Search Results for author: Joan Serrat

Found 6 papers, 1 papers with code

Implicit Learning of Scene Geometry from Poses for Global Localization

no code implementations4 Dec 2023 Mohammad Altillawi, Shile Li, Sai Manoj Prakhya, Ziyuan Liu, Joan Serrat

In this paper, we propose to utilize these minimal available labels (. i. e, poses) to learn the underlying 3D geometry of the scene and use the geometry to estimate the 6 DoF camera pose.

Visual Localization

Weakly Supervised Multi-Object Tracking and Segmentation

no code implementations3 Jan 2021 Idoia Ruiz, Lorenzo Porzi, Samuel Rota Bulò, Peter Kontschieder, Joan Serrat

We introduce the problem of weakly supervised Multi-Object Tracking and Segmentation, i. e. joint weakly supervised instance segmentation and multi-object tracking, in which we do not provide any kind of mask annotation.

Instance Segmentation Multi-Object Tracking +7

Monitoring War Destruction from Space: A Machine Learning Approach

no code implementations12 Oct 2020 Hannes Mueller, Andre Groger, Jonathan Hersh, Andrea Matranga, Joan Serrat

Existing data on building destruction in conflict zones rely on eyewitness reports or manual detection, which makes it generally scarce, incomplete and potentially biased.

BIG-bench Machine Learning Data Augmentation +1

Learning Multi-Object Tracking and Segmentation from Automatic Annotations

no code implementations CVPR 2020 Lorenzo Porzi, Markus Hofinger, Idoia Ruiz, Joan Serrat, Samuel Rota Bulò, Peter Kontschieder

Training MOTSNet with our automatically extracted data leads to significantly improved sMOTSA scores on the novel KITTI MOTS dataset (+1. 9%/+7. 5% on cars/pedestrians), and MOTSNet improves by +4. 1% over previously best methods on the MOTSChallenge dataset.

Instance Segmentation Multi-Object Tracking +4

Metric Learning for Novelty and Anomaly Detection

1 code implementation16 Aug 2018 Marc Masana, Idoia Ruiz, Joan Serrat, Joost Van de Weijer, Antonio M. Lopez

When neural networks process images which do not resemble the distribution seen during training, so called out-of-distribution images, they often make wrong predictions, and do so too confidently.

Anomaly Detection Metric Learning +3

Road Detection via On--line Label Transfer

no code implementations10 Dec 2014 José M. Álvarez, Ferran Diego, Joan Serrat, Antonio M. López

The major challenges of road detection are dealing with shadows and lighting variations and the presence of other objects in the scene.

Pedestrian Detection valid +1

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