% Use this file for citations not found in the ACL Anthology (contained in "anthology.bib").


@article{AlSaad2024Multimodal,title={Multimodal Large Language Models in Health Care: Applications, Challenges, and Future Outlook},author={Rawan AlSaad and Alaa A. Abd-alrazaq and Sabri Boughorbel and Arfan Ahmed and Max-Antoine Renault and R. Damseh and Javaid Sheikh},journal={Journal of Medical Internet Research},year={2024},volume={26},doi={10.2196/59505}}

@article{Li2024LLaVA-Surg:,title={LLaVA-Surg: Towards Multimodal Surgical Assistant via Structured Surgical Video Learning},author={Jiajie Li and Garrett C. Skinner and Gene Yang and Brian R Quaranto and Steven D. Schwaitzberg and Peter C W Kim and Jinjun Xiong},journal={ArXiv},year={2024},volume={abs/2408.07981},doi={10.48550/arxiv.2408.07981}}

@article{Xiao2023Can,title={Can I Trust Your Answer? Visually Grounded Video Question Answering},author={Junbin Xiao and Angela Yao and Yicong Li and Tat-Seng Chua},journal={2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},year={2023},pages={13204-13214},doi={10.1109/cvpr52733.2024.01254}}

@article{Zhang2023Video-LLaMA:,title={Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding},author={Hang Zhang and Xin Li and Lidong Bing},year={2023},pages={543-553},doi={10.48550/arxiv.2306.02858}}

@article{Wang2025SurgVidLM:,title={SurgVidLM: Towards Multi-grained Surgical Video Understanding with Large Language Model},author={Guan-Feng Wang and Wenjin Mo and Junyi Wang and Long Bai and Kun Yuan and Ming Hu and Jinlin Wu and Junjun He and Yiming Huang and N. Padoy and Zhen Lei and Hongbin Liu and Nassir Navab and Hongliang Ren},journal={ArXiv},year={2025},volume={abs/2506.17873},doi={10.48550/arxiv.2506.17873}}

@INPROCEEDINGS{11092739,
  author={Li, Chaoyu and Im, Eun Woo and Fazli, Pooyan},
  booktitle={2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, 
  title={VidHalluc: Evaluating Temporal Hallucinations in Multimodal Large Language Models for Video Understanding}, 
  year={2025},
  volume={},
  number={},
  pages={13723-13733},
  keywords={Visualization;Computer vision;Codes;Large language models;Buildings;Benchmark testing;Cognition;Pattern recognition;Videos;hallucination;multimodal large language model;video understanding;benchmark},
  doi={10.1109/CVPR52734.2025.01281}}

@article{Timoh2023A,title={A systematic review of annotation for surgical process model analysis in minimally invasive surgery based on video},author={K. Nyangoh Timoh and Arnaud Huaulmé and K. Cleary and Myra A. Zaheer and V. Lavoué and D. Donoho and P. Jannin},journal={Surgical Endoscopy},year={2023},volume={37},pages={4298 - 4314},doi={10.1007/s00464-023-10041-w}}

@article{gupta2023dataset,
  title={A Dataset for Medical Instructional Video Classification and Question Answering},
  author={Gupta, Deepak and Attal, Kush and Demner-Fushman, Dina},
  journal={Scientific Data},
  volume={10},
  number={1},
  pages={158},
  year={2023},
  publisher={Nature Publishing Group UK London}
}

@inproceedings{gupta-etal-2024-towards,
    title = "Towards Answering Health-related Questions from Medical Videos: Datasets and Approaches",
    author = "Gupta, Deepak  and
      Attal, Kush  and
      Demner-Fushman, Dina",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.1425/",
    pages = "16399--16411",
    abstract = "The increase in the availability of online videos has transformed the way we access information and knowledge. A growing number of individuals now prefer instructional videos as they offer a series of step-by-step procedures to accomplish particular tasks. Instructional videos from the medical domain may provide the best possible visual answers to first aid, medical emergency, and medical education questions. This paper focuses on answering health-related questions asked by health consumers by providing visual answers from medical videos. The scarcity of large-scale datasets in the medical domain is a key challenge that hinders the development of applications that can help the public with their health-related questions. To address this issue, we first proposed a pipelined approach to create two large-scale datasets: HealthVidQA-CRF and HealthVidQA-Prompt. Leveraging the datasets, we developed monomodal and multimodal approaches that can effectively provide visual answers from medical videos to natural language questions. We conducted a comprehensive analysis of the results and outlined the findings, focusing on the impact of the created datasets on model training and the significance of visual features in enhancing the performance of the monomodal and multi-modal approaches for medical visual answer localization task."
}

