no code implementations • 8 Feb 2024 • Ivana Balažević, Yuge Shi, Pinelopi Papalampidi, Rahma Chaabouni, Skanda Koppula, Olivier J. Hénaff
Most transformer-based video encoders are limited to short temporal contexts due to their quadratic complexity.
no code implementations • 18 Dec 2023 • Nikhil Parthasarathy, Olivier J. Hénaff, Eero P. Simoncelli
Finally, when the two-stage model is used as a fixed front-end for a deep network trained to perform object recognition, the resultant model (LCL-V2Net) is significantly better than standard end-to-end self-supervised, supervised, and adversarially-trained models in terms of generalization to out-of-distribution tasks and alignment with human behavior.
no code implementations • 23 Mar 2023 • Relja Arandjelović, Alex Andonian, Arthur Mensch, Olivier J. Hénaff, Jean-Baptiste Alayrac, Andrew Zisserman
The core problem in zero-shot open vocabulary detection is how to align visual and text features, so that the detector performs well on unseen classes.
no code implementations • 12 Oct 2022 • Nikhil Parthasarathy, S. M. Ali Eslami, João Carreira, Olivier J. Hénaff
Humans learn powerful representations of objects and scenes by observing how they evolve over time.
1 code implementation • 16 Mar 2022 • Olivier J. Hénaff, Skanda Koppula, Evan Shelhamer, Daniel Zoran, Andrew Jaegle, Andrew Zisserman, João Carreira, Relja Arandjelović
The promise of self-supervised learning (SSL) is to leverage large amounts of unlabeled data to solve complex tasks.
2 code implementations • ICCV 2021 • Olivier J. Hénaff, Skanda Koppula, Jean-Baptiste Alayrac, Aaron van den Oord, Oriol Vinyals, João Carreira
Self-supervised pretraining has been shown to yield powerful representations for transfer learning.
Ranked #55 on Semantic Segmentation on Cityscapes val (using extra training data)
2 code implementations • 12 Jun 2020 • Lucas Beyer, Olivier J. Hénaff, Alexander Kolesnikov, Xiaohua Zhai, Aäron van den Oord
Yes, and no.
4 code implementations • ICML 2020 • Olivier J. Hénaff, Aravind Srinivas, Jeffrey De Fauw, Ali Razavi, Carl Doersch, S. M. Ali Eslami, Aaron van den Oord
Human observers can learn to recognize new categories of images from a handful of examples, yet doing so with artificial ones remains an open challenge.
Ranked #6 on Contrastive Learning on imagenet-1k
no code implementations • 19 Nov 2015 • Olivier J. Hénaff, Eero P. Simoncelli
We develop a new method for visualizing and refining the invariances of learned representations.
no code implementations • 20 Dec 2014 • Olivier J. Hénaff, Johannes Ballé, Neil C. Rabinowitz, Eero P. Simoncelli
We develop a new statistical model for photographic images, in which the local responses of a bank of linear filters are described as jointly Gaussian, with zero mean and a covariance that varies slowly over spatial position.