116 citations · 477 across the 58 of their papers we have counts for
65 papers · 1 filter
MUMINS: Metadata-conditioned Uncertainty-aware Medical Image Next-state Synthesis
Anna Oliveras, Roger Marí, Rafael Redondo +7
Forecasting anatomical changes such as tumor growth and neurodegeneration is a challenging generative vision task. Morphological evolution is subtle relative to static anatomy, hig…
PerBite: A Curated Diagnostic Workflow for Bite-Aware Food Volume Estimation
Ahmad AlMughrabi, Farid Al-Areqi, David Fernández Gómez +4
Can a visually plausible food mesh be trusted to estimate the volume of consumed food? \method investigates this question using selected paired before- and after-consumption states…
BenchSeg: A Large-Scale Dataset and Benchmark for Multi-View Food Video Segmentation
Ahmad AlMughrabi, Guillermo Rivo, Carlos Jiménez-Farfán +6
Food image segmentation is a critical task for dietary analysis, enabling accurate estimation of food volume and nutrients. However, current methods suffer from limited multi-view…
LAND: Lung and Nodule Diffusion for 3D Chest CT Synthesis with Anatomical Guidance
Anna Oliveras, Roger Marí, Rafael Redondo +6
This work introduces a new latent diffusion model to generate high-quality 3D chest CT scans conditioned on 3D anatomical masks. The method synthesizes volumetric images of size 25…
DADO: A Depth-Attention framework for Object Discovery
Federico Gonzalez, Estefania Talavera, Petia Radeva
Unsupervised object discovery, the task of identifying and localizing objects in images without human-annotated labels, remains a significant challenge and a growing focus in compu…
Stochastic-based Patch Filtering for Few-Shot Learning
Javier Rodenas, Eduardo Aguilar, Petia Radeva
Food images present unique challenges for few-shot learning models due to their visual complexity and variability. For instance, a pasta dish might appear with various garnishes on…
Slot Attention-based Feature Filtering for Few-Shot Learning
Javier Rodenas, Eduardo Aguilar, Petia Radeva
Irrelevant features can significantly degrade few-shot learn ing performance. This problem is used to match queries and support images based on meaningful similarities despite the…
Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders
Gabriel A. Carneiro, Thierry J. Aubry, António Cunha +2
Grapevine varieties are essential for the economies of many wine-producing countries, influencing the production of wine, juice, and the consumption of fruits and leaves. Tradition…
Representation Discrepancy Bridging Method for Remote Sensing Image-Text Retrieval
Hailong Ning, Siying Wang, Tao Lei +5
Remote Sensing Image-Text Retrieval (RSITR) plays a critical role in geographic information interpretation, disaster monitoring, and urban planning by establishing semantic associa…
VolE: A Point-cloud Framework for Food 3D Reconstruction and Volume Estimation
Umair Haroon, Ahmad AlMughrabi, Thanasis Zoumpekas +2
Accurate food volume estimation is crucial for medical nutrition management and health monitoring applications, but current food volume estimation methods are often limited by mono…
Calibration and Uncertainty for multiRater Volume Assessment in multiorgan Segmentation (CURVAS) challenge results
Meritxell Riera-Marin, Sikha O K, Julia Rodriguez-Comas +29
Deep learning (DL) has become the dominant approach for medical image segmentation, yet ensuring the reliability and clinical applicability of these models requires addressing key…
Conjuring Positive Pairs for Efficient Unification of Representation Learning and Image Synthesis
Imanol G. Estepa, Jesús M. Rodríguez-de-Vera, Ignacio Sarasúa +2
While representation learning and generative modeling seek to understand visual data, unifying both domains remains unexplored. Recent Unified Self-Supervised Learning (SSL) method…
Multi-label out-of-distribution detection via evidential learning
Eduardo Aguilar, Bogdan Raducanu, Petia Radeva
A crucial requirement for machine learning algorithms is not only to perform well, but also to show robustness and adaptability when encountering novel scenarios. One way to achiev…
MVSBoost: An Efficient Point Cloud-based 3D Reconstruction
Umair Haroon, Ahmad AlMughrabi, Ricardo Marques +1
Efficient and accurate 3D reconstruction is crucial for various applications, including augmented and virtual reality, medical imaging, and cinematic special effects. While traditi…
MetaFood CVPR 2024 Challenge on Physically Informed 3D Food Reconstruction: Methods and Results
Jiangpeng He, Yuhao Chen, Gautham Vinod +16
The increasing interest in computer vision applications for nutrition and dietary monitoring has led to the development of advanced 3D reconstruction techniques for food items. How…
MomentsNeRF: Leveraging Orthogonal Moments for Few-Shot Neural Rendering
Ahmad AlMughrabi, Ricardo Marques, Petia Radeva
We propose MomentsNeRF, a novel framework for one- and few-shot neural rendering that predicts a neural representation of a 3D scene using Orthogonal Moments. Our architecture offe…
FoodMem: Near Real-time and Precise Food Video Segmentation
Ahmad AlMughrabi, Adrián Galán, Ricardo Marques +1
Food segmentation, including in videos, is vital for addressing real-world health, agriculture, and food biotechnology issues. Current limitations lead to inaccurate nutritional an…
Precision at Scale: Domain-Specific Datasets On-Demand
Jesús M Rodríguez-de-Vera, Imanol G Estepa, Ignacio Sarasúa +2
In the realm of self-supervised learning (SSL), conventional wisdom has gravitated towards the utility of massive, general domain datasets for pretraining robust backbones. In this…
VolETA: One- and Few-shot Food Volume Estimation
Ahmad AlMughrabi, Umair Haroon, Ricardo Marques +1
Accurate food volume estimation is essential for dietary assessment, nutritional tracking, and portion control applications. We present VolETA, a sophisticated methodology for esti…
Continual Evidential Deep Learning for Out-of-Distribution Detection
Eduardo Aguilar, Bogdan Raducanu, Petia Radeva +1
Uncertainty-based deep learning models have attracted a great deal of interest for their ability to provide accurate and reliable predictions. Evidential deep learning stands out a…
Pre-NeRF 360: Enriching Unbounded Appearances for Neural Radiance Fields
Ahmad AlMughrabi, Umair Haroon, Ricardo Marques +1
Neural radiance fields (NeRF) appeared recently as a powerful tool to generate realistic views of objects and confined areas. Still, they face serious challenges with open scenes,…
All4One: Symbiotic Neighbour Contrastive Learning via Self-Attention and Redundancy Reduction
Imanol G. Estepa, Ignacio Sarasúa, Bhalaji Nagarajan +1
Nearest neighbour based methods have proved to be one of the most successful self-supervised learning (SSL) approaches due to their high generalization capabilities. However, their…
ELFIS: Expert Learning for Fine-grained Image Recognition Using Subsets
Pablo Villacorta, Jesús M. Rodríguez-de-Vera, Marc Bolaños +3
Fine-Grained Visual Recognition (FGVR) tackles the problem of distinguishing highly similar categories. One of the main approaches to FGVR, namely subset learning, tries to leverag…
Modeling long-term interactions to enhance action recognition
Alejandro Cartas, Petia Radeva, Mariella Dimiccoli
In this paper, we propose a new approach to under-stand actions in egocentric videos that exploits the semantics of object interactions at both frame and temporal levels. At the fr…