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Tullie Murrell

dblp:251/9178 · DBLP profile ↗
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6ranked-venue papers
0as first author
3since 2021 · last 2025
0000-0001-5592-8218ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 4 · 2 since 2021Graphics, computer vision, multimedia, augmented reality and games · 4 · 2 since 2021Applied, interdisciplinary, general and emerging computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
5 papers
Video understanding and tracking · 66% Efficient and distributed learning · 8% Deep learning architectures and training · 7%
Computer graphics and multimedia
2 papers
Image and video processing · 84% Multimedia analysis and retrieval · 16%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Medical and health informatics · 100%

Topics — the 14 heaviest of 15, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computer vision › Video understanding and tracking
egocentric video understanding		 1.422025
Ego4D: Around the World in 3,600 Hours of Egocentric Video · IEEE Trans. Pattern Anal. Mach. Intell. 2025
Ego4D: Around the World in 3, 000 Hours of Egocentric Video · CVPR 2022
Computer vision › Video understanding and tracking
activity prediction		 0.912025
Ego4D: Around the World in 3,600 Hours of Egocentric Video · IEEE Trans. Pattern Anal. Mach. Intell. 2025
Computer vision › Video understanding and tracking
activity understanding		 0.612022
Ego4D: Around the World in 3, 000 Hours of Egocentric Video · CVPR 2022
Computer vision › Video understanding and tracking › egocentric video understanding
first-person activity recognition		 0.612022
Ego4D: Around the World in 3, 000 Hours of Egocentric Video · CVPR 2022
Machine learning › Efficient and distributed learning
hardware acceleration		 0.512021
PyTorchVideo: A Deep Learning Library for Video Understanding · ACM Multimedia 2021
Machine learning › Trustworthy machine learning › robustness › adversarial robustness
adversarial training		 0.412020
MRI Banding Removal via Adversarial Training · NeurIPS 2020
Machine learning › Generative modeling › diffusion model › image restoration
artifact correction		 0.412020
MRI Banding Removal via Adversarial Training · NeurIPS 2020
Machine learning › Deep learning architectures and training
physics-informed neural network		 0.412020
GrappaNet: Combining Parallel Imaging With Deep Learning for Multi-Coil MRI Reconstruction · CVPR 2020
Medical and health informatics
medical imaging		 0.412020
GrappaNet: Combining Parallel Imaging With Deep Learning for Multi-Coil MRI Reconstruction · CVPR 2020
Medical and health informatics › medical imaging › magnetic resonance imaging
MRI reconstruction		 0.412020
GrappaNet: Combining Parallel Imaging With Deep Learning for Multi-Coil MRI Reconstruction · CVPR 2020
Image and video processing
image restoration		 0.412020
MRI Banding Removal via Adversarial Training · NeurIPS 2020
Image and video processing › image reconstruction › medical image reconstruction
MRI reconstruction		 0.412020
MRI Banding Removal via Adversarial Training · NeurIPS 2020
Computer vision › 3D vision
3d scene reconstruction		 0.312025
Ego4D: Around the World in 3,600 Hours of Egocentric Video · IEEE Trans. Pattern Anal. Mach. Intell. 2025
Multimedia analysis and retrieval › video dataset
video dataset benchmark		 0.212022
Ego4D: Around the World in 3, 000 Hours of Egocentric Video · CVPR 2022

Methods — techniques the papers use, named apart from their topics

deep learning · 1.7parallel imaging · 0.9compressed sensing · 0.9adversarial loss · 0.9multimodal data loading · 0.5deep learning library · 0.5
YearPublicationVenuePosition
2025 Ego4D: Around the World in 3,600 Hours of Egocentric Video
abstract
We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite. It offers 3,670 hours of daily-life activity video spanning hundreds of scenarios (household, outdoor, workplace, leisure, etc.) captured by 931 unique camera wearers from 74 worldwide locations and 9 different countries. The approach to collection is designed to uphold rigorous privacy and ethics standards, with consenting participants and robust de-identification procedures where relevant. Ego4D dramatically expands the volume of diverse egocentric video footage publicly available to the research community. Portions of the video are accompanied by audio, 3D meshes of the environment, eye gaze, stereo, and/or synchronized videos from multiple egocentric cameras at the same event. Furthermore, we present a host of new benchmark challenges centered around understanding the first-person visual experience in the past (querying an episodic memory), present (analyzing hand-object manipulation, audio-visual conversation, and social interactions), and future (forecasting activities). By publicly sharing this massive annotated dataset and benchmark suite, we aim to push the frontier of first-person perception.