@ARTICLE{10552074,
  author={Li, Shutao and Li, Bin and Sun, Bin and Weng, Yixuan},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
  title={Towards Visual-Prompt Temporal Answer Grounding in Instructional Video}, 
  year={2024},
  volume={46},
  number={12},
  pages={8836-8853},
  keywords={Visualization;Task analysis;Thyroid;Feature extraction;Semantics;Grounding;Location awareness;Instructional video;pre-trained language model;temporal answer grounding;visual prompt},
  doi={10.1109/TPAMI.2024.3411045}}

@ARTICLE{10075491,
  author={Zhang, Hao and Sun, Aixin and Jing, Wei and Zhou, Joey Tianyi},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
  title={Temporal Sentence Grounding in Videos: A Survey and Future Directions}, 
  year={2023},
  volume={45},
  number={8},
  pages={10443-10465},
  keywords={Videos;Feature extraction;Proposals;Grounding;Task analysis;Taxonomy;Location awareness;Cross-modal video retrieval;multimodal learning;multimodal retrieval;natural language video localization;temporal sentence grounding in video;temporal video grounding;video moment retrieval;video understanding;vision and language},
  doi={10.1109/TPAMI.2023.3258628}}

@inproceedings{cao-etal-2021-pursuit,
    title = "On Pursuit of Designing Multi-modal Transformer for Video Grounding",
    author = "Cao, Meng  and
      Chen, Long  and
      Shou, Mike Zheng  and
      Zhang, Can  and
      Zou, Yuexian",
    editor = "Moens, Marie-Francine  and
      Huang, Xuanjing  and
      Specia, Lucia  and
      Yih, Scott Wen-tau",
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.emnlp-main.773/",
    doi = "10.18653/v1/2021.emnlp-main.773",
    pages = "9810--9823",
    abstract = "Video grounding aims to localize the temporal segment corresponding to a sentence query from an untrimmed video. Almost all existing video grounding methods fall into two frameworks: 1) Top-down model: It predefines a set of segment candidates and then conducts segment classification and regression. 2) Bottom-up model: It directly predicts frame-wise probabilities of the referential segment boundaries. However, all these methods are not end-to-end, i.e., they always rely on some time-consuming post-processing steps to refine predictions. To this end, we reformulate video grounding as a set prediction task and propose a novel end-to-end multi-modal Transformer model, dubbed as GTR. Specifically, GTR has two encoders for video and language encoding, and a cross-modal decoder for grounding prediction. To facilitate the end-to-end training, we use a Cubic Embedding layer to transform the raw videos into a set of visual tokens. To better fuse these two modalities in the decoder, we design a new Multi-head Cross-Modal Attention. The whole GTR is optimized via a Many-to-One matching loss. Furthermore, we conduct comprehensive studies to investigate different model design choices. Extensive results on three benchmarks have validated the superiority of GTR. All three typical GTR variants achieve record-breaking performance on all datasets and metrics, with several times faster inference speed."
}

@inproceedings{zhang-etal-2021-parallel,
    title = "Parallel Attention Network with Sequence Matching for Video Grounding",
    author = "Zhang, Hao  and
      Sun, Aixin  and
      Jing, Wei  and
      Zhen, Liangli  and
      Zhou, Joey Tianyi  and
      Goh, Siow Mong Rick",
    editor = "Zong, Chengqing  and
      Xia, Fei  and
      Li, Wenjie  and
      Navigli, Roberto",
    booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.findings-acl.69/",
    doi = "10.18653/v1/2021.findings-acl.69",
    pages = "776--790"
}

