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Training Multimedia Event Extraction With Generated Images and Captions

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Boyang LiAuthors Info & Claims

MM '23: Proceedings of the 31st ACM International Conference on Multimedia

Pages 5504 - 5513

https://doi.org/10.1145/3581783.3612526

Published: 27 October 2023 Publication History

Abstract

Contemporary news reporting increasingly features multimedia content, motivating research on multimedia event extraction. However, the task lacks annotated multimodal training data and artificially generated training data suffer from the distribution shift from the real-world data. In this paper, we propose Cross-modality Augmented Multimedia Event Learning (CAMEL), which successfully utilizes artificially generated multimodal training data and achieves state-of-the-art performance. Conditioned on unimodal training data, we generate multimodal training data using off-the-shelf image generators like Stable Diffusion [45] and image captioners like BLIP [24]. After that, we train the network on the resultant multimodal datasets. In order to learn robust features that are effective across domains, we devise an iterative and gradual training strategy. Substantial experiments show that CAMEL surpasses state-of-the-art (SOTA) baselines on the M2E2 benchmark. On multimedia events in particular, we outperform the prior SOTA by 4.2% F1 on event mention identification and by 9.8% F1 on argument identification, which demonstrates that CAMEL learns synergistic representations from the two modalities. Our work demonstrates a recipe to unleash the power of synthetic training data in structured prediction.

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[1]

Shakhmatov Arseniy, Razzhigaev Anton, Nikolich Aleksandr, Arkhipkin Vladimir, Pavlov Igor, Kuznetsov Andrey, and Dimitrov Denis. 2023. Kandinsky 2.1. https://github.com/ai-forever/Kandinsky-2.

[2]

Brian Chen, Xudong Lin, Christopher Thomas, Manling Li, Shoya Yoshida, Lovish Chum, Heng Ji, and Shih-Fu Chang. 2021. Joint Multimedia Event Extraction from Video and Article. In Findings of the Association for Computational Linguistics: EMNLP 2021. 74--88.

[3]

Zhi-Qi Cheng, Qi Dai, Siyao Li, Teruko Mitamura, and Alexander Hauptmann. 2022. GSRFormer: Grounded Situation Recognition Transformer with Alternate Semantic Attention Refinement. In Proceedings of the 30th ACM International Conference on Multimedia. 3272--3281.

Digital Library

[4]

Junhyeong Cho, Youngseok Yoon, and Suha Kwak. 2022a. Collaborative transformers for grounded situation recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 19659--19668.

[5]

Junhyeong Cho, Youngseok Yoon, and Suha Kwak. 2022b. Collaborative Transformers for Grounded Situation Recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[6]

Florinel-Alin Croitoru, Vlad Hondru, Radu Tudor Ionescu, and Mubarak Shah. 2023. Diffusion models in vision: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence (2023).

Digital Library

[7]

George R Doddington, Alexis Mitchell, Mark A Przybocki, Lance A Ramshaw, Stephanie M Strassel, and Ralph M Weischedel. 2004. The automatic content extraction (ace) program-tasks, data, and evaluation. In Lrec, Vol. 2. Lisbon, 837--840.

[8]

Xinya Du, Alexander M Rush, and Claire Cardie. 2021. GRIT: Generative Role-filler Transformers for Document-level Event Entity Extraction. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume. 634--644.

[9]

Jing Gao, Peng Li, Zhikui Chen, and Jianing Zhang. 2020. A survey on deep learning for multimodal data fusion. Neural Computation, Vol. 32, 5 (2020), 829--864.

Digital Library

[10]

Jiahui Gao, Renjie Pi, LIN Yong, Hang Xu, Jiacheng Ye, Zhiyong Wu, Weizhong Zhang, Xiaodan Liang, Zhenguo Li, and Lingpeng Kong. 2023. Self-Guided Noise-Free Data Generation for Efficient Zero-Shot Learning. In International Conference on Learning Representations.

[11]

Daniel Gildea and Daniel Jurafsky. 2002. Automatic labeling of semantic roles. Computational linguistics, Vol. 28, 3 (2002), 245--288.

