906 citations · 944 across the 9 of their papers we have counts for
6 papers · 1 filter
Towards In-context Scene Understanding
Ivana Balažević, David Steiner, Nikhil Parthasarathy +2
In-context learning$\unicode{x2013}$the ability to configure a model's behavior with different prompts$\unicode{x2013}$has revolutionized the field of natural language processing,…
Self-supervised video pretraining yields robust and more human-aligned visual representations
Nikhil Parthasarathy, S. M. Ali Eslami, João Carreira +1
Humans learn powerful representations of objects and scenes by observing how they evolve over time. Yet, outside of specific tasks that require explicit temporal understanding, sta…
Divide and Contrast: Self-supervised Learning from Uncurated Data
Yonglong Tian, Olivier J. Henaff, Aaron van den Oord
Self-supervised learning holds promise in leveraging large amounts of unlabeled data, however much of its progress has thus far been limited to highly curated pre-training data suc…
Data-Efficient Image Recognition with Contrastive Predictive Coding
Olivier J. Hénaff, Aravind Srinivas, Jeffrey De Fauw +4
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. We hypothesize that data-ef…
Geodesics of learned representations
Olivier J. Hénaff, Eero P. Simoncelli
We develop a new method for visualizing and refining the invariances of learned representations. Specifically, we test for a general form of invariance, linearization, in which the…
The local low-dimensionality of natural images
Olivier J. Hénaff, Johannes Ballé, Neil C. Rabinowitz +1
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 covaria…