@inproceedings{10.1145/3394171.3413862,
author = {Wu, Jie and Li, Guanbin and Han, Xiaoguang and Lin, Liang},
title = {Reinforcement Learning for Weakly Supervised Temporal Grounding of Natural Language in Untrimmed Videos},
year = {2020},
isbn = {9781450379885},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3394171.3413862},
doi = {10.1145/3394171.3413862},
abstract = {Temporal grounding of natural language in untrimmed videos is a fundamental yet challenging multimedia task facilitating cross-media visual content retrieval. We focus on the weakly supervised setting of this task that merely accesses to coarse video-level language description annotation without temporal boundary, which is more consistent with reality as such weak labels are more readily available in practice. In this paper, we propose a Boundary Adaptive Refinement (BAR) framework that resorts to reinforcement learning (RL) to guide the process of progressively refining the temporal boundary. To the best of our knowledge, we offer the first attempt to extend RL to temporal localization task with weak supervision. As it is non-trivial to obtain a straightforward reward function in the absence of pairwise granular boundary-query annotations, a cross-modal alignment evaluator is crafted to measure the alignment degree of segment-query pair to provide tailor-designed rewards. This refinement scheme completely abandons traditional sliding window based solution pattern and contributes to acquiring more efficient, boundary-flexible and content-aware grounding results. Extensive experiments on two public benchmarks Charades-STA and ActivityNet demonstrate that BAR outperforms the state-of-the-art weakly-supervised method and even beats some competitive fully-supervised ones.},
booktitle = {Proceedings of the 28th ACM International Conference on Multimedia},
pages = {1283–1291},
numpages = {9},
keywords = {boundary adaptive refinement, reinforcement learning, temporal grounding of natural language in untrimmed videos},
location = {Seattle, WA, USA},
series = {MM '20}
}

@INPROCEEDINGS{10377813,
  author={Lin, Kevin Qinghong and Zhang, Pengchuan and Chen, Joya and Pramanick, Shraman and Gao, Difei and Wang, Alex Jinpeng and Yan, Rui and Shou, Mike Zheng},
  booktitle={2023 IEEE/CVF International Conference on Computer Vision (ICCV)}, 
  title={UniVTG: Towards Unified Video-Language Temporal Grounding}, 
  year={2023},
  volume={},
  number={},
  pages={2782-2792},
  keywords={Training;Computer vision;Video on demand;Codes;Grounding;Social networking (online);Annotations},
  doi={10.1109/ICCV51070.2023.00262}}

@article{Li2025Universal,title={Universal Video Temporal Grounding with Generative Multi-modal Large Language Models},author={Zeqian Li and Shangzhe Di and Zhonghua Zhai and Weilin Huang and Yanfeng Wang and Weidi Xie},journal={ArXiv},year={2025},volume={abs/2506.18883},doi={10.48550/arxiv.2506.18883}}

@article{Chen2025SurgLLM:,title={SurgLLM: A Versatile Large Multimodal Model with Spatial Focus and Temporal Awareness for Surgical Video Understanding},author={Zhen Chen and Xingjian Luo and Kun Yuan and Jinlin Wu and Danny T. M. Chan and Nassir Navab and Hongbin Liu and Zhen Lei and Jiebo Luo},journal={ArXiv},year={2025},volume={abs/2509.00357},doi={10.48550/arxiv.2509.00357}}

@ARTICLE{10179965,
  author={Zhang, Xuemei and Zhao, Peng and Ji, Jinsheng and Lu, Xiankai and Yin, Yilong},
  journal={IEEE Transactions on Circuits and Systems for Video Technology}, 
  title={Video Corpus Moment Retrieval via Deformable Multigranularity Feature Fusion and Adversarial Training}, 
  year={2024},
  volume={34},
  number={8},
  pages={6686-6698},
  keywords={Training;Task analysis;Location awareness;Feature extraction;Semantics;Visualization;Transformers;Video corpus moment retrieval;deformable attention;multi-granularity;adversarial training},
  doi={10.1109/TCSVT.2023.3294567}}

@inproceedings{gupta-etal-2026-medgenvid-qa-overview, 
  title = "Overview of the MedGenVidQA 2026 Shared Task on Medical Generative Video Question Answering", 
  author = "Gupta, Deepak and Campbell, Collin Scott and Golnari, Pedram and Demner-Fushman, Dina", 
  booktitle = "Proceedings of the 25th Workshop on Biomedical Language Processing (BioNLP 2026)", 
  year = "2026",
  address = "San Diego, USA", 
  publisher = "Association for Computational Linguistics"
}

