Publications | Chris Thomas at Virginia Tech

2025

Maximal Matching Matters: Preventing Representation Collapse for Robust Cross-Modal Retrieval

Hani Alomari, Anushka Sivakumar, Andrew Zhang, and 1 more author

In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (ACL) (To appear), 2025

@inproceedings{alomari2025maximal,
  title = {Maximal Matching Matters: Preventing Representation Collapse for Robust Cross-Modal Retrieval},
  author = {Alomari, Hani and Sivakumar, Anushka and Zhang, Andrew and Thomas, Chris},
  booktitle = {Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (ACL) (To appear)},
  year = {2025},
  address = {Toronto, Canada},
  publisher = {Association for Computational Linguistics},
}

NeurIPSW

ENTER: Event Based Interpretable Reasoning for VideoQA

Hammad Ayyubi, Junzhang Liu, Ali Asgarov, and 10 more authors

In Proceedings of the Advances in Neural Information Processing Systems, Multimodal Algorithmic Reasoning Workshop, 2025

Spotlight

Bib

@inproceedings{ayyubi2025enter,
  title = {ENTER: Event Based Interpretable Reasoning for VideoQA},
  author = {Ayyubi, Hammad and Liu, Junzhang and Asgarov, Ali and Hakim, Zaber Ibn Abdul and Sarker, Najibul Haque and Wang, Zhecan and Tang, Chia-Wei and Alomari, Hani and Atabuzzaman, Md and Lin, Xudong and Dyava, Naveen Reddy and Chang, Shih-Fu and Thomas, Chris},
  booktitle = {Proceedings of the Advances in Neural Information Processing Systems, Multimodal Algorithmic Reasoning Workshop},
  year = {2025},
  note = {Spotlight},
}

WACV

Advancing chart question answering with robust chart component recognition

Hanwen Zheng, Sijia Wang, Chris Thomas, and 1 more author

In 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2025

Bib

@inproceedings{zheng2025advancing,
  title = {Advancing chart question answering with robust chart component recognition},
  author = {Zheng, Hanwen and Wang, Sijia and Thomas, Chris and Huang, Lifu},
  booktitle = {2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
  pages = {5741--5750},
  year = {2025},
  organization = {IEEE},
}

2024

M3D: MultiModal MultiDocument Fine-Grained Inconsistency Detection

Chia-Wei Tang, Ting-Chih Chen, Kiet Nguyen, and 3 more authors

In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

Bib Website

@inproceedings{tang2024m3d,
  title = {M3D: MultiModal MultiDocument Fine-Grained Inconsistency Detection},
  author = {Tang, Chia-Wei and Chen, Ting-Chih and Nguyen, Kiet and Mehrab, Kazi Sajeed and Ishmam, Alvi and Thomas, Chris},
  booktitle = {Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing},
  pages = {22270--22293},
  year = {2024},
}

ACM

Detecting Multimodal Situations with Insufficient Context and Abstaining from Baseless Predictions

Junzhang Liu, Zhecan Wang, Hammad Ayyubi, and 5 more authors

In Proceedings of the 32nd ACM International Conference on Multimedia, 2024

arXiv Bib Website

@inproceedings{liu2024detecting,
  title = {Detecting Multimodal Situations with Insufficient Context and Abstaining from Baseless Predictions},
  author = {Liu, Junzhang and Wang, Zhecan and Ayyubi, Hammad and You, Haoxuan and Thomas, Chris and Sun, Rui and Chang, Shih-Fu and Chang, Kai-Wei},
  booktitle = {Proceedings of the 32nd ACM International Conference on Multimedia},
  pages = {8402--8411},
  year = {2024},
}

Journeybench: A challenging one-stop vision-language understanding benchmark of generated images

Zhecan Wang, Junzhang Liu, Chia-Wei Tang, and 8 more authors

Advances in Neural Information Processing Systems, 2024

Bib Poster Website

@article{wang2024journeybench,
  title = {Journeybench: A challenging one-stop vision-language understanding benchmark of generated images},
  author = {Wang, Zhecan and Liu, Junzhang and Tang, Chia-Wei and Alomari, Hani and Sivakumar, Anushka and Sun, Rui and Li, Wenhao and Atabuzzaman, Md and Ayyubi, Hammad and You, Haoxuan and others},
  journal = {Advances in Neural Information Processing Systems},
  volume = {37},
  pages = {63110--63123},
  year = {2024},
}

MetaSumPerceiver: Multimodal Multi-Document Evidence Summarization for Fact-Checking

