Marc Proesmans — accepted AI-conference papers

2023

Unbalanced Optimal Transport: A Unified Framework for Object Detection

Henri De Plaen, Pierre-François De Plaen, Johan A. K. Suykens, Marc Proesmans, Tinne Tuytelaars, Luc Van Gool

CVPR 2023poster

During training, supervised object detection tries to correctly match the predicted bounding boxes and associated classification scores to the ground truth. This is essential to determine which predictions are to be pushed towards which solutions, or to be discarded. Popular matching strategies incl…

Cited by 17 Source ↗PDF ↗code ↗Scholar ↗

2020

Modeling the Effects of Windshield Refraction for Camera Calibration

Frank Verbiest, Marc Proesmans, Luc Van Gool

ECCV 2020poster

In this paper, we study the effects of windshield refraction for autonomous driving applications. These distortion effects are surprisingly large and can not be explained by traditional camera models. Instead of using a generalized camera approach, we propose a novel approach to jointly optimize a t…

Cited by 16 Source ↗PDF ↗Scholar ↗

2020

SCAN: Learning to Classify Images without Labels

Wouter Van Gansbeke, Simon Vandenhende, Stamatios Georgoulis, Marc Proesmans, Luc Van Gool

ECCV 2020poster

Can we automatically group images into semantically meaningful clusters when ground-truth annotations are absent? The task of unsupervised image classification remains an important, and open challenge in computer vision. Several recent approaches have tried to tackle this problem in an end-to-end fa…

Cited by 710 Source ↗PDF ↗code ↗Scholar ↗

2019

Instance Segmentation by Jointly Optimizing Spatial Embeddings and Clustering Bandwidth

Davy Neven, Bert De Brabandere, Marc Proesmans, Luc Van Gool

CVPR 2019oral

Current state-of-the-art instance segmentation methods are not suited for real-time applications like autonomous driving, which require fast execution times at high accuracy. Although the currently dominant proposal-based methods have high accuracy, they are slow and generate masks at a fixed and lo…

Cited by 325 PDF ↗code ↗Scholar ↗

2015

A Gaussian Process Latent Variable Model for BRDF Inference

Stamatios Georgoulis, Vincent Vanweddingen, Marc Proesmans, Luc Van Gool

ICCV 2015poster

The problem of estimating a full BRDF from partial observations has already been studied using either parametric or non-parametric approaches. The goal in each case is to best match this sparse set of input measurements. In this paper we address the problem of inferring higher order reflectance info…

Cited by 13 PDF ↗Scholar ↗