1.3k citations · 1.8k across the 10 of their papers we have counts for
8 papers · 1 filter
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…
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…
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…
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…