[12]

Tao Gui, Jiacheng Ye, Qi Zhang, Zhengyan Li, Zichu Fei, Yeyun Gong, and Xuan-Jing Huang. 2020. Uncertainty-Aware Label Refinement for Sequence Labeling. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2316--2326.

[13]

Luheng He, Mike Lewis, and Luke Zettlemoyer. 2015. Question-answer driven semantic role labeling: Using natural language to annotate natural language. In Proceedings of the 2015 conference on empirical methods in natural language processing. 643--653.

[14]

Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner. 2016. beta-vae: Learning basic visual concepts with a constrained variational framework. In International conference on learning representations.

[15]

Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. 2019. The curious case of neural text degeneration. arXiv preprint arXiv:1904.09751 (2019).

[16]

Or Honovich, Thomas Scialom, Omer Levy, and Timo Schick. 2022. Unnatural Instructions: Tuning Language Models with (Almost) No Human Labor. arXiv preprint arXiv:2212.09689 (2022).

[17]

I-Hung Hsu, Kuan-Hao Huang, Elizabeth Boschee, Scott Miller, Prem Natarajan, Kai-Wei Chang, and Nanyun Peng. 2022. DEGREE: A data-efficient generation-based event extraction model. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 1890--1908.

[18]

Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig. 2021. Scaling up visual and vision-language representation learning with noisy text supervision. In International Conference on Machine Learning. PMLR, 4904--4916.

[19]

Bingyi Kang, Saining Xie, Marcus Rohrbach, Zhicheng Yan, Albert Gordo, Jiashi Feng, and Yannis Kalantidis. 2019. Decoupling representation and classifier for long-tailed recognition. arXiv preprint arXiv:1910.09217 (2019).

[20]

Paul R Kingsbury and Martha Palmer. 2002. From TreeBank to PropBank. In LREC. 1989--1993.

[21]

Ankur Kumar. 2022. The Illustrated Image Captioning using transformers. ankur3107.github.io (2022). https://ankur3107.github.io/blogs/the-illustrated-image-captioning-using-transformers/

[22]

Bowen Li, Xiaojuan Qi, Thomas Lukasiewicz, and Philip Torr. 2019. Controllable text-to-image generation. Advances in Neural Information Processing Systems, Vol. 32 (2019).

[23]

Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. 2023 b. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. arXiv preprint arXiv:2301.12597 (2023).

[24]

Junnan Li, Dongxu Li, Caiming Xiong, and Steven Hoi. 2022b. Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation. In International Conference on Machine Learning. PMLR, 12888--12900.

[25]

Manling Li, Ruochen Xu, Shuohang Wang, Luowei Zhou, Xudong Lin, Chenguang Zhu, Michael Zeng, Heng Ji, and Shih-Fu Chang. 2022c. Clip-event: Connecting text and images with event structures. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 16420--16429.

[26]

Manling Li, Alireza Zareian, Qi Zeng, Spencer Whitehead, Di Lu, Heng Ji, and Shih-Fu Chang. 2020. Cross-media Structured Common Space for Multimedia Event Extraction. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 2557--2568.

[27]

Qian Li, Jianxin Li, Jiawei Sheng, Shiyao Cui, Jia Wu, Yiming Hei, Hao Peng, Shu Guo, Lihong Wang, Amin Beheshti, et al. 2022a. A Survey on Deep Learning Event Extraction: Approaches and Applications. IEEE Transactions on Neural Networks and Learning Systems (2022).

[28]

Yunxin Li, Baotian Hu, Xinyu Chen, Lin Ma, and Min Zhang. 2023 a. LMEye: An Interactive Perception Network for Large Language Models. arXiv preprint arXiv:2305.03701 (2023).

[29]

Jian Liu, Yufeng Chen, and Jinan Xu. 2022. Multimedia Event Extraction From News With a Unified Contrastive Learning Framework. In Proceedings of the 30th ACM International Conference on Multimedia. 1945--1953.

Digital Library

[30]

Xiao Liu, Zhunchen Luo, and He-Yan Huang. 2018. Jointly Multiple Events Extraction via Attention-based Graph Information Aggregation. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 1247--1256.

[31]

Quanyu Long, Mingxuan Wang, and Lei Li. 2021. Generative Imagination Elevates Machine Translation. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 5738--5748.

