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Artykuły w czasopismach na temat "Visual question generation"
Patil, Charulata, i Manasi Patwardhan. "Visual Question Generation". ACM Computing Surveys 53, nr 3 (5.07.2020): 1–22. http://dx.doi.org/10.1145/3383465.
Pełny tekst źródłaLiu, Hongfei, Jiali Chen, Wenhao Fang, Jiayuan Xie i Yi Cai. "Category-Guided Visual Question Generation (Student Abstract)". Proceedings of the AAAI Conference on Artificial Intelligence 37, nr 13 (26.06.2023): 16262–63. http://dx.doi.org/10.1609/aaai.v37i13.26991.
Pełny tekst źródłaMi, Li, Syrielle Montariol, Javiera Castillo Navarro, Xianjie Dai, Antoine Bosselut i Devis Tuia. "ConVQG: Contrastive Visual Question Generation with Multimodal Guidance". Proceedings of the AAAI Conference on Artificial Intelligence 38, nr 5 (24.03.2024): 4207–15. http://dx.doi.org/10.1609/aaai.v38i5.28216.
Pełny tekst źródłaSarrouti, Mourad, Asma Ben Abacha i Dina Demner-Fushman. "Goal-Driven Visual Question Generation from Radiology Images". Information 12, nr 8 (20.08.2021): 334. http://dx.doi.org/10.3390/info12080334.
Pełny tekst źródłaPang, Wei, i Xiaojie Wang. "Visual Dialogue State Tracking for Question Generation". Proceedings of the AAAI Conference on Artificial Intelligence 34, nr 07 (3.04.2020): 11831–38. http://dx.doi.org/10.1609/aaai.v34i07.6856.
Pełny tekst źródłaKamala, M. "Visual Question Generation from Remote Sensing Images Using Gemini API". International Journal for Research in Applied Science and Engineering Technology 12, nr 3 (31.03.2024): 2924–29. http://dx.doi.org/10.22214/ijraset.2024.59537.
Pełny tekst źródłaKachare, Atul, Mukesh Kalla i Ashutosh Gupta. "Visual Question Generation Answering (VQG-VQA) using Machine Learning Models". WSEAS TRANSACTIONS ON SYSTEMS 22 (28.06.2023): 663–70. http://dx.doi.org/10.37394/23202.2023.22.67.
Pełny tekst źródłaZhu, He, Ren Togo, Takahiro Ogawa i Miki Haseyama. "Diversity Learning Based on Multi-Latent Space for Medical Image Visual Question Generation". Sensors 23, nr 3 (17.01.2023): 1057. http://dx.doi.org/10.3390/s23031057.
Pełny tekst źródłaBoukhers, Zeyd, Timo Hartmann i Jan Jürjens. "COIN: Counterfactual Image Generation for Visual Question Answering Interpretation". Sensors 22, nr 6 (14.03.2022): 2245. http://dx.doi.org/10.3390/s22062245.
Pełny tekst źródłaGuo, Zihan, Dezhi Han i Kuan-Ching Li. "Double-layer affective visual question answering network". Computer Science and Information Systems, nr 00 (2020): 38. http://dx.doi.org/10.2298/csis200515038g.
Pełny tekst źródłaRozprawy doktorskie na temat "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.
Pełny tekst źródłaIn 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.
Pełny tekst źródłaTestoni, 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.
Pełny tekst źródłaWei, Min-Chia, i 魏敏家. "Evaluation of Visual Question Generation With Captions". Thesis, 2017. http://ndltd.ncl.edu.tw/handle/65t4uu.
Pełny tekst źródła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.
Pełny tekst źródłaKsiążki na temat "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.
Pełny tekst źródłaNowell Smith, David. W. S. Graham. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9780192842909.001.0001.
Pełny tekst źródłaBuchner, Helmut. Evoked potentials. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199688395.003.0015.
Pełny tekst źródłaFox, Kieran C. R. Neural Origins of Self-Generated Thought. Redaktorzy Kalina Christoff i Kieran C. R. Fox. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780190464745.013.1.
Pełny tekst źródłaBrantingham, Patricia L., Paul J. Brantingham, Justin Song i Valerie Spicer. Advances in Visualization for Theory Testing in Environmental Criminology. Redaktorzy Gerben J. N. Bruinsma i Shane D. Johnson. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780190279707.013.37.
Pełny tekst źródłaGover, K. E. Art and Authority. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198768692.001.0001.
Pełny tekst źródłaCampbell, Kenneth L. Western Civilization in a Global Context: Prehistory to the Enlightenment. Bloomsbury Publishing Plc, 2015. http://dx.doi.org/10.5040/9781474275491.
Pełny tekst źródłaContreras, Ayana. Energy Never Dies. University of Illinois Press, 2021. http://dx.doi.org/10.5622/illinois/9780252044069.001.0001.