Ting-Chih Chen, Chia-Wei Tang, and Christopher Thomas

In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2024

Abs Bib Website

Fact-checking real-world claims often requires reviewing multiple multimodal documents in order to assess the claim’s truthfulness, a highly laborious and time-consuming task. In this paper, we present a summarization model crafted to generate claim-specific summaries useful for fact-checking from multimodal multi-document datasets. The model takes inputs in the form of documents, images, and a claim, with the objective of assisting in fact-checking tasks. We introduce a dynamic perceiver-based model that is able to handle inputs from multiple modalities of arbitrary lengths. To train our model, we leverage a novel reinforcement learning-based entailment objective in order to generate summaries that provide evidence distinguishing between different truthfulness labels. To assess the efficacy of our approach, we conduct experiments on both an existing benchmark as well as a new dataset of multi-document claims which we contribute. Our approach outperforms the SOTA approach by 4.6% in the claim verification task on the MOCHEG dataset and demonstrates strong performance on our new Multi-News-Fact-Checking dataset.
@inproceedings{chen-etal-2024-metasumperceiver, title = {{M}eta{S}um{P}erceiver: Multimodal Multi-Document Evidence Summarization for Fact-Checking}, author = {Chen, Ting-Chih and Tang, Chia-Wei and Thomas, Christopher}, editor = {Ku, Lun-Wei and Martins, Andre and Srikumar, Vivek}, booktitle = {Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)}, year = {2024}, address = {Bangkok, Thailand}, publisher = {Association for Computational Linguistics}, url = {https://aclanthology.org/2024.acl-long.474}, pages = {8742--8757}, }

Semantic Shield: Defending Vision-Language Models Against Backdooring and Poisoning via Fine-grained Knowledge Alignment

Alvi Md Ishmam, and Christopher Thomas

In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

Bib Website

@inproceedings{ishmam2024semantic,
  title = {Semantic Shield: Defending Vision-Language Models Against Backdooring and Poisoning via Fine-grained Knowledge Alignment},
  author = {Ishmam, Alvi Md and Thomas, Christopher},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages = {24820--24830},
  year = {2024},
}

Beyond Grounding: Extracting Fine-Grained Event Hierarchies across Modalities

Hammad Ayyubi, Christopher Thomas, Lovish Chum, and 8 more authors

In Proceedings of the AAAI Conference on Artificial Intelligence, 2024

Bib Website

@inproceedings{ayyubi2024beyond,
  title = {Beyond Grounding: Extracting Fine-Grained Event Hierarchies across Modalities},
  author = {Ayyubi, Hammad and Thomas, Christopher and Chum, Lovish and Lokesh, Rahul and Chen, Long and Niu, Yulei and Lin, Xudong and Feng, Xuande and Koo, Jaywon and Ray, Sounak and others},
  booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence},
  volume = {38},
  number = {16},
  pages = {17664--17672},
  year = {2024},
}

2023

ACL
Enhanced Chart Understanding via Visual Language Pre-training on Plot Table Pairs

Mingyang Zhou, Yi Fung, Long Chen, and 3 more authors

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

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Building cross-model intelligence that can understand charts and communicate the salient information hidden behind them is an appealing challenge in the vision and language (V+L) community. The capability to uncover the underlined table data of chart figures is a critical key to automatic chart understanding. We introduce ChartT5, a V+L model that learns how to interpret table information from chart images via cross-modal pre-training on plot table pairs. Specifically, we propose two novel pre-training objectives: Masked Header Prediction (MHP) and Masked Value Prediction (MVP) to facilitate the model with different skills to interpret the table information. We have conducted extensive experiments on chart question answering and chart summarization to verify the effectiveness of the proposed pre-training strategies. In particular, on the ChartQA benchmark, our ChartT5 outperforms the state-of-the-art non-pretraining methods by over 8% performance gains.
@inproceedings{zhou-etal-2023-enhanced, title = {Enhanced Chart Understanding via Visual Language Pre-training on Plot Table Pairs}, author = {Zhou, Mingyang and Fung, Yi and Chen, Long and Thomas, Christopher and Ji, Heng and Chang, Shih-Fu}, editor = {Rogers, Anna and Boyd-Graber, Jordan and Okazaki, Naoaki}, booktitle = {Findings of the Association for Computational Linguistics: ACL 2023}, year = {2023}, address = {Toronto, Canada}, publisher = {Association for Computational Linguistics}, url = {https://aclanthology.org/2023.findings-acl.85}, doi = {10.18653/v1/2023.findings-acl.85}, pages = {1314--1326}, }