[32]

Ilya Loshchilov and Frank Hutter. 2017. Decoupled Weight Decay Regularization. In International Conference on Learning Representations.

[33]

Yujie Lu, Wanrong Zhu, Xin Wang, Miguel Eckstein, and William Yang Wang. 2022. Imagination-Augmented Natural Language Understanding. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 4392--4402.

[34]

Yubo Ma, Zehao Wang, Yixin Cao, Mukai Li, Meiqi Chen, Kun Wang, and Jing Shao. 2022. Prompt for Extraction? PAIE: Prompting Argument Interaction for Event Argument Extraction. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 6759--6774.

[35]

Arun Mallya and Svetlana Lazebnik. 2017. Recurrent models for situation recognition. In Proceedings of the IEEE International Conference on Computer Vision. 455--463.

[36]

Yu Meng, Jiaxin Huang, Yu Zhang, and Jiawei Han. 2022a. Generating training data with language models: Towards zero-shot language understanding. arXiv preprint arXiv:2202.04538 (2022).

[37]

Yu Meng, Martin Michalski, Jiaxin Huang, Yu Zhang, Tarek Abdelzaher, and Jiawei Han. 2022b. Tuning Language Models as Training Data Generators for Augmentation-Enhanced Few-Shot Learning. arXiv preprint arXiv:2211.03044 (2022).

[38]

Alexander Quinn Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob Mcgrew, Ilya Sutskever, and Mark Chen. 2022. GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models. In International Conference on Machine Learning. PMLR, 16784--16804.

[39]

Sarah Pratt, Mark Yatskar, Luca Weihs, Ali Farhadi, and Aniruddha Kembhavi. 2020. Grounded situation recognition. In Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part IV 16. Springer, 314--332.

Digital Library

[40]

Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. 2021. Learning transferable visual models from natural language supervision. In International conference on machine learning. PMLR, 8748--8763.

[41]

Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever. 2021. Zero-shot text-to-image generation. In International Conference on Machine Learning. PMLR, 8821--8831.

[42]

Alan Ramponi, Rob van der Goot, Rosario Lombardo, and Barbara Plank. 2020. Biomedical event extraction as sequence labeling. In Proceedings of the 2020 conference on empirical methods in natural language processing (emnlp). 5357--5367.

[43]

Scott Reed, Zeynep Akata, Xinchen Yan, Lajanugen Logeswaran, Bernt Schiele, and Honglak Lee. 2016. Generative adversarial text to image synthesis. In International conference on machine learning. PMLR, 1060--1069.

[44]

Ellen Riloff and Mark Schmelzenbach. 1998. An empirical approach to conceptual case frame acquisition. In Sixth Workshop on Very Large Corpora.

[45]

Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. 2022. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 10684--10695.

[46]

Arka Sadhu, Kan Chen, and Ram Nevatia. 2021a. Video Question Answering with Phrases via Semantic Roles. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2460--2478.

[47]

Arka Sadhu, Tanmay Gupta, Mark Yatskar, Ram Nevatia, and Aniruddha Kembhavi. 2021b. Visual semantic role labeling for video understanding. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 5589--5600.

[48]

Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, et al. 2022. Photorealistic text-to-image diffusion models with deep language understanding. Advances in Neural Information Processing Systems, Vol. 35 (2022), 36479--36494.

[49]

Meihan Tong, Shuai Wang, Yixin Cao, Bin Xu, Juanzi Li, Lei Hou, and Tat-Seng Chua. 2020. Image enhanced event detection in news articles. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 9040--9047.

[50]

Ultralytics. 2023. YOLOv8. https://github.com/ultralytics/ultralytics.

[51]

Arash Vahdat and Jan Kautz. 2020. NVAE: A deep hierarchical variational autoencoder. Advances in neural information processing systems, Vol. 33 (2020), 19667--19679.

[52]

Christopher Walker, Stephanie Strassel, Julie Medero, and Kazuaki Maeda. 2006. ACE 2005 multilingual training corpus. Linguistic Data Consortium, Philadelphia, Vol. 57 (2006), 45.

