.
Texto completo da fonteKoeva, Svetla. "Multilingual Image Corpus". In European Language Grid, 313–18. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-17258-8_22.
Texto completo da fonteShi, Yanan, Yanxin Tan, Fangxiang Feng, Chunping Zheng e Xiaojie Wang. "Category-Based Strategy-Driven Question Generator for Visual Dialogue". In Lecture Notes in Computer Science, 177–92. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-84186-7_12.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Visual question generation"
Vedd, Nihir, Zixu Wang, Marek Rei, Yishu Miao e Lucia Specia. "Guiding Visual Question Generation". In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Stroudsburg, PA, USA: Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.naacl-main.118.
Texto completo da fonteBi, Chao, Shuhui Wang, Zhe Xue, Shengbo Chen e Qingming Huang. "Inferential Visual Question Generation". In MM '22: The 30th ACM International Conference on Multimedia. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3503161.3548055.
Texto completo da fonteZhang, Shijie, Lizhen Qu, Shaodi You, Zhenglu Yang e Jiawan Zhang. "Automatic Generation of Grounded Visual Questions". In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/592.
Texto completo da fonteFan, Zhihao, Zhongyu Wei, Piji Li, Yanyan Lan e Xuanjing Huang. "A Question Type Driven Framework to Diversify Visual Question Generation". In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/563.
Texto completo da fonteLi, Yikang, Nan Duan, Bolei Zhou, Xiao Chu, Wanli Ouyang, Xiaogang Wang e Ming Zhou. "Visual Question Generation as Dual Task of Visual Question Answering". In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2018. http://dx.doi.org/10.1109/cvpr.2018.00640.
Texto completo da fonteKrishna, Ranjay, Michael Bernstein e Li Fei-Fei. "Information Maximizing Visual Question Generation". In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2019. http://dx.doi.org/10.1109/cvpr.2019.00211.
Texto completo da fontePatil, Charulata, e Anagha Kulkarni. "Attention-based Visual Question Generation". In 2021 International Conference on Emerging Smart Computing and Informatics (ESCI). IEEE, 2021. http://dx.doi.org/10.1109/esci50559.2021.9396956.
Texto completo da fonteXie, Jiayuan, Yi Cai, Qingbao Huang e Tao Wang. "Multiple Objects-Aware Visual Question Generation". In MM '21: ACM Multimedia Conference. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3474085.3476969.
Texto completo da fonteXu, Xing, Jingkuan Song, Huimin Lu, Li He, Yang Yang e Fumin Shen. "Dual Learning for Visual Question Generation". In 2018 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2018. http://dx.doi.org/10.1109/icme.2018.8486475.
Texto completo da fonteRathi, Snehal, Atharv Raje, Gauri Ghule, Shruti Sankpal, Soham Shitole e Priyanka More. "Visual Question Generation Using Deep Learning". In 2023 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS). IEEE, 2023. http://dx.doi.org/10.1109/icccis60361.2023.10425302.
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