1 code implementation • 28 Aug 2022 • Elena Sizikova, Joshua Vendrow, Xu Cao, Rachel Grotheer, Jamie Haddock, Lara Kassab, Alona Kryshchenko, Thomas Merkh, R. W. M. A. Madushani, Kenny Moise, Annie Ulichney, Huy V. Vo, Chuntian Wang, Megan Coffee, Kathryn Leonard, Deanna Needell
Automatic infectious disease classification from images can facilitate needed medical diagnoses.
1 code implementation • 25 Jul 2022 • Huy V. Vo, Oriane Siméoni, Spyros Gidaris, Andrei Bursuc, Patrick Pérez, Jean Ponce
On COCO, using on average 10 fully-annotated images per class, or equivalently 1% of the training set, BiB also reduces the performance gap (in AP) between the weakly-supervised detector and the fully-supervised Fast RCNN by over 70%, showing a good trade-off between performance and data efficiency.
2 code implementations • 29 Sep 2021 • Oriane Siméoni, Gilles Puy, Huy V. Vo, Simon Roburin, Spyros Gidaris, Andrei Bursuc, Patrick Pérez, Renaud Marlet, Jean Ponce
We also show that training a class-agnostic detector on the discovered objects boosts results by another 7 points.
Ranked #4 on Weakly-Supervised Object Localization on CUB-200-2011 (Top-1 Localization Accuracy metric)
1 code implementation • NeurIPS 2021 • Huy V. Vo, Elena Sizikova, Cordelia Schmid, Patrick Pérez, Jean Ponce
Extensive experiments on COCO and OpenImages show that, in the single-object discovery setting where a single prominent object is sought in each image, the proposed LOD (Large-scale Object Discovery) approach is on par with, or better than the state of the art for medium-scale datasets (up to 120K images), and over 37% better than the only other algorithms capable of scaling up to 1. 7M images.
1 code implementation • ECCV 2020 • Huy V. Vo, Patrick Pérez, Jean Ponce
This paper addresses the problem of discovering the objects present in a collection of images without any supervision.
Ranked #1 on Multi-object colocalization on VOC_all
1 code implementation • CVPR 2019 • Huy V. Vo, Francis Bach, Minsu Cho, Kai Han, Yann Lecun, Patrick Perez, Jean Ponce
Learning with complete or partial supervision is powerful but relies on ever-growing human annotation efforts.
Ranked #2 on Single-object colocalization on Object Discovery
no code implementations • 27 Mar 2018 • Huy V. Vo, Ngoc Q. K. Duong, Patrick Perez
Scene-agnostic visual inpainting remains very challenging despite progress in patch-based methods.