4 citations · 4 across the 8 of their papers we have counts for
8 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…
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…
SynthmanticLiDAR: A Synthetic Dataset for Semantic Segmentation on LiDAR Imaging
Javier Montalvo, Pablo Carballeira, Álvaro García-Martín
Semantic segmentation on LiDAR imaging is increasingly gaining attention, as it can provide useful knowledge for perception systems and potential for autonomous driving. However, c…
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…