@inproceedings{10.5555/3495724.3496517,
author = {Lewis, Patrick and Perez, Ethan and Piktus, Aleksandra and Petroni, Fabio and Karpukhin, Vladimir and Goyal, Naman and K\"{u}ttler, Heinrich and Lewis, Mike and Yih, Wen-tau and Rockt\"{a}schel, Tim and Riedel, Sebastian and Kiela, Douwe},
title = {Retrieval-augmented generation for knowledge-intensive NLP tasks},
year = {2020},
isbn = {9781713829546},
publisher = {Curran Associates Inc.},
address = {Red Hook, NY, USA},
abstract = {Large pre-trained language models have been shown to store factual knowledge in their parameters, and achieve state-of-the-art results when fine-tuned on downstream NLP tasks. However, their ability to access and precisely manipulate knowledge is still limited, and hence on knowledge-intensive tasks, their performance lags behind task-specific architectures. Additionally, providing provenance for their decisions and updating their world knowledge remain open research problems. Pre-trained models with a differentiable access mechanism to explicit non-parametric memory can overcome this issue, but have so far been only investigated for extractive downstream tasks. We explore a general-purpose fine-tuning recipe for retrieval-augmented generation (RAG) — models which combine pre-trained parametric and non-parametric memory for language generation. We introduce RAG models where the parametric memory is a pre-trained seq2seq model and the non-parametric memory is a dense vector index of Wikipedia, accessed with a pre-trained neural retriever. We compare two RAG formulations, one which conditions on the same retrieved passages across the whole generated sequence, and another which can use different passages per token. We fine-tune and evaluate our models on a wide range of knowledge-intensive NLP tasks and set the state of the art on three open domain QA tasks, outperforming parametric seq2seq models and task-specific retrieve-and-extract architectures. For language generation tasks, we find that RAG models generate more specific, diverse and factual language than a state-of-the-art parametric-only seq2seq baseline.},
booktitle = {Proceedings of the 34th International Conference on Neural Information Processing Systems},
articleno = {793},
numpages = {16},
location = {Vancouver, BC, Canada},
series = {NIPS '20}
}

@article{Rackauckas2024RAGFusionAN,
  title={RAG-Fusion: a New Take on Retrieval-Augmented Generation},
  author={Zackary Rackauckas},
  journal={ArXiv},
  year={2024},
  volume={abs/2402.03367},
  url={https://api.semanticscholar.org/CorpusID:267499891}
}

@misc{castellano_pyscenedetect,
  author       = {Castellano, Brandon and contributors},
  title        = {{PySceneDetect}: A video scene cut detection and analysis tool},
  howpublished = {\url{https://github.com/Breakthrough/PySceneDetect}},
  note         = {Accessed: 2026-04-29},
  year         = {2025}
}

@article{Qwen3-VL,
      title={Qwen3-VL Technical Report}, 
      author={Shuai Bai and Yuxuan Cai and Ruizhe Chen and Keqin Chen and Xionghui Chen and Zesen Cheng and Lianghao Deng and Wei Ding and Chang Gao and Chunjiang Ge and Wenbin Ge and Zhifang Guo and Qidong Huang and Jie Huang and Fei Huang and Binyuan Hui and Shutong Jiang and Zhaohai Li and Mingsheng Li and Mei Li and Kaixin Li and Zicheng Lin and Junyang Lin and Xuejing Liu and Jiawei Liu and Chenglong Liu and Yang Liu and Dayiheng Liu and Shixuan Liu and Dunjie Lu and Ruilin Luo and Chenxu Lv and Rui Men and Lingchen Meng and Xuancheng Ren and Xingzhang Ren and Sibo Song and Yuchong Sun and Jun Tang and Jianhong Tu and Jianqiang Wan and Peng Wang and Pengfei Wang and Qiuyue Wang and Yuxuan Wang and Tianbao Xie and Yiheng Xu and Haiyang Xu and Jin Xu and Zhibo Yang and Mingkun Yang and Jianxin Yang and An Yang and Bowen Yu and Fei Zhang and Hang Zhang and Xi Zhang and Bo Zheng and Humen Zhong and Jingren Zhou and Fan Zhou and Jing Zhou and Yuanzhi Zhu and Ke Zhu},
	  journal={arXiv preprint arXiv:2511.21631},
      year={2025}
}

