1 code implementation • 5 Oct 2024 • Juan Ignacio Bravo Pérez-Villar, Álvaro García-Martín, Jesús Bescós, Juan C. SanMiguel
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.
1 code implementation • 11 Jun 2024 • Javier Montalvo, Juan Ignacio Bravo Pérez-Villar, Álvaro García-Martín, Pablo Carballeira, Jesús Bescós
To address these limitations, we present SPIN (SPacecraft Imagery for Navigation), an open-source spacecraft image generation tool designed to support a wide range of visual navigation scenarios in space, with a particular focus on relative navigation tasks.
1 code implementation • 27 Dec 2022 • Juan Ignacio Bravo Pérez-Villar, Álvaro García-Martín, Jesús Bescós
Spacecraft pose estimation is a key task to enable space missions in which two spacecrafts must navigate around each other.
no code implementations • 4 May 2022 • Alejandro López-Cifuentes, Marcos Escudero-Viñolo, Jesús Bescós, Juan C. SanMiguel
Feature-based Knowledge Distillation is a subfield of KD that relies on intermediate network representations, either unaltered or depth-reduced via maximum activation maps, as the source knowledge.
no code implementations • 26 Aug 2020 • Alejandro López-Cifuentes, Marcos Escudero-Viñolo, Jesús Bescós
Action recognition is currently one of the top-challenging research fields in computer vision.
1 code implementation • 5 Sep 2019 • Alejandro López-Cifuentes, Marcos Escudero-Viñolo, Jesús Bescós, Álvaro García-Martín
Scene recognition is currently one of the top-challenging research fields in computer vision.
Ranked #1 on
Scene Recognition
on ADE20K
no code implementations • 27 Dec 2018 • Alejandro López-Cifuentes, Marcos Escudero-Viñolo, Jesús Bescós, Pablo Carballeira
Contrarily to the majority of the methods of the state-of-the-art, the proposed approach is scene-agnostic, not requiring a tailored adaptation to the target scenario\textemdash e. g., via fine-tunning.