Publications by Martin Pierre Engilberge

CLOAF: CoLlisiOn-Aware Human Flow

Pascal Fua, Martin Pierre Engilberge, Andrey Davydov

Even the best current algorithms for estimating body 3D shape and pose yield results that include body self- intersections. In this paper, we present CLOAF, which exploits the diffeomorphic nature of Ordinary Differential Equations to eliminate such self-i ...

IEEE2024

No Identity, no problem: Motion through detection for people tracking

Pascal Fua, Martin Pierre Engilberge

Tracking-by-detection has become the de facto standard approach to people tracking. To increase robustness, some approaches incorporate re-identification using appearance models and regressing motion offset, which requires costly identity annotations. In t ...

2024

Multi-view Tracking Using Weakly Supervised Human Motion Prediction

Pascal Fua, Martin Pierre Engilberge, Weizhe Liu

Multi-view approaches to people-tracking have the potential to better handle occlusions than single-view ones in crowded scenes. They often rely on the tracking-by-detection paradigm, which involves detecting people first and then connecting the detections ...

2023

Learning Transformations To Reduce the Geometric Shift in Object Detection

Mathieu Salzmann, Martin Pierre Engilberge, Vidit Vidit

The performance of modern object detectors drops when the test distribution differs from the training one. Most of the methods that address this focus on object appearance changes caused by, e.g., different illumination conditions, or gaps between syntheti ...

Ieee Computer Soc2023

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

Mathieu Salzmann, Martin Pierre Engilberge, Vidit Vidit

Single Domain Generalization (SDG) tackles the problem of training a model on a single source domain so that it generalizes to any unseen target domain. While this has been well studied for image classification, the literature on SDG object detection remai ...

Ieee Computer Soc2023

Multi-view Tracking UsingWeakly Supervised Human Motion Prediction

Pascal Fua, Martin Pierre Engilberge, Weizhe Liu

Multi-view approaches to people-tracking have the potential to better handle occlusions than single-view ones in crowded scenes. They often rely on the tracking-by-detection paradigm, which involves detecting people first and then connecting the detections ...

IEEE COMPUTER SOC2023

Two-level Data Augmentation for Calibrated Multi-view Detection

Pascal Fua, Martin Pierre Engilberge, Zhiye Wang, Haixin Shi

Data augmentation has proven its usefulness to improve model generalization and performance. While it is commonly applied in computer vision application when it comes to multi-view systems, it is rarely used. Indeed geometric data augmentation can break th ...

IEEE COMPUTER SOC2023

Color Representation in Deep Neural Networks

Sabine Süsstrunk, Martin Pierre Engilberge, Edo Collins

Convolutional neural networks are top-performers on image classification tasks. Understanding how they make use of color information in images may be useful for various tasks. In this paper we analyze the representation learned by a popular CNN to detect a ...

2017