Search Results for author: João F. Henriques

Found 27 papers, 13 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challenge to current state-of-the-art recognition pipelines, since the assumption of a mostly upright camera is no longer valid.

Pixels Together Strong: Segmenting Unknown Regions Rejected by All

no code implementations25 Nov 2022 Nazir Nayal, Mısra Yavuz, João F. Henriques, Fatma Güney

Semantic segmentation methods typically perform per-pixel classification by assuming a fixed set of semantic categories.

Classification Semantic Segmentation

PVT3D: Point Voxel Transformers for Place Recognition from Sparse Lidar Scans

no code implementations22 Nov 2022 Yan Xia, Mariia Gladkova, Rui Wang, João F. Henriques, Daniel Cremers, Uwe Stilla

Training deep networks to match such scans presents a difficult trade-off: a higher spatial resolution of the network's intermediate representations is needed to perform fine-grained matching of subtle geometric features, but growing it too large makes the memory requirements infeasible.

Learn what matters: cross-domain imitation learning with task-relevant embeddings

no code implementations24 Sep 2022 Tim Franzmeyer, Philip H. S. Torr, João F. Henriques

We study how an autonomous agent learns to perform a task from demonstrations in a different domain, such as a different environment or different agent.

Imitation Learning

Illusionary Attacks on Sequential Decision Makers and Countermeasures

no code implementations20 Jul 2022 Tim Franzmeyer, João F. Henriques, Jakob N. Foerster, Philip H. S. Torr, Adel Bibi, Christian Schroeder de Witt

In this paper, we note that such minimum-norm perturbation attacks can be trivially detected by victim agents, as these result in observation sequences that are not consistent with the victim agent's actions.

A 23 MW data centre is all you need

no code implementations31 Mar 2022 Samuel Albanie, Dylan Campbell, João F. Henriques

The field of machine learning has achieved striking progress in recent years, witnessing breakthrough results on language modelling, protein folding and nitpickingly fine-grained dog breed classification.

Board Games Language Modelling +1

Audio Retrieval with Natural Language Queries: A Benchmark Study

1 code implementation17 Dec 2021 A. Sophia Koepke, Andreea-Maria Oncescu, João F. Henriques, Zeynep Akata, Samuel Albanie

Additionally, we introduce the SoundDescs benchmark, which consists of paired audio and natural language descriptions for a diverse collection of sounds that are complementary to those found in AudioCaps and Clotho.

Audio captioning Audio to Text Retrieval +4

Quantised Transforming Auto-Encoders: Achieving Equivariance to Arbitrary Transformations in Deep Networks

no code implementations25 Nov 2021 Jianbo Jiao, João F. Henriques

In this work we investigate how to achieve equivariance to input transformations in deep networks, purely from data, without being given a model of those transformations.

Pose Estimation Translation

Towards real-world navigation with deep differentiable planners

1 code implementation CVPR 2022 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.

Imitation Learning Motion Planning +3

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.

General Classification Image Clustering +3

Meta-learning with differentiable closed-form solvers

5 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.

BIG-bench Machine Learning Few-Shot Learning +1

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.

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.

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

8 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 Detection +1

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

9 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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