[53]

Jianfeng Wang, Zhengyuan Yang, Xiaowei Hu, Linjie Li, Kevin Lin, Zhe Gan, Zicheng Liu, Ce Liu, and Lijuan Wang. 2022b. Git: A generative image-to-text transformer for vision and language. arXiv preprint arXiv:2205.14100 (2022).

[54]

Peng Wang, An Yang, Rui Men, Junyang Lin, Shuai Bai, Zhikang Li, Jianxin Ma, Chang Zhou, Jingren Zhou, and Hongxia Yang. 2022c. Ofa: Unifying architectures, tasks, and modalities through a simple sequence-to-sequence learning framework. In International Conference on Machine Learning. PMLR, 23318--23340.

[55]

Sitong Wang, Samia Menon, Tao Long, Keren Henderson, Dingzeyu Li, Kevin Crowston, Mark Hansen, Jeffrey V Nickerson, and Lydia B Chilton. 2023. ReelFramer: Co-creating News Reels on Social Media with Generative AI. arXiv preprint arXiv:2304.09653 (2023).

[56]

Weiyao Wang, Du Tran, and Matt Feiszli. 2020. What Makes Training Multi-Modal Classification Networks Hard?. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[57]

Yizhong Wang, Yeganeh Kordi, Swaroop Mishra, Alisa Liu, Noah A Smith, Daniel Khashabi, and Hannaneh Hajishirzi. 2022a. Self-Instruct: Aligning Language Model with Self Generated Instructions. arXiv preprint arXiv:2212.10560 (2022).

[58]

Ziqi Wang, Xiaozhi Wang, Xu Han, Yankai Lin, Lei Hou, Zhiyuan Liu, Peng Li, Juanzi Li, and Jie Zhou. 2021. CLEVE: Contrastive Pre-training for Event Extraction. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 6283--6297.

[59]

Kaiwen Wei, Xian Sun, Zequn Zhang, Jingyuan Zhang, Guo Zhi, and Li Jin. 2021. Trigger is not sufficient: Exploiting frame-aware knowledge for implicit event argument extraction. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 4672--4682.

[60]

Meng Wei, Long Chen, Wei Ji, Xiaoyu Yue, and Tat-Seng Chua. 2022. Rethinking the two-stage framework for grounded situation recognition. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 36. 2651--2658.

[61]

Haoyang Wen, Yanru Qu, Heng Ji, Qiang Ning, Jiawei Han, Avirup Sil, Hanghang Tong, and Dan Roth. 2021. Event time extraction and propagation via graph attention networks. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 62--73.

[62]

Peter West, Chandra Bhagavatula, Jack Hessel, Jena Hwang, Liwei Jiang, Ronan Le Bras, Ximing Lu, Sean Welleck, and Yejin Choi. 2022. Symbolic Knowledge Distillation: from General Language Models to Commonsense Models. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 4602--4625.

[63]

Julia Wiedmann. 2017. Joint learning of structural and textual features for web scale event extraction. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 31.

[64]

Runxin Xu, Tianyu Liu, Lei Li, and Baobao Chang. 2021. Document-level Event Extraction via Heterogeneous Graph-based Interaction Model with a Tracker. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 3533--3546.

[65]

Nianwen Xue and Martha Palmer. 2003. Annotating the propositions in the Penn Chinese Treebank. In Proceedings of the second SIGHAN workshop on Chinese language processing. 47--54.

Digital Library

[66]

Yue Yang, Wenlin Yao, Hongming Zhang, Xiaoyang Wang, Dong Yu, and Jianshu Chen. 2022. Z-LaVI: Zero-Shot Language Solver Fueled by Visual Imagination. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Abu Dhabi, United Arab Emirates, 1186--1203. https://aclanthology.org/2022.emnlp-main.78

[67]

Mark Yatskar, Vicente Ordonez, Luke Zettlemoyer, and Ali Farhadi. 2017. Commonly uncommon: Semantic sparsity in situation recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 7196--7205.

[68]

Mark Yatskar, Luke Zettlemoyer, and Ali Farhadi. 2016. Situation recognition: Visual semantic role labeling for image understanding. In Proceedings of the IEEE conference on computer vision and pattern recognition. 5534--5542.

