5 citations · 17 across the 20 of their papers we have counts for
21 papers
ReSAGE-PAR: Representational Similarity Assessment for Generative Expansion in Pedestrian Attribute Recognition
Pablo Ayuso-Albizu, Pablo Carballeira, Juan C. SanMiguel +1
To address the limited diversity and data scarcity in Pedestrian Attribute Recognition (PAR), we explore image synthesis using diffusion models guided by attribute-based prompts. W…
MIRAGE: Retrieval and Generation of Multimodal Images and Texts for Medical Education
Miguel Diaz Benito, Cecilia Diana Albelda, Alvaro Garcia Martin +3
Access to diverse, well-annotated medical images with interactive learning tools is fundamental for training practitioners in medicine and related fields to improve their diagnosti…
Large Language Models Meet Extreme Multi-label Classification: Scaling and Multi-modal Framework
Diego Ortego, Marlon Rodríguez, Mario Almagro +3
Foundation models have revolutionized artificial intelligence across numerous domains, yet their transformative potential remains largely untapped in Extreme Multi-label Classifica…
Enhancing Zero-Shot Pedestrian Attribute Recognition with Synthetic Data Generation: A Comparative Study with Image-To-Image Diffusion Models
Pablo Ayuso-Albizu, Juan C. SanMiguel, Pablo Carballeira
Pedestrian Attribute Recognition (PAR) involves identifying various human attributes from images with applications in intelligent monitoring systems. The scarcity of large-scale an…
A Data-Centric Approach to Pedestrian Attribute Recognition: Synthetic Augmentation via Prompt-driven Diffusion Models
Alejandro Alonso, Sawaiz A. Chaudhry, Juan C. SanMiguel +3
Pedestrian Attribute Recognition (PAR) is a challenging task as models are required to generalize across numerous attributes in real-world data. Traditional approaches focus on com…
Unsupervised Class Generation to Expand Semantic Segmentation Datasets
Javier Montalvo, Álvaro García-Martín, Pablo Carballeira +1
Semantic segmentation is a computer vision task where classification is performed at a pixel level. Due to this, the process of labeling images for semantic segmentation is time-co…
Leveraging Contrastive Learning for Semantic Segmentation with Consistent Labels Across Varying Appearances
Javier Montalvo, Roberto Alcover-Couso, Pablo Carballeira +3
This paper introduces a novel synthetic dataset that captures urban scenes under a variety of weather conditions, providing pixel-perfect, ground-truth-aligned images to facilitate…
VLMs meet UDA: Boosting Transferability of Open Vocabulary Segmentation with Unsupervised Domain Adaptation
Roberto Alcover-Couso, Marcos Escudero-Viñolo, Juan C. SanMiguel +1
Segmentation models are typically constrained by the categories defined during training. To address this, researchers have explored two independent approaches: adapting Vision-Lang…
Test-Time Adaptation for Keypoint-Based Spacecraft Pose Estimation Based on Predicted-View Synthesis
Juan Ignacio Bravo Pérez-Villar, Álvaro García-Martín, Jesús Bescós +1
Due to the difficulty of replicating the real conditions during training, supervised algorithms for spacecraft pose estimation experience a drop in performance when trained on synt…
Layer-wise Model Merging for Unsupervised Domain Adaptation in Segmentation Tasks
Roberto Alcover-Couso, Juan C. SanMiguel, Marcos Escudero-Viñolo +1
Merging parameters of multiple models has resurfaced as an effective strategy to enhance task performance and robustness, but prior work is limited by the high costs of ensemble cr…
Gradient-based Class Weighting for Unsupervised Domain Adaptation in Dense Prediction Visual Tasks
Roberto Alcover-Couso, Marcos Escudero-Viñolo, Juan C. SanMiguel +1
In unsupervised domain adaptation (UDA), where models are trained on source data (e.g., synthetic) and adapted to target data (e.g., real-world) without target annotations, address…
Open-Vocabulary Attention Maps with Token Optimization for Semantic Segmentation in Diffusion Models
Pablo Marcos-Manchón, Roberto Alcover-Couso, Juan C. SanMiguel +1
Diffusion models represent a new paradigm in text-to-image generation. Beyond generating high-quality images from text prompts, models such as Stable Diffusion have been successful…
The Robust Semantic Segmentation UNCV2023 Challenge Results
Xuanlong Yu, Yi Zuo, Zitao Wang +34
This paper outlines the winning solutions employed in addressing the MUAD uncertainty quantification challenge held at ICCV 2023. The challenge was centered around semantic segment…
Soft labelling for semantic segmentation: Bringing coherence to label down-sampling
Roberto Alcover-Couso, Marcos Escudero-Vinolo, Juan C. SanMiguel +1
In semantic segmentation, training data down-sampling is commonly performed due to limited resources, the need to adapt image size to the model input, or improve data augmentation.…
Detection-aware multi-object tracking evaluation
Juan C. SanMiguel, Jorge Muñoz, Fabio Poiesi
How would you fairly evaluate two multi-object tracking algorithms (i.e. trackers), each one employing a different object detector? Detectors keep improving, thus trackers can make…
Attention-based Knowledge Distillation in Multi-attention Tasks: The Impact of a DCT-driven Loss
Alejandro López-Cifuentes, Marcos Escudero-Viñolo, Jesús Bescós +1
Knowledge Distillation (KD) is a strategy for the definition of a set of transferability gangways to improve the efficiency of Convolutional Neural Networks. Feature-based Knowledg…
Graph Neural Networks for Cross-Camera Data Association
Elena Luna, Juan C. SanMiguel, José M. Martínez +1
Cross-camera image data association is essential for many multi-camera computer vision tasks, such as multi-camera pedestrian detection, multi-camera multi-target tracking, 3D pose…
Improved skin lesion recognition by a Self-Supervised Curricular Deep Learning approach
Kirill Sirotkin, Marcos Escudero-Viñolo, Pablo Carballeira +1
State-of-the-art deep learning approaches for skin lesion recognition often require pretraining on larger and more varied datasets, to overcome the generalization limitations deriv…
Deep Anomaly Generation: An Image Translation Approach of Synthesizing Abnormal Banded Chromosome Images
Lukas Uzolas, Javier Rico, Pierrick Coupé +2
Advances in deep-learning-based pipelines have led to breakthroughs in a variety of microscopy image diagnostics. However, a sufficiently big training data set is usually difficult…
Online Clustering-based Multi-Camera Vehicle Tracking in Scenarios with overlapping FOVs
Elena Luna, Juan C. SanMiguel, Jose M. Martínez +1
Multi-Target Multi-Camera (MTMC) vehicle tracking is an essential task of visual traffic monitoring, one of the main research fields of Intelligent Transportation Systems. Several…
On guiding video object segmentation
Diego Ortego, Kevin McGuinness, Juan C. SanMiguel +3
This paper presents a novel approach for segmenting moving objects in unconstrained environments using guided convolutional neural networks. This guiding process relies on foregrou…