Search Results for author: Marcos Escudero-Viñolo

Found 10 papers, 1 papers with code

Self-Supervised Curricular Deep Learning for Chest X-Ray Image Classification

no code implementations25 Jan 2023 Iván de Andrés Tamé, Kirill Sirotkin, Pablo Carballeira, Marcos Escudero-Viñolo

Deep learning technologies have already demonstrated a high potential to build diagnosis support systems from medical imaging data, such as Chest X-Ray images.

Image Classification Self-Supervised Learning

Attention-based Knowledge Distillation in Multi-attention Tasks: The Impact of a DCT-driven Loss

no code implementations4 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.

Descriptive Knowledge Distillation +1

Online Clustering-based Multi-Camera Vehicle Tracking in Scenarios with overlapping FOVs

no code implementations8 Feb 2021 Elena Luna, Juan C. SanMiguel, Jose M. Martínez, Marcos Escudero-Viñolo

Multi-Target Multi-Camera (MTMC) vehicle tracking is an essential task of visual traffic monitoring, one of the main research fields of Intelligent Transportation Systems.

Clustering Online Clustering

Egocentric Human Segmentation for Mixed Reality

no code implementations25 May 2020 Andrija Gajic, Ester Gonzalez-Sosa, Diego Gonzalez-Morin, Marcos Escudero-Viñolo, Alvaro Villegas

The objective of this work is to segment human body parts from egocentric video using semantic segmentation networks.

Mixed Reality Segmentation +1

Semantic Driven Multi-Camera Pedestrian Detection

no code implementations27 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.

Pedestrian Detection Semantic Segmentation

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