Search Results for author: Eleonora Vig

Found 8 papers, 2 papers with code

Learning Attribute-Driven Disentangled Representations for Interactive Fashion Retrieval

1 code implementation ICCV 2021 Yuxin Hou, Eleonora Vig, Michael Donoser, Loris Bazzani

Interactive retrieval for online fashion shopping provides the ability of changing image retrieval results according to the user feedback.

Disentanglement Image Retrieval +1

SkyScapes -- Fine-Grained Semantic Understanding of Aerial Scenes

no code implementations12 Jul 2020 Seyed Majid Azimi, Corentin Henry, Lars Sommer, Arne Schumann, Eleonora Vig

We have defined two main tasks on this dataset: dense semantic segmentation and multi-class lane-marking prediction.

Autonomous Driving Edge Detection +2

Towards Multi-class Object Detection in Unconstrained Remote Sensing Imagery

no code implementations7 Jul 2018 Seyed Majid Azimi, Eleonora Vig, Reza Bahmanyar, Marco Körner, Peter Reinartz

During training, we minimize joint horizontal and oriented bounding box loss functions, as well as a novel loss that enforces oriented boxes to be rectangular.

Management object-detection +2

End-to-End Saliency Mapping via Probability Distribution Prediction

1 code implementation CVPR 2016 Saumya Jetley, Naila Murray, Eleonora Vig

Most saliency estimation methods aim to explicitly model low-level conspicuity cues such as edges or blobs and may additionally incorporate top-down cues using face or text detection.

Saliency Prediction

Sympathy for the Details: Dense Trajectories and Hybrid Classification Architectures for Action Recognition

no code implementations25 Aug 2016 César Roberto de Souza, Adrien Gaidon, Eleonora Vig, Antonio Manuel López

Action recognition in videos is a challenging task due to the complexity of the spatio-temporal patterns to model and the difficulty to acquire and learn on large quantities of video data.

Action Recognition Action Recognition In Videos +3

Virtual Worlds as Proxy for Multi-Object Tracking Analysis

no code implementations CVPR 2016 Adrien Gaidon, Qiao Wang, Yohann Cabon, Eleonora Vig

We provide quantitative experimental evidence suggesting that (i) modern deep learning algorithms pre-trained on real data behave similarly in real and virtual worlds, and (ii) pre-training on virtual data improves performance.

Instance Segmentation Multi-Object Tracking +4

Online Domain Adaptation for Multi-Object Tracking

no code implementations4 Aug 2015 Adrien Gaidon, Eleonora Vig

We quantitatively measure the benefit of our domain adaptation strategy on the KITTI tracking benchmark and on a new dataset (PASCAL-to-KITTI) we introduce to study the domain mismatch problem in MOT.

Domain Adaptation Multi-Object Tracking +1

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