2022

Fine-Grained Visual Entailment

Christopher Thomas, Yipeng Zhang, and Shih-Fu Chang

In Proceedings of the European Conference on Computer Vision, 2022

arXiv Bib HTML

@inproceedings{thomas2022fine,
  title = {Fine-Grained Visual Entailment},
  author = {Thomas, Christopher and Zhang, Yipeng and Chang, Shih-Fu},
  booktitle = {Proceedings of the European Conference on Computer Vision},
  pages = {398--416},
  year = {2022},
}

Community implications for gun violence prevention during co-occurring pandemics; a qualitative and computational analysis study

Desmond U. Patton, Nathan Aguilar, Aviv Y. Landau, and 9 more authors

Preventive Medicine, 2022

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This study provides insight into New York City residents’ perceptions about violence after the outbreak of Coronavirus disease (COVID-19) based on information from communities in New York City Housing Authority (NYCHA) buildings. In this novel analysis, we used focus group and social media data to confirm or reject findings from qualitative interviews. We first used data from 69 in-depth, semi-structured interviews with low-income residents and community stakeholders to further explore how violence impacts New York City’s low-income residents of color, as well as the role of city government in providing tangible support for violence prevention during co-occurring health (COVID-19) and social (anti-Black racism) pandemics. Residents described how COVID-19 and the Black Lives Matter movement impacted safety in their communities while offering direct recommendations to improve safety. Residents also shared recommendations that indirectly improve community safety by addressing long term systemic issues. As the recruitment of interviewees was concluding, researchers facilitated two focus groups with 38 interviewees to discuss similar topics. In order to assess the degree to which the themes discovered in our qualitative interviews were shared by the broader community, we developed an integrative community data science study which leveraged natural language processing and computer vision techniques to study text and images on public social media data of 12 million tweets generated by residents. We joined computational methods with qualitative analysis through a social work lens and design justice principles to most accurately and holistically analyze the community perceptions of gun violence issues and potential prevention strategies. Findings indicate valuable community-based insights that elucidate how the co-occurring pandemics impact residents’ experiences of gun violence and provide important implications for gun violence prevention in a digital era.
@article{PATTON2022107263, title = {Community implications for gun violence prevention during co-occurring pandemics; a qualitative and computational analysis study}, journal = {Preventive Medicine}, pages = {107263}, year = {2022}, issn = {0091-7435}, doi = {https://doi.org/10.1016/j.ypmed.2022.107263}, url = {https://www.sciencedirect.com/science/article/pii/S0091743522003127}, author = {Patton, Desmond U. and Aguilar, Nathan and Landau, Aviv Y. and Thomas, Chris and Kagan, Rachel and Ren, Tianai and Stoneberg, Eric and Wang, Timothy and Halmos, Daniel and Saha, Anish and Ananthram, Amith and McKeown, Kathleen}, keywords = {Gun violence, COVID-19, Black lives matter, Defund the police, Social media, Qualitative and computational analysis}, }

Emphasizing Complementary Samples for Non-Literal Cross-Modal Retrieval

Christopher Thomas, and Adriana Kovashka

In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2022

Bib HTML PDF Supp

@inproceedings{thomas2022emphasizing,
  title = {Emphasizing Complementary Samples for Non-Literal Cross-Modal Retrieval},
  author = {Thomas, Christopher and Kovashka, Adriana},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  pages = {4632--4641},
  year = {2022},
}

arXiv

Multimodal Event Graphs: Towards Event Centric Understanding of Multimodal World

Hammad A Ayyubi, Christopher Thomas, Lovish Chum, and 7 more authors

arXiv preprint arXiv:2206.07207, 2022

arXiv Bib

@article{ayyubi2022multimodal,
  title = {Multimodal Event Graphs: Towards Event Centric Understanding of Multimodal World},
  author = {Ayyubi, Hammad A and Thomas, Christopher and Chum, Lovish and Lokesh, Rahul and Niu, Yulei and Lin, Xudong and Chen, Long and Koo, Jaywon and Ray, Sounak and Chang, Shih-Fu},
  journal = {arXiv preprint arXiv:2206.07207},
  year = {2022}
}

TPAMI

Learning to Overcome Noise in Weak Caption Supervision for Object Detection

Mesut Erhan Unal, Keren Ye, Mingda Zhang, and 5 more authors

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022

Bib PDF

@article{unal2022learning,
  title = {Learning to Overcome Noise in Weak Caption Supervision for Object Detection},
  author = {Unal, Mesut Erhan and Ye, Keren and Zhang, Mingda and Thomas, Christopher and Kovashka, Adriana and Li, Wei and Qin, Danfeng and Berent, Jesse},
  journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year = {2022},
  publisher = {IEEE}
}