Pełny tekst źródłaCzęści książek na temat "Visual question generation"
Wu, Qi, Peng Wang, Xin Wang, Xiaodong He i Wenwu Zhu. "Visual Question Generation". W Visual Question Answering, 189–97. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0964-1_13.
Pełny tekst źródłaChen, Feng, Jiayuan Xie, Yi Cai, Tao Wang i Qing Li. "Difficulty-Controllable Visual Question Generation". W Web and Big Data, 332–47. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85896-4_26.
Pełny tekst źródłaXu, Feifei, Yingchen Zhou, Zheng Zhong i Guangzhen Li. "Object Category-Based Visual Dialog for Effective Question Generation". W Computational Visual Media, 316–31. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-2092-7_16.
Pełny tekst źródłaZhang, Junjie, Qi Wu, Chunhua Shen, Jian Zhang, Jianfeng Lu i Anton van den Hengel. "Goal-Oriented Visual Question Generation via Intermediate Rewards". W Computer Vision – ECCV 2018, 189–204. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01228-1_12.
Pełny tekst źródłaNahar, Shrey, Shreya Naik, Niti Shah, Saumya Shah i Lakshmi Kurup. "Automated Question Generation and Answer Verification Using Visual Data". W Studies in Computational Intelligence, 99–114. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-38445-6_8.
Pełny tekst źródłaUehara, Kohei, Antonio Tejero-De-Pablos, Yoshitaka Ushiku i Tatsuya Harada. "Visual Question Generation for Class Acquisition of Unknown Objects". W Computer Vision – ECCV 2018, 492–507. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01258-8_30.
Pełny tekst źródłaChai, Zi, Xiaojun Wan, Soyeon Caren Han i Josiah Poon. "Visual Question Generation Under Multi-granularity Cross-Modal Interaction". W MultiMedia Modeling, 255–66. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-27077-2_20.
Pełny tekst źródłaSalewski, Leonard, A. Sophia Koepke, Hendrik P. A. Lensch i Zeynep Akata. "CLEVR-X: A Visual Reasoning Dataset for Natural Language Explanations". W xxAI - Beyond Explainable AI, 69–88. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04083-2_5.
Pełny tekst źródłaKoeva, Svetla. "Multilingual Image Corpus". W European Language Grid, 313–18. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-17258-8_22.
Pełny tekst źródłaShi, Yanan, Yanxin Tan, Fangxiang Feng, Chunping Zheng i Xiaojie Wang. "Category-Based Strategy-Driven Question Generator for Visual Dialogue". W Lecture Notes in Computer Science, 177–92. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-84186-7_12.
Pełny tekst źródłaStreszczenia konferencji na temat "Visual question generation"
Vedd, Nihir, Zixu Wang, Marek Rei, Yishu Miao i Lucia Specia. "Guiding Visual Question Generation". W 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.
Pełny tekst źródłaBi, Chao, Shuhui Wang, Zhe Xue, Shengbo Chen i Qingming Huang. "Inferential Visual Question Generation". W MM '22: The 30th ACM International Conference on Multimedia. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3503161.3548055.
Pełny tekst źródłaZhang, Shijie, Lizhen Qu, Shaodi You, Zhenglu Yang i Jiawan Zhang. "Automatic Generation of Grounded Visual Questions". W 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.
Pełny tekst źródłaFan, Zhihao, Zhongyu Wei, Piji Li, Yanyan Lan i Xuanjing Huang. "A Question Type Driven Framework to Diversify Visual Question Generation". W 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.
Pełny tekst źródłaLi, Yikang, Nan Duan, Bolei Zhou, Xiao Chu, Wanli Ouyang, Xiaogang Wang i Ming Zhou. "Visual Question Generation as Dual Task of Visual Question Answering". W 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2018. http://dx.doi.org/10.1109/cvpr.2018.00640.
Pełny tekst źródłaKrishna, Ranjay, Michael Bernstein i Li Fei-Fei. "Information Maximizing Visual Question Generation". W 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2019. http://dx.doi.org/10.1109/cvpr.2019.00211.
Pełny tekst źródłaPatil, Charulata, i Anagha Kulkarni. "Attention-based Visual Question Generation". W 2021 International Conference on Emerging Smart Computing and Informatics (ESCI). IEEE, 2021. http://dx.doi.org/10.1109/esci50559.2021.9396956.
Pełny tekst źródłaXie, Jiayuan, Yi Cai, Qingbao Huang i Tao Wang. "Multiple Objects-Aware Visual Question Generation". W MM '21: ACM Multimedia Conference. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3474085.3476969.
Pełny tekst źródłaXu, Xing, Jingkuan Song, Huimin Lu, Li He, Yang Yang i Fumin Shen. "Dual Learning for Visual Question Generation". W 2018 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2018. http://dx.doi.org/10.1109/icme.2018.8486475.
Pełny tekst źródłaRathi, Snehal, Atharv Raje, Gauri Ghule, Shruti Sankpal, Soham Shitole i Priyanka More. "Visual Question Generation Using Deep Learning". W 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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