Chris Thomas

Selected Publications

1. 

   LAMP: Learning Universal Adversarial Perturbations for Multi-Image Tasks via Pre-trained Models

   Alvi Md Ishmam, Najibul Haque Sarker, Zaber Ibn Abdul Hakim, and 1 more author

   In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI-26), 2026

   Bib

   @inproceedings{ishmam2026lamp,
     title = {LAMP: Learning Universal Adversarial Perturbations for Multi-Image Tasks via Pre-trained Models},
     author = {Ishmam, Alvi Md and Sarker, Najibul Haque and Hakim, Zaber Ibn Abdul and Thomas, Chris},
     booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence (AAAI-26)},
     year = {2026},
     publisher = {AAAI Press},
   }

2. 

   Flexible-length Text Infilling for Discrete Diffusion Models

   Andrew Zhang, Anushka Sivakumar, Chia-Wei Tang, and 1 more author

   In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, 2025

   Bib Website

   @inproceedings{zhang2025flexible,
     title = {Flexible-length Text Infilling for Discrete Diffusion Models},
     author = {Zhang, Andrew and Sivakumar, Anushka and Tang, Chia-Wei and Thomas, Chris},
     booktitle = {Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing},
     year = {2025},
     address = {Suzhou, China},
     publisher = {Association for Computational Linguistics},
   }

3. 

   Benchmarking and Mitigating MCQA Selection Bias of Large Vision-Language Models

   Md. Atabuzzaman, Ali Asgarov, and Chris Thomas

   In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, 2025

   Bib Website

   @inproceedings{atabuzzaman2025mcqa,
     title = {Benchmarking and Mitigating MCQA Selection Bias of Large Vision-Language Models},
     author = {Atabuzzaman, Md. and Asgarov, Ali and Thomas, Chris},
     booktitle = {Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing},
     year = {2025},
     address = {Suzhou, China},
     publisher = {Association for Computational Linguistics},
   }

4. 

   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), 2025

   Bib Website

   @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)},
     year = {2025},
     address = {Toronto, Canada},
     publisher = {Association for Computational Linguistics},
   }

5. 

   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},
   }

6. 

   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},
   }

7. 

   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},
   }

8. 

   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},
   }

9. 

   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},
   }

10. 

    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},
    }

11. 

    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},
    }

12. 

    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},
    }

13. 

    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},
    }