1.3k citations · 1.8k across the 10 of their papers we have counts for
12 papers
Making the Most of What You Have: Adapting Pre-trained Visual Language Models in the Low-data Regime
Chuhan Zhang, Antoine Miech, Jiajun Shen +2
Large-scale visual language models are widely used as pre-trained models and then adapted for various downstream tasks. While humans are known to efficiently learn new tasks from a…
Zorro: the masked multimodal transformer
Adrià Recasens, Jason Lin, Joāo Carreira +8
Attention-based models are appealing for multimodal processing because inputs from multiple modalities can be concatenated and fed to a single backbone network - thus requiring ver…
Flamingo: a Visual Language Model for Few-Shot Learning
Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc +24
Building models that can be rapidly adapted to novel tasks using only a handful of annotated examples is an open challenge for multimodal machine learning research. We introduce Fl…
Towards Learning Universal Audio Representations
Luyu Wang, Pauline Luc, Yan Wu +8
The ability to learn universal audio representations that can solve diverse speech, music, and environment tasks can spur many applications that require general sound content under…
Multimodal Self-Supervised Learning of General Audio Representations
Luyu Wang, Pauline Luc, Adria Recasens +2
We present a multimodal framework to learn general audio representations from videos. Existing contrastive audio representation learning methods mainly focus on using the audio mod…
Broaden Your Views for Self-Supervised Video Learning
Adrià Recasens, Pauline Luc, Jean-Baptiste Alayrac +11
Most successful self-supervised learning methods are trained to align the representations of two independent views from the data. State-of-the-art methods in video are inspired by…
Game Plan: What AI can do for Football, and What Football can do for AI
Karl Tuyls, Shayegan Omidshafiei, Paul Muller +33
The rapid progress in artificial intelligence (AI) and machine learning has opened unprecedented analytics possibilities in various team and individual sports, including baseball,…
Transformation-based Adversarial Video Prediction on Large-Scale Data
Pauline Luc, Aidan Clark, Sander Dieleman +4
Recent breakthroughs in adversarial generative modeling have led to models capable of producing video samples of high quality, even on large and complex datasets of real-world vide…
Fully Parallel Hyperparameter Search: Reshaped Space-Filling
M. -L. Cauwet, C. Couprie, J. Dehos +5
Space-filling designs such as scrambled-Hammersley, Latin Hypercube Sampling and Jittered Sampling have been proposed for fully parallel hyperparameter search, and were shown to be…
Predicting Future Instance Segmentation by Forecasting Convolutional Features
Pauline Luc, Camille Couprie, Yann LeCun +1
Anticipating future events is an important prerequisite towards intelligent behavior. Video forecasting has been studied as a proxy task towards this goal. Recent work has shown th…
Predicting Deeper into the Future of Semantic Segmentation
Pauline Luc, Natalia Neverova, Camille Couprie +2
The ability to predict and therefore to anticipate the future is an important attribute of intelligence. It is also of utmost importance in real-time systems, e.g. in robotics or a…
Semantic Segmentation using Adversarial Networks
Pauline Luc, Camille Couprie, Soumith Chintala +1
Adversarial training has been shown to produce state of the art results for generative image modeling. In this paper we propose an adversarial training approach to train semantic s…