Zobrazeno 1 - 10
of 7 807
pro vyhledávání: '"López,Antonio"'
Autor:
Alberto Porta-Pérez
Publikováno v:
Mirai, Vol 7 (2023)
Estudios sobre cultura visual japonesa, coordinado por Antonio Loriguillo-López, contiene un total de nueve capítulos que reflexionan sobre videojuegos, manga y anime. El plantel de investigadores que conforman la publicación demuestra la compleji
Externí odkaz:
https://doaj.org/article/3303f1509ecd40958f1c98334b818875
We study an approximate controllability problem for the continuity equation and its application to constructing transport maps with normalizing flows. Specifically, we construct time-dependent controls $\theta=(w, a, b)$ in the vector field $w(a^\top
Externí odkaz:
http://arxiv.org/abs/2412.19366
Publikováno v:
Mirai, Vol 6 (2022)
La publicación de Antonio Loriguillo-López es la última y acertada inclusión en una lista, cada vez más extensa, de monografías académicas en lengua castellana, centradas en los Estudios de Anime (Anime Studies). Su aproximación escapa del qu
Externí odkaz:
https://doaj.org/article/8081050eaaa84cfda133a55b68303ddb
Understanding and predicting pedestrian crossing behavioral intention is crucial for autonomous vehicles driving safety. Nonetheless, challenges emerge when using promising images or environmental context masks to extract various factors for time-ser
Externí odkaz:
http://arxiv.org/abs/2409.20223
The last mile of unsupervised domain adaptation (UDA) for semantic segmentation is the challenge of solving the syn-to-real domain gap. Recent UDA methods have progressed significantly, yet they often rely on strategies customized for synthetic singl
Externí odkaz:
http://arxiv.org/abs/2406.18809
The development of Autonomous Driving (AD) systems in simulated environments like CARLA is crucial for advancing real-world automotive technologies. To drive innovation, CARLA introduced Leaderboard 2.0, significantly more challenging than its predec
Externí odkaz:
http://arxiv.org/abs/2406.08421
Back to the Color: Learning Depth to Specific Color Transformation for Unsupervised Depth Estimation
Autor:
Zhu, Yufan, Ran, Chongzhi, Feng, Mingtao, Wu, Fangfang, Dong, Le, Dong, Weisheng, López, Antonio M., Shi, Guangming
Virtual engines can generate dense depth maps for various synthetic scenes, making them invaluable for training depth estimation models. However, discrepancies between synthetic and real-world colors pose significant challenges for depth estimation i
Externí odkaz:
http://arxiv.org/abs/2406.07741
Autor:
Dionis-Ros, Alejandro, Vila-Francés, Joan, Magdalena-Benedicto, Rafael, Mateo, Fernando, Serrano-López, Antonio J.
In this article, we propose the detection of crowd anomalies through the extraction of information in the form of time series from video format using a multimodal approach. Through pattern recognition algorithms and segmentation, informative measures
Externí odkaz:
http://arxiv.org/abs/2405.12708