24 citations · 39 across the 17 of their papers we have counts for
12 papers · 1 filter
Beyond Training from Scratch: Foundation Models for Data-Efficient and Generalizable Cardiac MRI Reconstruction
Anam Hashmi, Mayug Maniparambil, Julia Dietlmeier +2
Cardiac magnetic resonance imaging reconstruction aims to recover high-quality images from undersampled acquisitions, enabling faster scans while preserving diagnostic fidelity. Re…
Ensemble Learning with Sparse Hypercolumns
Julia Dietlmeier, Vayangi Ganepola, Oluwabukola G. Adegboro +3
Directly inspired by findings in biological vision, high-dimensional hypercolumns are feature vectors built by concatenating multi-scale activations of convolutional neural network…
Underrepresented in Foundation Model Pretraining Data? A One-Shot Probe
Chris Vorster, Mayug Maniparambil, Noel E. O'Connor +2
Large-scale Vision-Language Foundation Models (VLFMs), such as CLIP, now underpin a wide range of computer vision research and applications. VLFMs are often adapted to various doma…
Hold-One-Shot-Out (HOSO) for Validation-Free Few-Shot CLIP Adapters
Chris Vorster, Mayug Maniparambil, Noel E. O'Connor +2
In many CLIP adaptation methods, a blending ratio hyperparameter controls the trade-off between general pretrained CLIP knowledge and the limited, dataset-specific supervision from…
Understanding Space Is Rocket Science -- Only Top Reasoning Models Can Solve Spatial Understanding Tasks
Nils Hoehing, Mayug Maniparambil, Ellen Rushe +2
We propose RocketScience, an open-source contrastive VLM benchmark that tests for spatial relation understanding. It is comprised of entirely new real-world image-text pairs coveri…
Pinpoint Counterfactuals: Reducing social bias in foundation models via localized counterfactual generation
Kirill Sirotkin, Marcos Escudero-Viñolo, Pablo Carballeira +3
Foundation models trained on web-scraped datasets propagate societal biases to downstream tasks. While counterfactual generation enables bias analysis, existing methods introduce a…
Harnessing Frozen Unimodal Encoders for Flexible Multimodal Alignment
Mayug Maniparambil, Raiymbek Akshulakov, Yasser Abdelaziz Dahou Djilali +3
Recent contrastive multimodal vision-language models like CLIP have demonstrated robust open-world semantic understanding, becoming the standard image backbones for vision-language…
Test-Time Adaptation with SaLIP: A Cascade of SAM and CLIP for Zero shot Medical Image Segmentation
Sidra Aleem, Fangyijie Wang, Mayug Maniparambil +6
The Segment Anything Model (SAM) and CLIP are remarkable vision foundation models (VFMs). SAM, a prompt driven segmentation model, excels in segmentation tasks across diverse domai…
Do Vision and Language Encoders Represent the World Similarly?
Mayug Maniparambil, Raiymbek Akshulakov, Yasser Abdelaziz Dahou Djilali +4
Aligned text-image encoders such as CLIP have become the de facto model for vision-language tasks. Furthermore, modality-specific encoders achieve impressive performances in their…
Enhancing CLIP with GPT-4: Harnessing Visual Descriptions as Prompts
Mayug Maniparambil, Chris Vorster, Derek Molloy +3
Contrastive pretrained large Vision-Language Models (VLMs) like CLIP have revolutionized visual representation learning by providing good performance on downstream datasets. VLMs a…
BaseTransformers: Attention over base data-points for One Shot Learning
Mayug Maniparambil, Kevin McGuinness, Noel O'Connor
Few shot classification aims to learn to recognize novel categories using only limited samples per category. Most current few shot methods use a base dataset rich in labeled exampl…
Phase retrieval for Fourier Ptychography under varying amount of measurements
Lokesh Boominathan, Mayug Maniparambil, Honey Gupta +2
Fourier Ptychography is a recently proposed imaging technique that yields high-resolution images by computationally transcending the diffraction blur of an optical system. At the c…