Search Results for author: João F. Henriques

Found 18 papers, 10 papers with code

360(o) Camera Alignment via Segmentation

no code implementations ECCV 2020 Benjamin Davidson, Mohsan S. Alvi, João F. Henriques

Panoramic 360º images taken under unconstrained conditions present a significant challenge to current state-of-the-art recognition pipelines, since the assumption of a mostly upright camera is no longer valid.

Towards real-world navigation with deep differentiable planners

no code implementations8 Aug 2021 Shu Ishida, João F. Henriques

To avoid the potentially hazardous trial-and-error of reinforcement learning, we focus on differentiable planners such as Value Iteration Networks (VIN), which are trained offline from safe expert demonstrations.


Learning Altruistic Behaviours in Reinforcement Learning without External Rewards

no code implementations20 Jul 2021 Tim Franzmeyer, Mateusz Malinowski, João F. Henriques

We evaluate our approach on three different multi-agent environments where another agent's success depends on the altruistic agent's behaviour.

Invariant Information Clustering for Unsupervised Image Classification and Segmentation

6 code implementations ICCV 2019 Xu Ji, João F. Henriques, Andrea Vedaldi

The method is not specialised to computer vision and operates on any paired dataset samples; in our experiments we use random transforms to obtain a pair from each image.

Classification General Classification +4

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.

Meta-learning with differentiable closed-form solvers

4 code implementations ICLR 2019 Luca Bertinetto, João F. Henriques, Philip H. S. Torr, Andrea Vedaldi

The main idea is to teach a deep network to use standard machine learning tools, such as ridge regression, as part of its own internal model, enabling it to quickly adapt to novel data.

Few-Shot Learning

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.

Stopping GAN Violence: Generative Unadversarial Networks

1 code implementation7 Mar 2017 Samuel Albanie, Sébastien Ehrhardt, João F. Henriques

While the costs of human violence have attracted a great deal of attention from the research community, the effects of the network-on-network (NoN) violence popularised by Generative Adversarial Networks have yet to be addressed.

Warped Convolutions: Efficient Invariance to Spatial Transformations

no code implementations ICML 2017 João F. Henriques, Andrea Vedaldi

Convolutional Neural Networks (CNNs) are extremely efficient, since they exploit the inherent translation-invariance of natural images.


Fully-Convolutional Siamese Networks for Object Tracking

5 code implementations30 Jun 2016 Luca Bertinetto, Jack Valmadre, João F. Henriques, Andrea Vedaldi, Philip H. S. Torr

The problem of arbitrary object tracking has traditionally been tackled by learning a model of the object's appearance exclusively online, using as sole training data the video itself.

Object Detection Object Tracking

Fast Training of Pose Detectors in the Fourier Domain

no code implementations NeurIPS 2014 João F. Henriques, Pedro Martins, Rui F. Caseiro, Jorge Batista

In many datasets, the samples are related by a known image transformation, such as rotation, or a repeatable non-rigid deformation.

Pose Estimation

High-Speed Tracking with Kernelized Correlation Filters

8 code implementations30 Apr 2014 João F. Henriques, Rui Caseiro, Pedro Martins, Jorge Batista

Interestingly, for linear regression our formulation is equivalent to a correlation filter, used by some of the fastest competitive trackers.

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