Search Results for author: Sebastien Ehrhardt

Found 11 papers, 6 papers with code

Co-Attention for Conditioned Image Matching

no code implementations CVPR 2021 Olivia Wiles, Sebastien Ehrhardt, Andrew Zisserman

We propose a new approach to determine correspondences between image pairs in the wild under large changes in illumination, viewpoint, context, and material.

3D Reconstruction Camera Localization +2

Automatically Discovering and Learning New Visual Categories with Ranking Statistics

1 code implementation ICLR 2020 Kai Han, Sylvestre-Alvise Rebuffi, Sebastien Ehrhardt, Andrea Vedaldi, Andrew Zisserman

In this work we address this problem by combining three ideas: (1) we suggest that the common approach of bootstrapping an image representation using the labeled data only introduces an unwanted bias, and that this can be avoided by using self-supervised learning to train the representation from scratch on the union of labelled and unlabelled data; (2) we use rank statistics to transfer the model's knowledge of the labelled classes to the problem of clustering the unlabelled images; and, (3) we train the data representation by optimizing a joint objective function on the labelled and unlabelled subsets of the data, improving both the supervised classification of the labelled data, and the clustering of the unlabelled data.

General Classification Self-Supervised Learning

Semi-Supervised Learning with Scarce Annotations

1 code implementation21 May 2019 Sylvestre-Alvise Rebuffi, Sebastien Ehrhardt, Kai Han, Andrea Vedaldi, Andrew Zisserman

The first is a simple but effective one: we leverage the power of transfer learning among different tasks and self-supervision to initialize a good representation of the data without making use of any label.

Multi-class Classification Self-Supervised Learning +1

Deep Industrial Espionage

no code implementations1 Apr 2019 Samuel Albanie, James Thewlis, Sebastien Ehrhardt, Joao Henriques

The theory of deep learning is now considered largely solved, and is well understood by researchers and influencers alike.

Small steps and giant leaps: Minimal Newton solvers for Deep Learning

6 code implementations ICLR 2019 João F. Henriques, Sebastien Ehrhardt, Samuel Albanie, Andrea Vedaldi

Instead, we propose to keep a single estimate of the gradient projected by the inverse Hessian matrix, and update it once per iteration.

PyTorch CurveBall - A second-order optimizer for deep networks

1 code implementation21 May 2018 João F. Henriques, Sebastien Ehrhardt, Samuel Albanie, Andrea Vedaldi

We propose a fast second-order method that can be used as a drop-in replacementfor current deep learning solvers.

Unsupervised Intuitive Physics from Visual Observations

no code implementations14 May 2018 Sebastien Ehrhardt, Aron Monszpart, Niloy Mitra, Andrea Vedaldi

While learning models of intuitive physics is an increasingly active area of research, current approaches still fall short of natural intelligences in one important regard: they require external supervision, such as explicit access to physical states, at training and sometimes even at test times.

Taking Visual Motion Prediction To New Heightfields

no code implementations22 Dec 2017 Sebastien Ehrhardt, Aron Monszpart, Niloy Mitra, Andrea Vedaldi

In order to be able to leverage the approximation capabilities of artificial intelligence techniques in such physics related contexts, researchers have handcrafted the relevant states, and then used neural networks to learn the state transitions using simulation runs as training data.

motion prediction

Learning A Physical Long-term Predictor

no code implementations1 Mar 2017 Sebastien Ehrhardt, Aron Monszpart, Niloy J. Mitra, Andrea Vedaldi

Evolution has resulted in highly developed abilities in many natural intelligences to quickly and accurately predict mechanical phenomena.

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