Kristen Grauman, Andrew Westbury, Eugene Byrne, Vincent Cartillier, Zachary Chavis, Antonino Furnari, Rohit Girdhar, Jackson Hamburger, Hao Jiang 0007, Devansh Kukreja, Miao Liu 0007, Xingyu Liu 0001, Tushar Nagarajan, Ilija Radosavovic, Santhosh K. Ramakrishnan, Fiona Ryan, Jayant Sharma 0002, Michael Wray, Mengmeng Xu 0006, Eric Zhongcong Xu, Chen Zhao 0002, Siddhant Bansal, Dhruv Batra, Sean Crane, Tien Do, Morrie Doulaty, Akshay Erapalli, Christoph Feichtenhofer, Adriano Fragomeni, Qichen Fu, Abrham Gebreselasie, Cristina González, James Hillis, Xuhua Huang, Yifei Huang 0002, Wenqi Jia 0001, Weslie Khoo, Jáchym Kolár, Satwik Kottur, Anurag Kumar 0003, Federico Landini, Yanghao Li, Zhenqiang Li 0002, Karttikeya Mangalam, Raghava Modhugu, Jonathan Munro, Tullie Murrell, Takumi Nishiyasu, Will Price, Paola Ruiz Puentes, Merey Ramazanova, Leda Sari, Kiran K. Somasundaram, Audrey Southerland, Yusuke Sugano, Ruijie Tao, Minh Vo, Xindi Wu, Takuma Yagi, Ziwei Zhao 0003, Yunyi Zhu, Pablo Andrés Arbeláez, David Crandall, Dima Damen, Giovanni Maria Farinella, Christian Fügen, Bernard Ghanem, Vamsi K. Ithapu, C. V. Jawahar, Hanbyul Joo, Kris Makoto Kitani, Haizhou Li 0001, Richard A. Newcombe, Aude Oliva, Hyun Soo Park, James M. Rehg, Yoichi Sato 0001, Jianbo Shi, Zheng Shou 0001, Antonio Torralba 0001, Lorenzo Torresani, Mingfei Yan, Jitendra Malik
 IEEE Trans. Pattern Anal. Mach. Intell.49
2022 Ego4D: Around the World in 3, 000 Hours of Egocentric Video
abstract
We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite. It offers 3,670 hours of dailylife activity video spanning hundreds of scenarios (household, outdoor, workplace, leisure, etc.) captured by 931 unique camera wearers from 74 worldwide locations and 9 different countries. The approach to collection is designed to uphold rigorous privacy and ethics standards, with consenting participants and robust de-identification procedures where relevant. Ego4D dramatically expands the volume of diverse egocentric video footage publicly available to the research community. Portions of the video are accompanied by audio, 3D meshes of the environment, eye gaze, stereo, and/or synchronized videos from multiple egocentric cameras at the same event. Furthermore, we present a host of new benchmark challenges centered around understanding the first-person visual experience in the past (querying an episodic memory), present (analyzing hand-object manipulation, audio-visual conversation, and social interactions), and future (forecasting activities). By publicly sharing this massive annotated dataset and benchmark suite, we aim to push the frontier of first-person perception. Project page: https://ego4d-data.org/
Kristen Grauman, Andrew Westbury, Eugene Byrne, Zachary Chavis, Antonino Furnari, Rohit Girdhar, Jackson Hamburger, Hao Jiang 0007, Miao Liu 0007, Xingyu Liu 0001, Tushar Nagarajan, Ilija Radosavovic, Santhosh K. Ramakrishnan, Fiona Ryan, Jayant Sharma 0002, Michael Wray, Mengmeng Xu 0006, Eric Zhongcong Xu, Chen Zhao 0002, Siddhant Bansal, Dhruv Batra, Vincent Cartillier, Sean Crane, Tien Do, Morrie Doulaty, Akshay Erapalli, Christoph Feichtenhofer, Adriano Fragomeni, Qichen Fu, Abrham Gebreselasie, Cristina González, James Hillis, Xuhua Huang, Yifei Huang 0002, Wenqi Jia 0001, Weslie Khoo, Jáchym Kolár, Satwik Kottur, Anurag Kumar 0003, Federico Landini, Yanghao Li, Zhenqiang Li 0002, Karttikeya Mangalam, Raghava Modhugu, Jonathan Munro, Tullie Murrell, Takumi Nishiyasu, Will Price, Paola Ruiz Puentes, Merey Ramazanova, Leda Sari, Kiran K. Somasundaram, Audrey Southerland, Yusuke Sugano, Ruijie Tao, Minh Vo, Xindi Wu, Takuma Yagi, Ziwei Zhao 0003, Yunyi Zhu, Pablo Andrés Arbeláez, David Crandall, Dima Damen, Giovanni Maria Farinella, Christian Fügen, Bernard Ghanem, Vamsi K. Ithapu, C. V. Jawahar, Hanbyul Joo, Kris Makoto Kitani, Haizhou Li 0001, Richard A. Newcombe, Aude Oliva, Hyun Soo Park, James M. Rehg, Yoichi Sato 0001, Jianbo Shi, Zheng Shou 0001, Antonio Torralba 0001, Lorenzo Torresani, Mingfei Yan, Jitendra Malik