EMNLP

Weakly-Supervised Temporal Article Grounding

Long Chen, Yulei Niu, Brian Chen, and 6 more authors

In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP2022), 2022

arXiv Bib

@inproceedings{chen2022weakly,
  title = {Weakly-Supervised Temporal Article Grounding},
  author = {Chen, Long and Niu, Yulei and Chen, Brian and Lin, Xudong and Han, Guangxing and Thomas, Christopher and Ayyubi, Hammad and Ji, Heng and Chang, Shih-Fu},
  booktitle = {Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP2022)},
  year = {2022}
}

2021

InfoSurgeon: Cross-Media Fine-grained Information Consistency Checking for Fake News Detection

Yi Fung, Christopher Thomas, Revanth Reddy, and 6 more authors

In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics (ACL), 2021

Bib HTML

@inproceedings{fung2021infosurgeon,
  title = {InfoSurgeon: Cross-Media Fine-grained Information Consistency Checking for Fake News Detection},
  author = {Fung, Yi and Thomas, Christopher and Reddy, Revanth and Polisetty, Sandeep and Ji, Heng and Chang, Shih-Fu and McKeown, Kathleen and Bansal, Mohit and Sil, Avi},
  booktitle = {Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics (ACL)},
  year = {2021},
}

IJCV

Predicting Visual Political Bias Using Webly Supervised Data and an Auxiliary Task

Christopher Thomas, and Adriana Kovashka

International Journal of Computer Vision, 2021

Bib HTML

@article{thomas2021predicting,
  title = {Predicting Visual Political Bias Using Webly Supervised Data and an Auxiliary Task},
  author = {Thomas, Christopher and Kovashka, Adriana},
  journal = {International Journal of Computer Vision},
  volume = {129},
  number = {11},
  pages = {2978--3003},
  year = {2021},
  publisher = {Springer US}
}

EMNLP

Joint Multimedia Event Extraction from Video and Article

Brian Chen, Xudong Lin, Christopher Thomas, and 5 more authors

In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP2021) Findings, 2021

Bib HTML

@inproceedings{chen2021joint,
  title = {Joint Multimedia Event Extraction from Video and Article},
  author = {Chen, Brian and Lin, Xudong and Thomas, Christopher and Li, Manling and Yoshida, Shoya and Chum, Lovish and Ji, Heng and Chang, Shih-Fu},
  booktitle = {Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP2021) Findings},
  year = {2021}
}

2020

Preserving Semantic Neighborhoods for Robust Cross-modal Retrieval

Christopher Thomas, and Adriana Kovashka

In Proceedings of the European Conference on Computer Vision (ECCV), 2020

Bib HTML PDF Website

@inproceedings{thomas2020preserving,
  title = {Preserving Semantic Neighborhoods for Robust Cross-modal Retrieval},
  author = {Thomas, Christopher and Kovashka, Adriana},
  booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
  year = {2020},
}

Modeling Visual Rhetoric and Semantics in Multimedia

Christopher Thomas

University of Pittsburgh, 2020

Bib HTML

arXiv

Learning to Transfer Visual Effects from Videos to Images

Christopher Thomas, Yale Song, and Adriana Kovashka

arXiv preprint arXiv:2012.01642, 2020

arXiv Bib

@article{thomas2020learning,
  title = {Learning to Transfer Visual Effects from Videos to Images},
  author = {Thomas, Christopher and Song, Yale and Kovashka, Adriana},
  journal = {arXiv preprint arXiv:2012.01642},
  year = {2020}
}

2019

Predicting the politics of an image using webly supervised data

Christopher Thomas, and Adriana Kovashka

In Advances in Neural Information Processing Systems (NeurIPS 2019), 2019

Bib PDF Supp Poster Slides Website

@inproceedings{thomas2019predicting,
  title = {Predicting the politics of an image using webly supervised data},
  author = {Thomas, Christopher and Kovashka, Adriana},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS 2019)},
  year = {2019},
}

2018

Persuasive faces: generating faces in advertisements

Christopher Thomas, and Adriana Kovashka

In Proceedings of the British Machine Vision Conference, 2018

Bib PDF Supp

@inproceedings{thomas2018persuasive,
  title = {Persuasive faces: generating faces in advertisements},
  author = {Thomas, Christopher and Kovashka, Adriana},
  booktitle = {Proceedings of the British Machine Vision Conference},
  year = {2018},
}

