Martin Engilberge

According to our database, Martin Engilberge authored at least 10 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

CLOAF: CoLlisiOn-Aware Human Flow.

Andrey Davydov, Martin Engilberge, Mathieu Salzmann, Pascal Fua

CoRR, 2024

2023

Two-level Data Augmentation for Calibrated Multi-view Detection.

Martin Engilberge, Haixin Shi, Zhiye Wang, Pascal Fua

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2023

Multi-view Tracking Using Weakly Supervised Human Motion Prediction.

Martin Engilberge, Weizhe Liu, Pascal Fua

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2023

Learning Transformations to Reduce the Geometric Shift in Object Detection.

Vidit Vidit, Martin Engilberge, Mathieu Salzmann

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

CLIP the Gap: A Single Domain Generalization Approach for Object Detection.

Vidit Vidit, Martin Engilberge, Mathieu Salzmann

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2020

Deep Inside Visual-Semantic Embeddings. (Au cœur des espaces de représentation visuelle-sémantique).

Martin Engilberge

PhD thesis, 2020

2019

VideoMem: Constructing, Analyzing, Predicting Short-Term and Long-Term Video Memorability.

Romain Cohendet, Claire-Hélène Demarty, Ngoc Q. K. Duong, Martin Engilberge

Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, 2019

SoDeep: A Sorting Deep Net to Learn Ranking Loss Surrogates.

Martin Engilberge, Louis Chevallier, Patrick Pérez, Matthieu Cord

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019

2018

Finding Beans in Burgers: Deep Semantic-Visual Embedding With Localization.

Martin Engilberge, Louis Chevallier, Patrick Pérez, Matthieu Cord

Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, 2018

2017

Color representation in deep neural networks.

Martin Engilberge, Edo Collins, Sabine Süsstrunk

Proceedings of the 2017 IEEE International Conference on Image Processing, 2017