Zobrazeno 1 - 10
of 1 634
pro vyhledávání: '"Facciolo A"'
Autor:
Bou, Xavier, Facciolo, Gabriele, von Gioi, Rafael Grompone, Morel, Jean-Michel, Ehret, Thibaud
Oriented object detection predicts orientation in addition to object location and bounding box. Precisely predicting orientation remains challenging due to angular periodicity, which introduces boundary discontinuity issues and symmetry ambiguities.
Externí odkaz:
http://arxiv.org/abs/2411.10497
Autor:
Dewil, Valéry, Zheng, Zhe, Barral, Arnaud, Raad, Lara, Nicolas, Nao, Cassagne, Ioannis, Morel, Jean-michel, Facciolo, Gabriele, Galerne, Bruno, Arias, Pablo
MIMO (multiple input, multiple output) approaches are a recent trend in neural network architectures for video restoration problems, where each network evaluation produces multiple output frames. The video is split into non-overlapping stacks of fram
Externí odkaz:
http://arxiv.org/abs/2408.12439
Publikováno v:
Inverse Problems and Imaging, 2024, 18(3):571-599
Image demosaicing and denoising play a critical role in the raw imaging pipeline. These processes have often been treated as independent, without considering their interactions. Indeed, most classic denoising methods handle noisy RGB images, not raw
Externí odkaz:
http://arxiv.org/abs/2408.06684
Autor:
Di Piazza, Theo, Meinhardt-Llopis, Enric, Facciolo, Gabriele, Bascle, Benedicte, Abgrall, Corentin, Devaux, Jean-Clement
We propose a novel method for geolocalizing Unmanned Aerial Vehicles (UAVs) in environments lacking Global Navigation Satellite Systems (GNSS). Current state-of-the-art techniques employ an offline-trained encoder to generate a vector representation
Externí odkaz:
http://arxiv.org/abs/2404.06207
Autor:
Bou, Xavier, Facciolo, Gabriele, von Gioi, Rafael Grompone, Morel, Jean-Michel, Ehret, Thibaud
The goal of this paper is to perform object detection in satellite imagery with only a few examples, thus enabling users to specify any object class with minimal annotation. To this end, we explore recent methods and ideas from open-vocabulary detect
Externí odkaz:
http://arxiv.org/abs/2403.05381
Publikováno v:
IEEE Signal Processing Letters, 2021, 28:1515-1519
With the widespread application of convolutional neural networks (CNNs), the traditional model based denoising algorithms are now outperformed. However, CNNs face two problems. First, they are computationally demanding, which makes their deployment e
Externí odkaz:
http://arxiv.org/abs/2403.03488
Radiance fields have been a major breakthrough in the field of inverse rendering, novel view synthesis and 3D modeling of complex scenes from multi-view image collections. Since their introduction, it was shown that they could be extended to other mo
Externí odkaz:
http://arxiv.org/abs/2312.12961
Publikováno v:
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, Jan 2024, WAIKOLOA, HAWAII, United States
Object detection models, a prominent class of machine learning algorithms, aim to identify and precisely locate objects in images or videos. However, this task might yield uneven performances sometimes caused by the objects sizes and the quality of t
Externí odkaz:
http://arxiv.org/abs/2311.11714
Autor:
Bou, Xavier, Artola, Aitor, Ehret, Thibaud, Facciolo, Gabriele, Morel, Jean-Michel, von Gioi, Rafael Grompone
Detecting relevant changes is a fundamental problem of video surveillance. Because of the high variability of data and the difficulty of properly annotating changes, unsupervised methods dominate the field. Arguably one of the most critical issues to
Externí odkaz:
http://arxiv.org/abs/2307.04159
This work proposes a strategy for training models while annotating data named Intelligent Annotation (IA). IA involves three modules: (1) assisted data annotation, (2) background model training, and (3) active selection of the next datapoints. Under
Externí odkaz:
http://arxiv.org/abs/2307.01582