Xiaoyang Wang

Xiaoyang Wang

Ph.D. and Senior Researcher.

Posts by Collection

portfolio

publications

A large-scale benchmark dataset for event recognition in surveillance video

Published in In the proceedings of 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011

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Recommended citation: Sangmin Oh, Anthony Hoogs, Amitha Perera, Naresh Cuntoor, Chia-Chih Chen, Jong Lee, Saurajit Mukherjee, JK Aggarwal, Hyungtae Lee, Larry Davis, All Others, "A large-scale benchmark dataset for event recognition in surveillance video." In the proceedings of 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011.

A Hierarchical Context Model for Event Recognition in Surveillance Video

Published in In the proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014

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Recommended citation: Xiaoyang Wang, Qiang Ji, "A Hierarchical Context Model for Event Recognition in Surveillance Video." In the proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014.

Video event recognition with deep hierarchical context model

Published in In the proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015

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Recommended citation: Xiaoyang Wang, Qiang Ji, "Video event recognition with deep hierarchical context model." In the proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.

Hierarchical context modeling for video event recognition

Published in IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2016

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Recommended citation: Xiaoyang Wang, Qiang Ji, "Hierarchical context modeling for video event recognition." IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2016.

Request-and-reverify: hierarchical hypothesis testing for concept drift detection with expensive labels

Published in In the proceedings of Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI), 2018

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Recommended citation: Shujian Yu, Xiaoyang Wang, Jos{\'e} Pr{\'\i}ncipe, "Request-and-reverify: hierarchical hypothesis testing for concept drift detection with expensive labels." In the proceedings of Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI), 2018.

Requet: Real-time qoe detection for encrypted youtube traffic

Published in In the proceedings of Proceedings of the 10th ACM Multimedia Systems Conference, 2019

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Recommended citation: Craig Gutterman, Katherine Guo, Sarthak Arora, Xiaoyang Wang, Les Wu, Ethan Katz-Bassett, Gil Zussman, "Requet: Real-time qoe detection for encrypted youtube traffic." In the proceedings of Proceedings of the 10th ACM Multimedia Systems Conference, 2019.

Requet: Real-time QoE metric detection for encrypted YouTube traffic

Published in ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 2020

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Recommended citation: Craig Gutterman, Katherine Guo, Sarthak Arora, Trey Gilliland, Xiaoyang Wang, Les Wu, Ethan Katz-Bassett, Gil Zussman, "Requet: Real-time QoE metric detection for encrypted YouTube traffic." ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 2020.

Towards Faithful Neural Table-to-Text Generation with Content-Matching Constraints

Published in In the proceedings of Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL), 2020

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Recommended citation: Zhenyi Wang, Xiaoyang Wang, Bang An, Dong Yu, Changyou Chen, "Towards Faithful Neural Table-to-Text Generation with Content-Matching Constraints." In the proceedings of Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL), 2020.

Meta-learning without data via wasserstein distributionally-robust model fusion

Published in In the proceedings of Uncertainty in Artificial Intelligence (UAI), 2022

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Recommended citation: Zhenyi Wang, Xiaoyang Wang, Li Shen, Qiuling Suo, Kaiqiang Song, Dong Yu, Yan Shen, Mingchen Gao, "Meta-learning without data via wasserstein distributionally-robust model fusion." In the proceedings of Uncertainty in Artificial Intelligence (UAI), 2022.

Salience Allocation as Guidance for Abstractive Summarization

Published in In the proceedings of Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2022

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Recommended citation: Fei Wang, Kaiqiang Song, Hongming Zhang, Lifeng Jin, Sangwoo Cho, Wenlin Yao, Xiaoyang Wang, Muhao Chen, Dong Yu, "Salience Allocation as Guidance for Abstractive Summarization." In the proceedings of Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2022.

Toward Unifying Text Segmentation and Long Document Summarization

Published in In the proceedings of Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2022

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Recommended citation: Sangwoo Cho, Kaiqiang Song, Xiaoyang Wang, Fei Liu, Dong Yu, "Toward Unifying Text Segmentation and Long Document Summarization." In the proceedings of Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2022.

Towards Abstractive Grounded Summarization of Podcast Transcripts

Published in In the proceedings of Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL), 2022

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Recommended citation: Kaiqiang Song, Chen Li, Xiaoyang Wang, Dong Yu, Fei Liu, "Towards Abstractive Grounded Summarization of Podcast Transcripts." In the proceedings of Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL), 2022.

Z-LaVI: Zero-Shot Language Solver Fueled by Visual Imagination

Published in In the proceedings of Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2022

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Recommended citation: Yue Yang, Wenlin Yao, Hongming Zhang, Xiaoyang Wang, Dong Yu, Jianshu Chen, "Z-LaVI: Zero-Shot Language Solver Fueled by Visual Imagination." In the proceedings of Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2022.

DecipherPref: Analyzing Influential Factors in Human Preference Judgments via GPT-4

Published in In the proceedings of Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP, 2023

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Recommended citation: Yebowen Hu, Kaiqiang Song, Sangwoo Cho, Xiaoyang Wang, Hassan Foroosh, Fei Liu, "DecipherPref: Analyzing Influential Factors in Human Preference Judgments via GPT-4." In the proceedings of Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP, 2023.

