activity
20182022
most citedLocalizing Objects with Self-Supervised Transformers and no Labels

107 citations · 111 across the 3 of their papers we have counts for

collaborators

11 papers

cs.CV20222 cited

Image-to-Lidar Self-Supervised Distillation for Autonomous Driving Data

Corentin Sautier, Gilles Puy, Spyros Gidaris +3

Segmenting or detecting objects in sparse Lidar point clouds are two important tasks in autonomous driving to allow a vehicle to act safely in its 3D environment. The best performi…

cs.CV2021107 cited

Localizing Objects with Self-Supervised Transformers and no Labels

Oriane Siméoni, Gilles Puy, Huy V. Vo +6

Localizing objects in image collections without supervision can help to avoid expensive annotation campaigns. We propose a simple approach to this problem, that leverages the activ…

cs.CV2020

OBoW: Online Bag-of-Visual-Words Generation for Self-Supervised Learning

Spyros Gidaris, Andrei Bursuc, Gilles Puy +3

Learning image representations without human supervision is an important and active research field. Several recent approaches have successfully leveraged the idea of making such a…

cs.CV2020

Learning Representations by Predicting Bags of Visual Words

Spyros Gidaris, Andrei Bursuc, Nikos Komodakis +2

Self-supervised representation learning targets to learn convnet-based image representations from unlabeled data. Inspired by the success of NLP methods in this area, in this work…

cs.CV2019

QUEST: Quantized embedding space for transferring knowledge

Himalaya Jain, Spyros Gidaris, Nikos Komodakis +2

Knowledge distillation refers to the process of training a compact student network to achieve better accuracy by learning from a high capacity teacher network. Most of the existing…

cs.CV2019

Large-Scale Historical Watermark Recognition: dataset and a new consistency-based approach

Xi Shen, Ilaria Pastrolin, Oumayma Bounou +4

Historical watermark recognition is a highly practical, yet unsolved challenge for archivists and historians. With a large number of well-defined classes, cluttered and noisy sampl…