[69]

Jiacheng Ye, Jiahui Gao, Qintong Li, Hang Xu, Jiangtao Feng, Zhiyong Wu, Tao Yu, and Lingpeng Kong. 2022. Zerogen: Efficient zero-shot learning via dataset generation. arXiv preprint arXiv:2202.07922 (2022).

[70]

Jiahui Yu, Xin Li, Jing Yu Koh, Han Zhang, Ruoming Pang, James Qin, Alexander Ku, Yuanzhong Xu, Jason Baldridge, and Yonghui Wu. 2021. Vector-quantized image modeling with improved VQGAN. arXiv preprint arXiv:2110.04627 (2021).

[71]

Jiahui Yu, Yuanzhong Xu, Jing Yu Koh, Thang Luong, Gunjan Baid, Zirui Wang, Vijay Vasudevan, Alexander Ku, Yinfei Yang, Burcu Karagol Ayan, et al. 2022. Scaling autoregressive models for content-rich text-to-image generation. arXiv preprint arXiv:2206.10789 (2022).

[72]

Chenshuang Zhang, Chaoning Zhang, Mengchun Zhang, and In So Kweon. 2023. Text-to-image Diffusion Model in Generative AI: A Survey. arXiv preprint arXiv:2303.07909 (2023).

[73]

Tongtao Zhang and Heng Ji. 2018. Event extraction with generative adversarial imitation learning. arXiv preprint arXiv:1804.07881 (2018).

[74]

Tongtao Zhang, Spencer Whitehead, Hanwang Zhang, Hongzhi Li, Joseph Ellis, Lifu Huang, Wei Liu, Heng Ji, and Shih-Fu Chang. 2017. Improving event extraction via multimodal integration. In Proceedings of the 25th ACM international conference on Multimedia. 270--278.

Digital Library

[75]

Yang Zhou, Yubo Chen, Jun Zhao, Yin Wu, Jiexin Xu, and Jinlong Li. 2021. What the role is vs. what plays the role: Semi-supervised event argument extraction via dual question answering. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 14638--14646.

[76]

Wanrong Zhu, An Yan, Yujie Lu, Wenda Xu, Xin Eric Wang, Miguel Eckstein, and William Yang Wang. 2022. Visualize Before You Write: Imagination-Guided Open-Ended Text Generation. arXiv preprint arXiv:2210.03765 (2022).

Cited By

Liu YLiu FJiao LBao QSun LLi SLi LLiu X(2024)Multi-Grained Gradual Inference Model for Multimedia Event ExtractionIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2024.340224234:10(10507-10520)Online publication date: Oct-2024
https://doi.org/10.1109/TCSVT.2024.3402242

Index Terms

Training Multimedia Event Extraction With Generated Images and Captions
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Structured outputs
2. Information systems
  1. Information systems applications
    1. Multimedia information systems

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cover image ACM Conferences

MM '23: Proceedings of the 31st ACM International Conference on Multimedia

October 2023

9913 pages

ISBN:9798400701085

DOI:10.1145/3581783

General Chairs:
Abdulmotaleb El Saddik
University of Ottawa, Canada & MBZUAI, UAE
,
Tao Mei
HiDream.ai, China
,
Rita Cucchiara
University of Modena and Reggio Emilia, Italy
,
Program Chairs:
Marco Bertini
University of Florence, Italy
,
Diana Patricia Tobon Vallejo
Unversidad de Medellin, Colombia
,
Pradeep K. Atrey
University at Albany, State University of New York, USA
,
M. Shamim Hossain
M. Shamim Hossain (King Saud University, KSA

Copyright © 2023 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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SIGMM: ACM Special Interest Group on Multimedia

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Association for Computing Machinery

New York, NY, United States

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Published: 27 October 2023

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the Nanyang Associate Professorship and the National Research Foundation Fellowship

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MM '23

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MM '23: The 31st ACM International Conference on Multimedia

October 29 - November 3, 2023

Ottawa ON, Canada

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Cited By

Liu YLiu FJiao LBao QSun LLi SLi LLiu X(2024)Multi-Grained Gradual Inference Model for Multimedia Event ExtractionIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2024.340224234:10(10507-10520)Online publication date: Oct-2024
https://doi.org/10.1109/TCSVT.2024.3402242

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