Generating user-engaging news headlines

Published in In the proceedings of Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (ACL), 2023

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Recommended citation: Pengshan Cai, Kaiqiang Song, Sangwoo Cho, Hongwei Wang, Xiaoyang Wang, Hong Yu, Fei Liu, Dong Yu, "Generating user-engaging news headlines." In the proceedings of Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (ACL), 2023.

More Than Spoken Words: Nonverbal Message Extraction and Generation

Published in In the proceedings of Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2023

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Recommended citation: Dian Yu, Xiaoyang Wang, Wanshun Chen, Nan Du, Longyue Wang, Haitao Mi, Dong Yu, "More Than Spoken Words: Nonverbal Message Extraction and Generation." In the proceedings of Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2023.

OASum: Large-Scale Open Domain Aspect-based Summarization

Published in In the proceedings of Findings of the Association for Computational Linguistics: ACL 2023, 2023

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Recommended citation: Xianjun Yang, Kaiqiang Song, Sangwoo Cho, Xiaoyang Wang, Xiaoman Pan, Linda Petzold, Dong Yu, "OASum: Large-Scale Open Domain Aspect-based Summarization." In the proceedings of Findings of the Association for Computational Linguistics: ACL 2023, 2023.

Can Large Language Models do Analytical Reasoning?

Published in arXiv preprint arXiv:2403.04031, 2024

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Recommended citation: Yebowen Hu, Kaiqiang Song, Sangwoo Cho, Xiaoyang Wang, Hassan Foroosh, Dong Yu, Fei Liu, "Can Large Language Models do Analytical Reasoning?." arXiv preprint arXiv:2403.04031, 2024.

From Language Modeling to Instruction Following: Understanding the Behavior Shift in LLMs after Instruction Tuning

Published in In the proceedings of Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2024

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Recommended citation: Xuansheng Wu, Wenlin Yao, Jianshu Chen, Xiaoman Pan, Xiaoyang Wang, Ninghao Liu, Dong Yu, "From Language Modeling to Instruction Following: Understanding the Behavior Shift in LLMs after Instruction Tuning." In the proceedings of Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2024.

InFoBench: Evaluating Instruction Following Ability in Large Language Models

Published in In the proceedings of Findings of the Association for Computational Linguistics: ACL 2024, 2024

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Recommended citation: Yiwei Qin, Kaiqiang Song, Yebowen Hu, Wenlin Yao, Sangwoo Cho, Xiaoyang Wang, Xuansheng Wu, Fei Liu, Pengfei Liu, Dong Yu, "InFoBench: Evaluating Instruction Following Ability in Large Language Models." In the proceedings of Findings of the Association for Computational Linguistics: ACL 2024, 2024.

MMC: Advancing Multimodal Chart Understanding with Large-scale Instruction Tuning

Published in In the proceedings of Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2024

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Recommended citation: Fuxiao Liu, Xiaoyang Wang, Wenlin Yao, Jianshu Chen, Kaiqiang Song, Sangwoo Cho, Yaser Yacoob, Dong Yu, "MMC: Advancing Multimodal Chart Understanding with Large-scale Instruction Tuning." In the proceedings of Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2024.

Polarity Calibration for Opinion Summarization

Published in In the proceedings of Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2024

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Recommended citation: Yuanyuan Lei, Kaiqiang Song, Sangwoo Cho, Xiaoyang Wang, Ruihong Huang, Dong Yu, "Polarity Calibration for Opinion Summarization." In the proceedings of Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2024.

Skills-in-Context: Unlocking Compositionality in Large Language Models

Published in In the proceedings of Findings of the Association for Computational Linguistics: EMNLP 2024, 2024

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Recommended citation: Jiaao Chen, Xiaoman Pan, Dian Yu, Kaiqiang Song, Xiaoyang Wang, Dong Yu, Jianshu Chen, "Skills-in-Context: Unlocking Compositionality in Large Language Models." In the proceedings of Findings of the Association for Computational Linguistics: EMNLP 2024, 2024.

SportsMetrics: Blending Text and Numerical Data to Understand Information Fusion in LLMs

Published in In the proceedings of Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (ACL), 2024

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Recommended citation: Yebowen Hu, Kaiqiang Song, Sangwoo Cho, Xiaoyang Wang, Hassan Foroosh, Dong Yu, Fei Liu, "SportsMetrics: Blending Text and Numerical Data to Understand Information Fusion in LLMs." In the proceedings of Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (ACL), 2024.

When Reasoning Meets Information Aggregation: A Case Study with Sports Narratives

Published in In the proceedings of Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2024

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Recommended citation: Yebowen Hu, Kaiqiang Song, Sangwoo Cho, Xiaoyang Wang, Wenlin Yao, Hassan Foroosh, Dong Yu, Fei Liu, "When Reasoning Meets Information Aggregation: A Case Study with Sports Narratives." In the proceedings of Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2024.

talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

This is a description of a teaching experience. You can use markdown like any other post.