Auswahl der wissenschaftlichen Literatur zum Thema „Visual question generation“
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Zeitschriftenartikel zum Thema "Visual question generation"
Patil, Charulata, und Manasi Patwardhan. „Visual Question Generation“. ACM Computing Surveys 53, Nr. 3 (05.07.2020): 1–22. http://dx.doi.org/10.1145/3383465.
Der volle Inhalt der QuelleLiu, Hongfei, Jiali Chen, Wenhao Fang, Jiayuan Xie und 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.
Der volle Inhalt der QuelleMi, Li, Syrielle Montariol, Javiera Castillo Navarro, Xianjie Dai, Antoine Bosselut und 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.
Der volle Inhalt der QuelleSarrouti, Mourad, Asma Ben Abacha und 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.
Der volle Inhalt der QuellePang, Wei, und Xiaojie Wang. „Visual Dialogue State Tracking for Question Generation“. Proceedings of the AAAI Conference on Artificial Intelligence 34, Nr. 07 (03.04.2020): 11831–38. http://dx.doi.org/10.1609/aaai.v34i07.6856.
Der volle Inhalt der QuelleKamala, 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.
Der volle Inhalt der QuelleKachare, Atul, Mukesh Kalla und 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.
Der volle Inhalt der QuelleZhu, He, Ren Togo, Takahiro Ogawa und 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.
Der volle Inhalt der QuelleBoukhers, Zeyd, Timo Hartmann und 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.
Der volle Inhalt der QuelleGuo, Zihan, Dezhi Han und 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.
Der volle Inhalt der QuelleDissertationen zum Thema "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.
Der volle Inhalt der QuelleIn 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.
Der volle Inhalt der QuelleTestoni, 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.
Der volle Inhalt der QuelleWei, Min-Chia, und 魏敏家. „Evaluation of Visual Question Generation With Captions“. Thesis, 2017. http://ndltd.ncl.edu.tw/handle/65t4uu.
Der volle Inhalt der Quelle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.
Der volle Inhalt der QuelleBücher zum Thema "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.
Der volle Inhalt der QuelleNowell Smith, David. W. S. Graham. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9780192842909.001.0001.
Der volle Inhalt der QuelleBuchner, Helmut. Evoked potentials. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199688395.003.0015.
Der volle Inhalt der QuelleFox, Kieran C. R. Neural Origins of Self-Generated Thought. Herausgegeben von Kalina Christoff und Kieran C. R. Fox. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780190464745.013.1.
Der volle Inhalt der QuelleBrantingham, Patricia L., Paul J. Brantingham, Justin Song und Valerie Spicer. Advances in Visualization for Theory Testing in Environmental Criminology. Herausgegeben von Gerben J. N. Bruinsma und Shane D. Johnson. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780190279707.013.37.
Der volle Inhalt der QuelleGover, K. E. Art and Authority. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198768692.001.0001.
Der volle Inhalt der QuelleCampbell, Kenneth L. Western Civilization in a Global Context: Prehistory to the Enlightenment. Bloomsbury Publishing Plc, 2015. http://dx.doi.org/10.5040/9781474275491.
Der volle Inhalt der QuelleContreras, Ayana. Energy Never Dies. University of Illinois Press, 2021. http://dx.doi.org/10.5622/illinois/9780252044069.001.0001.
Der volle Inhalt der QuelleBuchteile zum Thema "Visual question generation"
Wu, Qi, Peng Wang, Xin Wang, Xiaodong He und 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.
Der volle Inhalt der QuelleChen, Feng, Jiayuan Xie, Yi Cai, Tao Wang und 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.
Der volle Inhalt der QuelleXu, Feifei, Yingchen Zhou, Zheng Zhong und 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.
Der volle Inhalt der QuelleZhang, Junjie, Qi Wu, Chunhua Shen, Jian Zhang, Jianfeng Lu und 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.
Der volle Inhalt der QuelleNahar, Shrey, Shreya Naik, Niti Shah, Saumya Shah und 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.
Der volle Inhalt der QuelleUehara, Kohei, Antonio Tejero-De-Pablos, Yoshitaka Ushiku und 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.
Der volle Inhalt der QuelleChai, Zi, Xiaojun Wan, Soyeon Caren Han und 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.
Der volle Inhalt der QuelleSalewski, Leonard, A. Sophia Koepke, Hendrik P. A. Lensch und 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.
Der volle Inhalt der QuelleKoeva, 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.
Der volle Inhalt der QuelleShi, Yanan, Yanxin Tan, Fangxiang Feng, Chunping Zheng und 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.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Visual question generation"
Vedd, Nihir, Zixu Wang, Marek Rei, Yishu Miao und 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.
Der volle Inhalt der QuelleBi, Chao, Shuhui Wang, Zhe Xue, Shengbo Chen und 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.
Der volle Inhalt der QuelleZhang, Shijie, Lizhen Qu, Shaodi You, Zhenglu Yang und 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.
Der volle Inhalt der QuelleFan, Zhihao, Zhongyu Wei, Piji Li, Yanyan Lan und 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.
Der volle Inhalt der QuelleLi, Yikang, Nan Duan, Bolei Zhou, Xiao Chu, Wanli Ouyang, Xiaogang Wang und 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.
Der volle Inhalt der QuelleKrishna, Ranjay, Michael Bernstein und 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.
Der volle Inhalt der QuellePatil, Charulata, und 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.
Der volle Inhalt der QuelleXie, Jiayuan, Yi Cai, Qingbao Huang und 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.
Der volle Inhalt der QuelleXu, Xing, Jingkuan Song, Huimin Lu, Li He, Yang Yang und 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.
Der volle Inhalt der QuelleRathi, Snehal, Atharv Raje, Gauri Ghule, Shruti Sankpal, Soham Shitole und 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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