Zobrazeno 1 - 10
of 4 967
pro vyhledávání: '"Sanmiguel, P."'
Autor:
Montalvo, Javier, Alcover-Couso, Roberto, Carballeira, Pablo, García-Martín, Álvaro, SanMiguel, Juan C., Escudero-Viñolo, Marcos
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 effective feature alignment across domains. Additionally, we propose
Externí odkaz:
http://arxiv.org/abs/2412.16592
Segmentation models are typically constrained by the categories defined during training. To address this, researchers have explored two independent approaches: adapting Vision-Language Models (VLMs) and leveraging synthetic data. However, VLMs often
Externí odkaz:
http://arxiv.org/abs/2412.09240
We consider the problem of text-to-video generation tasks with precise control for various applications such as camera movement control and video-to-video editing. Most methods tacking this problem rely on providing user-defined controls, such as bin
Externí odkaz:
http://arxiv.org/abs/2411.10501
Test-Time Adaptation for Keypoint-Based Spacecraft Pose Estimation Based on Predicted-View Synthesis
Publikováno v:
IEEE Transactions on Aerospace and Electronic Systems (2024)
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 synthetic data and applied to real operational data. To address this issue
Externí odkaz:
http://arxiv.org/abs/2410.04298
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 creation and inference. In this paper, we leverage the abundance of free
Externí odkaz:
http://arxiv.org/abs/2409.15813
Autor:
Francés-Belda, Víctor, Solera-Rico, Alberto, Nieto-Centenero, Javier, Andrés, Esther, Vila, Carlos Sanmiguel, Castellanos, Rodrigo
Surrogate models that combine dimensionality reduction and regression techniques are essential to reduce the need for costly high-fidelity computational fluid dynamics data. New approaches using $\beta$-Variational Autoencoder ($\beta$-VAE) architect
Externí odkaz:
http://arxiv.org/abs/2408.04969
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, addressing the challenge of significant class imbalance remains an open issue
Externí odkaz:
http://arxiv.org/abs/2407.01327
Open-Vocabulary Attention Maps with Token Optimization for Semantic Segmentation in Diffusion Models
Publikováno v:
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2024)
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 successfully extended to the joint generation of semantic segmentation pseudo-ma
Externí odkaz:
http://arxiv.org/abs/2403.14291
Autor:
Edith Mora Ordoñez
Publikováno v:
Visitas al Patio, Iss 12 (2018)
Las literaturas de la frontera en México y chicana en Estados Unidos traducen diversas perspectivas y significaciones de identidad fronteriza desde ambos territorios. Este trabajo realiza una interpretación de los relatos “Bajo el puente” y “
Externí odkaz:
https://doaj.org/article/3e6747ea71cd4b539229aa84e72b864e
Autor:
Yu, Xuanlong, Zuo, Yi, Wang, Zitao, Zhang, Xiaowen, Zhao, Jiaxuan, Yang, Yuting, Jiao, Licheng, Peng, Rui, Wang, Xinyi, Zhang, Junpei, Zhang, Kexin, Liu, Fang, Alcover-Couso, Roberto, SanMiguel, Juan C., Escudero-Viñolo, Marcos, Tian, Hanlin, Matsui, Kenta, Wang, Tianhao, Adan, Fahmy, Gao, Zhitong, He, Xuming, Bouniot, Quentin, Moghaddam, Hossein, Rai, Shyam Nandan, Cermelli, Fabio, Masone, Carlo, Pilzer, Andrea, Ricci, Elisa, Bursuc, Andrei, Solin, Arno, Trapp, Martin, Li, Rui, Yao, Angela, Chen, Wenlong, Simpson, Ivor, Campbell, Neill D. F., Franchi, Gianni
This paper outlines the winning solutions employed in addressing the MUAD uncertainty quantification challenge held at ICCV 2023. The challenge was centered around semantic segmentation in urban environments, with a particular focus on natural advers
Externí odkaz:
http://arxiv.org/abs/2309.15478