@article{Qwen3-ASR,
  title={Qwen3-ASR Technical Report},
  author={Xian Shi and Xiong Wang and Zhifang Guo and Yongqi Wang and Pei Zhang and Xinyu Zhang and Zishan Guo and Hongkun Hao and Yu Xi and Baosong Yang and Jin Xu and Jingren Zhou and Junyang Lin},
  journal={arXiv preprint arXiv:2601.21337},
  year={2026}
}

@misc{radford2022whisper,
  doi = {10.48550/ARXIV.2212.04356},
  url = {https://arxiv.org/abs/2212.04356},
  author = {Radford, Alec and Kim, Jong Wook and Xu, Tao and Brockman, Greg and McLeavey, Christine and Sutskever, Ilya},
  title = {Robust Speech Recognition via Large-Scale Weak Supervision},
  publisher = {arXiv},
  year = {2022},
  copyright = {arXiv.org perpetual, non-exclusive license}
}

@misc{nvidia2024parakeet,
  title={{Parakeet-TDT-0.6B-v2}: State-of-the-Art Speech Recognition Model},
  author={{NVIDIA}},
  year={2024},
  publisher={Hugging Face},
  howpublished={\url{https://huggingface.co/nvidia/parakeet-tdt-0.6b-v2}}
}

@article{embedding_gemma_2025,
    title={EmbeddingGemma: Powerful and Lightweight Text Representations},
    author={Schechter Vera, Henrique* and Dua, Sahil* and Zhang, Biao and Salz, Daniel and Mullins, Ryan and Raghuram Panyam, Sindhu and Smoot, Sara and Naim, Iftekhar and Zou, Joe and Chen, Feiyang and Cer, Daniel and Lisak, Alice and Choi, Min and Gonzalez, Lucas and Sanseviero, Omar and Cameron, Glenn and Ballantyne, Ian and Black, Kat and Chen, Kaifeng and Wang, Weiyi and Li, Zhe and Martins, Gus and Lee, Jinhyuk and Sherwood, Mark and Ji, Juyeong and Wu, Renjie and Zheng, Jingxiao and Singh, Jyotinder and Sharma, Abheesht and Sreepat, Divya and Jain, Aashi and Elarabawy, Adham and Co, AJ and Doumanoglou, Andreas and Samari, Babak and Hora, Ben and Potetz, Brian and Kim, Dahun and Alfonseca, Enrique and Moiseev, Fedor and Han, Feng and Palma Gomez, Frank and Hernández Ábrego, Gustavo and Zhang, Hesen and Hui, Hui and Han, Jay and Gill, Karan and Chen, Ke and Chen, Koert and Shanbhogue, Madhuri and Boratko, Michael and Suganthan, Paul and Duddu, Sai Meher Karthik and Mariserla, Sandeep and Ariafar, Setareh and Zhang, Shanfeng and Zhang, Shijie and Baumgartner, Simon and Goenka, Sonam and Qiu, Steve and Dabral, Tanmaya and Walker, Trevor and Rao, Vikram and Khawaja, Waleed and Zhou, Wenlei and Ren, Xiaoqi and Xia, Ye and Chen, Yichang and Chen, Yi-Ting and Dong, Zhe and Ding, Zhongli and Visin, Francesco and Liu, Gaël and Zhang, Jiageng and Kenealy, Kathleen and Casbon, Michelle and Kumar, Ravin and Mesnard, Thomas and Gleicher, Zach and Brick, Cormac and Lacombe, Olivier and Roberts, Adam and Sung, Yunhsuan and Hoffmann, Raphael and Warkentin, Tris and Joulin, Armand and Duerig, Tom and Seyedhosseini, Mojtaba},
    publisher={Google DeepMind},
    year={2025},
    url={https://arxiv.org/abs/2509.20354}
}

@article{Winterbottom2020OnMB,
  title={On Modality Bias in the TVQA Dataset},
  author={Thomas Winterbottom and Sarah Xiao and Alistair McLean and N. A. Moubayed},
  journal={ArXiv},
  year={2020},
  volume={abs/2012.10210},
  url={https://api.semanticscholar.org/CorpusID:221668467}
}