 CVPR48
2021 PyTorchVideo: A Deep Learning Library for Video Understanding
abstract
We introduce PyTorchVideo, an open-source deep-learning library that provides a rich set of modular, efficient, and reproducible components for a variety of video understanding tasks, including classification, detection, self-supervised learning, and low-level processing. The library covers a full stack of video understanding tools including multimodal data loading, transformations, and models that reproduce state-of-the-art performance. PyTorchVideo further supports hardware acceleration that enables real-time inference on mobile devices. The library is based on PyTorch and can be used by any training framework; for example, PyTorchLightning, PySlowFast, or Classy Vision. PyTorchVideo is available at https://pytorchvideo.org/.
Haoqi Fan 0001, Tullie Murrell, Kalyan Vasudev Alwala, Yanghao Li, Yilei Li, Nikhila Ravi, Meng Li 0004, Haichuan Yang, Jitendra Malik, Ross B. Girshick, Matt Feiszli, Aaron Adcock, Wan-Yen Lo, Christoph Feichtenhofer
 ACM Multimedia2
2020 GrappaNet: Combining Parallel Imaging With Deep Learning for Multi-Coil MRI Reconstruction
abstract
Magnetic Resonance Image (MRI) acquisition is an inherently slow process which has spurred the development of two different acceleration methods: acquiring multiple correlated samples simultaneously (parallel imaging) and acquiring fewer samples than necessary for traditional signal processing methods (compressed sensing). Both methods provide complementary approaches to accelerating MRI acquisition. In this paper, we present a novel method to integrate traditional parallel imaging methods into deep neural networks that is able to generate high quality reconstructions even for high acceleration factors. The proposed method, called GrappaNet, performs progressive reconstruction by first mapping the reconstruction problem to a simpler one that can be solved by a traditional parallel imaging methods using a neural network, followed by an application of a parallel imaging method, and finally fine-tuning the output with another neural network. The entire network can be trained end-to-end. We present experimental results on the recently released fastMRI dataset and show that GrappaNet can generate higher quality reconstructions than competing methods for both 4× and 8× acceleration.
Anuroop Sriram, Jure Zbontar, Tullie Murrell, C. Lawrence Zitnick, Aaron Defazio, Daniel K. Sodickson
 CVPR3
2020 End-to-End Variational Networks for Accelerated MRI Reconstruction
Anuroop Sriram, Jure Zbontar, Tullie Murrell, Aaron Defazio, C. Lawrence Zitnick, Nafissa Yakubova, Florian Knoll, Patricia M. Johnson
 MICCAI (2)3
2020 MRI Banding Removal via Adversarial Training
abstract
MR images reconstructed from sub-sampled Cartesian data using deep learning techniques show a characteristic banding (sometimes described as streaking), which is particularly strong in low signal-to-noise regions of the reconstructed image. In this work, we propose the use of an adversarial loss that penalizes banding structures without requiring any human annotation. Our technique greatly reduces the appearance of banding, without requiring any additional computation or post-processing at reconstruction time. We report the results of a blind comparison against a strong baseline by a group of expert evaluators (board-certified radiologists), where our approach is ranked superior at banding removal with no statistically significant loss of detail. A reference implementation of our method is available in the supplementary material.
Aaron Defazio, Tullie Murrell, Michael P. Recht
 NeurIPS2