no code implementations • 10 Apr 2024 • Anam Hashmi, Julia Dietlmeier, Kathleen M. Curran, Noel E. O'Connor
This study aims to explore the untapped potential of attention mechanisms incorporated with a deep learning model within the context of the CMR reconstruction problem.
no code implementations • 9 Apr 2024 • Sidra Aleem, Fangyijie Wang, Mayug Maniparambil, Eric Arazo, Julia Dietlmeier, Kathleen Curran, Noel E. O'Connor, Suzanne Little
Finally, SAM is prompted by the retrieved ROI to segment a specific organ.
no code implementations • 1 Apr 2024 • Jun Lyu, Chen Qin, Shuo Wang, Fanwen Wang, Yan Li, Zi Wang, Kunyuan Guo, Cheng Ouyang, Michael Tänzer, Meng Liu, Longyu Sun, Mengting Sun, Qin Li, Zhang Shi, Sha Hua, Hao Li, Zhensen Chen, Zhenlin Zhang, Bingyu Xin, Dimitris N. Metaxas, George Yiasemis, Jonas Teuwen, Liping Zhang, Weitian Chen, Yidong Zhao, Qian Tao, Yanwei Pang, Xiaohan Liu, Artem Razumov, Dmitry V. Dylov, Quan Dou, Kang Yan, Yuyang Xue, Yuning Du, Julia Dietlmeier, Carles Garcia-Cabrera, Ziad Al-Haj Hemidi, Nora Vogt, Ziqiang Xu, Yajing Zhang, Ying-Hua Chu, Weibo Chen, Wenjia Bai, Xiahai Zhuang, Jing Qin, Lianmin Wu, Guang Yang, Xiaobo Qu, He Wang, Chengyan Wang
To address this issue, we organized the Cardiac MRI Reconstruction Challenge (CMRxRecon) in 2023, in collaboration with the 26th International Conference on MICCAI.
1 code implementation • 7 Feb 2024 • Sidra Aleem, Julia Dietlmeier, Eric Arazo, Suzanne Little
To further boost adaptation, we utilize Adaptive Batch Normalization (AdaBN) which computes target-specific running statistics and use it along with ConvLoRA.
1 code implementation • 1 Aug 2023 • Grace Billingsley, Julia Dietlmeier, Vivek Narayanaswamy, Andreas Spanias, Noel E. OConnor
We compare our results against the state-of-the-art on this dataset and show that by integrating l2-normalized spatial attention into a baseline network we achieve a performance gain of 1. 79 percentage points.
1 code implementation • 11 Oct 2022 • Ayush K. Rai, Tarun Krishna, Julia Dietlmeier, Kevin McGuinness, Alan F. Smeaton, Noel E. O'Connor
In this work, we address this issue by revisiting a simple and effective self-supervised method and augment it with a differentiable motion feature learning module to tackle the spatial and temporal diversities in the GEBD task.
no code implementations • 17 Nov 2021 • Julia Dietlmeier, Feiyan Hu, Frances Ryan, Noel E. O'Connor, Kevin McGuinness
We apply state-of-the-art person re-identification models to our dataset and show that by leveraging the available timestamp information we are able to achieve a significant gain of 37. 43% in mAP and a gain of 30. 22% in Rank1 accuracy.
no code implementations • 18 Jun 2021 • Ayush K Rai, Tarun Krishna, Julia Dietlmeier, Kevin McGuinness, Alan F Smeaton, Noel E O'Connor
Detecting generic, taxonomy-free event boundaries invideos represents a major stride forward towards holisticvideo understanding.
no code implementations • 13 Oct 2020 • Julia Dietlmeier, Joseph Antony, Kevin McGuinness, Noel E. O'Connor
This paper investigates the dependence of existing state-of-the-art person re-identification models on the presence and visibility of human faces.