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pro vyhledávání: '"Couso A"'
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
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
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:
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
Autor:
Reboredo-Fernández, Aurora, Abeledo-Lameiro, María Jesús, Couso-Pérez, Seila, Polo-López, María Inmaculada, Fernández-Ibáñez, Pilar, Ares-Mazás, Elvira, Gómez-Couso, Hipólito
Publikováno v:
In Journal of Water Process Engineering January 2025 69
Microbes are often discussed in terms of dichotomies such as copiotrophic/oligotrophic and fast/slow-growing microbes, defined using the characterisation of microbial growth in isolated cultures. The dichotomies are usually qualitative and/or study-s
Externí odkaz:
http://arxiv.org/abs/2303.12000
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. This down-sampling typically employs different strategies for the ima
Externí odkaz:
http://arxiv.org/abs/2302.13961
Autor:
Alba Macías Couso
Publikováno v:
Pragmalingüística, Iss 32 (2024)
El objetivo de este trabajo consiste en caracterizar, dentro de la tipología de la variación, los fenómenos lingüísticos de cheli, parlache, lunfardo y coa. La tipología tradicional de la variación (diatópica, diafásica, diastrática y diacr
Externí odkaz:
https://doaj.org/article/8e47fbd610034857b7c5964a6b4ac2a7