489 citations · 1.9k across the 46 of their papers we have counts for
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A Performance Evaluation of Local Features for Image Based 3D Reconstruction
Bin Fan, Qingqun Kong, Xinchao Wang +4
This paper performs a comprehensive and comparative evaluation of the state of the art local features for the task of image based 3D reconstruction. The evaluated local features co…
Residual Parameter Transfer for Deep Domain Adaptation
Artem Rozantsev, Mathieu Salzmann, Pascal Fua
The goal of Deep Domain Adaptation is to make it possible to use Deep Nets trained in one domain where there is enough annotated training data in another where there is little or n…
Real-Time Seamless Single Shot 6D Object Pose Prediction
Bugra Tekin, Sudipta N. Sinha, Pascal Fua
We propose a single-shot approach for simultaneously detecting an object in an RGB image and predicting its 6D pose without requiring multiple stages or having to examine multiple…
Learning to Find Good Correspondences
Kwang Moo Yi, Eduard Trulls, Yuki Ono +3
We develop a deep architecture to learn to find good correspondences for wide-baseline stereo. Given a set of putative sparse matches and the camera intrinsics, we train our networ…
The WILDTRACK Multi-Camera Person Dataset
Tatjana Chavdarova, Pierre Baqué, Stéphane Bouquet +6
People detection methods are highly sensitive to the perpetual occlusions among the targets. As multi-camera set-ups become more frequently encountered, joint exploitation of the a…
Deep Occlusion Reasoning for Multi-Camera Multi-Target Detection
Pierre Baqué, François Fleuret, Pascal Fua
People detection in single 2D images has improved greatly in recent years. However, comparatively little of this progress has percolated into multi-camera multi-people tracking alg…
Learning Active Learning from Data
Ksenia Konyushkova, Raphael Sznitman, Pascal Fua
In this paper, we suggest a novel data-driven approach to active learning (AL). The key idea is to train a regressor that predicts the expected error reduction for a candidate samp…