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.
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 • 15 Sep 2023 • Abhishek Mandal, Susan Leavy, Suzanne Little
We examine bias amplification when models belonging to these two architectures are used as a part of large multimodal models, evaluating the different image encoders of Contrastive Language Image Pretraining which is an important model used in many generative models such as DALL-E and Stable Diffusion.
1 code implementation • 17 May 2023 • Sidra Aleem, Mayug Maniparambil, Suzanne Little, Noel O'Connor, Kevin McGuinness
Chest X-rays have been widely used for COVID-19 screening; however, 3D computed tomography (CT) is a more effective modality.
no code implementations • 26 Apr 2023 • Abhishek Mandal, Susan Leavy, Suzanne Little
In this paper, we propose Multimodal Composite Association Score (MCAS) as a new method of measuring gender bias in multimodal generative models.
no code implementations • 22 Apr 2023 • Małgorzata Gutowska, Suzanne Little, Andrew McCarren
Unsupervised anomaly detection (AD) is critical for a wide range of practical applications, from network security to health and medical tools.
1 code implementation • 3 Oct 2022 • Sidra Aleem, Teerath Kumar, Suzanne Little, Malika Bendechache, Rob Brennan, Kevin McGuinness
To evaluate the generalization of the proposed method, we use four medical datasets and compare its performance with state-of-the-art methods for both classification and segmentation tasks.
no code implementations • 8 Sep 2021 • Shardul Suryawanshi, Bharathi Raja Chakravarthi, Mihael Arcan, Suzanne Little, Paul Buitelaar
To enable this analysis, we enhanced an existing dataset by annotating the data with our defined classes, resulting in a dataset of 8, 881 IWT or multimodal memes in the English language (TrollsWithOpinion dataset).
no code implementations • 31 Jul 2020 • Feiyan Hu, Venkatesh G M, Noel E. O'Connor, Alan F. Smeaton, Suzanne Little
We investigate: 1) How a visual attention map such as a \emph{subjectness} attention or saliency map and an \emph{objectness} attention map can facilitate region proposal generation in a 2-stage object detector; 2) How a visual attention map can be used for tracking multiple objects.
no code implementations • 14 May 2020 • Sérgio Nunes, Suzanne Little, Sumit Bhatia, Ludovico Boratto, Guillaume Cabanac, Ricardo Campos, Francisco M. Couto, Stefano Faralli, Ingo Frommholz, Adam Jatowt, Alípio Jorge, Mirko Marras, Philipp Mayr, Giovanni Stilo
In this report, we describe the experience of organizing the ECIR 2020 Workshops in this scenario from two perspectives: the workshop organizers and the workshop participants.
no code implementations • 25 Oct 2019 • Panagiotis Linardos, Suzanne Little, Kevin McGuinness
This work tackles the Pixel Privacy task put forth by MediaEval 2019.
no code implementations • CVPR 2018 • Mark Marsden, Kevin McGuinness, Suzanne Little, Ciara E. Keogh, Noel E. O'Connor
In this paper we propose a technique to adapt a convolutional neural network (CNN) based object counter to additional visual domains and object types while still preserving the original counting function.
1 code implementation • 30 May 2017 • Mark Marsden, Kevin McGuinness, Suzanne Little, Noel E. O'Connor
In this paper we propose ResnetCrowd, a deep residual architecture for simultaneous crowd counting, violent behaviour detection and crowd density level classification.
no code implementations • 1 Dec 2016 • Mark Marsden, Kevin McGuinness, Suzanne Little, Noel E. O'Connor
In this paper we advance the state-of-the-art for crowd counting in high density scenes by further exploring the idea of a fully convolutional crowd counting model introduced by (Zhang et al., 2016).