ACCV

Artistic object recognition by unsupervised style adaptation

Christopher Thomas, and Adriana Kovashka

In Asian Conference on Computer Vision, 2018

Bib PDF Supp Poster Website

@inproceedings{thomas2018artistic,
  title = {Artistic object recognition by unsupervised style adaptation},
  author = {Thomas, Christopher and Kovashka, Adriana},
  booktitle = {Asian Conference on Computer Vision},
  pages = {460--476},
  year = {2018},
  organization = {Springer, Cham}
}

2017

Automatic understanding of image and video advertisements

Zaeem Hussain, Mingda Zhang, Xiaozhong Zhang, and 5 more authors

In Proceedings of the IEEE conference on computer vision and pattern recognition, 2017

Bib PDF Supp Poster Website

@inproceedings{hussain2017automatic,
  title = {Automatic understanding of image and video advertisements},
  author = {Hussain, Zaeem and Zhang, Mingda and Zhang, Xiaozhong and Ye, Keren and Thomas, Christopher and Agha, Zuha and Ong, Nathan and Kovashka, Adriana},
  booktitle = {Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages = {1705--1715},
  year = {2017},
}

2016

Seeing Behind the Camera: Identifying the Authorship of a Photograph

Christopher Thomas, and Adriana Kovashka

In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016

Bib PDF Supp Poster Website

@inproceedings{thomas2016seeing,
  title = {Seeing Behind the Camera: Identifying the Authorship of a Photograph},
  author = {Thomas, Christopher and Kovashka, Adriana},
  booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  year = {2016},
}

arXiv

Opensalicon: An open source implementation of the salicon saliency model

Christopher Thomas

arXiv preprint arXiv:1606.00110, 2016

arXiv Bib Website

@article{thomas2016opensalicon,
  title = {Opensalicon: An open source implementation of the salicon saliency model},
  author = {Thomas, Christopher},
  journal = {arXiv preprint arXiv:1606.00110},
  year = {2016}
}

CVPRW

A Visual Attention Algorithm Designed for Coupled Oscillator Acceleration

Christopher Thomas, Adriana Kovashka, Donald Chiarulli, and 1 more author

In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2016

Bib HTML PDF

@inproceedings{thomas2016visual,
  title = {A Visual Attention Algorithm Designed for Coupled Oscillator Acceleration},
  author = {Thomas, Christopher and Kovashka, Adriana and Chiarulli, Donald and Levitan, Steven},
  booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops},
  pages = {10--18},
  year = {2016}
}

2015

arXiv

Hand Posture’s Effect on Touch Screen Text Input Behaviors: A Touch Area Based Study

Christopher Thomas, and Brandon Jennings

arXiv preprint arXiv:1504.02134, 2015

arXiv Bib

@article{thomas2015hand,
  title = {Hand Posture's Effect on Touch Screen Text Input Behaviors: A Touch Area Based Study},
  author = {Thomas, Christopher and Jennings, Brandon},
  journal = {arXiv preprint arXiv:1504.02134},
  year = {2015}
}

SEKE

Application of Slow Intelligence Framework for Smart Pet Care System Design

Shi-Kuo Chang, Wen-Hui Chen, Wen-Chyi Lin, and 1 more author

In Software Engineering and Knowledge Engineering (SEKE 2015), 2015

Bib HTML

@inproceedings{chang2015application,
  title = {Application of Slow Intelligence Framework for Smart Pet Care System Design},
  author = {Chang, Shi-Kuo and Chen, Wen-Hui and Lin, Wen-Chyi and Thomas, Christopher Lee},
  booktitle = {Software Engineering and Knowledge Engineering (SEKE 2015)},
  year = {2015}
}

2014

INLG

TBI-Doc: Generating patient & clinician reports from brain imaging data

Pamela Jordan, Nancy Green, Chistopher Thomas, and 1 more author

In Proceedings of the 8th International Natural Language Generation Conference (INLG), 2014

Bib HTML PDF

@inproceedings{jordan2014tbi,
  title = {TBI-Doc: Generating patient \& clinician reports from brain imaging data},
  author = {Jordan, Pamela and Green, Nancy and Thomas, Chistopher and Holm, Susan},
  booktitle = {Proceedings of the 8th International Natural Language Generation Conference (INLG)},
  pages = {143--146},
  year = {2014}
}

Student Response Analysis

Sean Myers, Timothy Parenti, and Chris Thomas

2014

Bib PDF

@misc{myers2014student,
  title = {Student Response Analysis},
  author = {Myers, Sean and Parenti, Timothy and Thomas, Chris},
  journal = {Technical Report - University of Pittsburgh Department of Computer Science},
  year = {2014}
}