Search Results for author: Andrea Vedaldi

Found 145 papers, 67 papers with code

Guess What Moves: Unsupervised Video and Image Segmentation by Anticipating Motion

no code implementations16 May 2022 Subhabrata Choudhury, Laurynas Karazija, Iro Laina, Andrea Vedaldi, Christian Rupprecht

In the unsupervised video segmentation mode, the network is trained on a collection of unlabelled videos, using the learning process itself as an algorithm to segment these videos.

Optical Flow Estimation Semantic Segmentation +3

End-to-End Visual Editing with a Generatively Pre-Trained Artist

no code implementations3 May 2022 Andrew Brown, Cheng-Yang Fu, Omkar Parkhi, Tamara L. Berg, Andrea Vedaldi

We consider the targeted image editing problem: blending a region in a source image with a driver image that specifies the desired change.

BANMo: Building Animatable 3D Neural Models from Many Casual Videos

1 code implementation23 Dec 2021 Gengshan Yang, Minh Vo, Natalia Neverova, Deva Ramanan, Andrea Vedaldi, Hanbyul Joo

Our key insight is to merge three schools of thought; (1) classic deformable shape models that make use of articulated bones and blend skinning, (2) volumetric neural radiance fields (NeRFs) that are amenable to gradient-based optimization, and (3) canonical embeddings that generate correspondences between pixels and an articulated model.

3D Shape Reconstruction 3D Shape Reconstruction from Videos

Audio-Visual Synchronisation in the wild

no code implementations8 Dec 2021 Honglie Chen, Weidi Xie, Triantafyllos Afouras, Arsha Nagrani, Andrea Vedaldi, Andrew Zisserman

Finally, we set the first benchmark for general audio-visual synchronisation with over 160 diverse classes in the new VGG-Sound Sync video dataset.

Lip Reading

Unsupervised Part Discovery from Contrastive Reconstruction

1 code implementation NeurIPS 2021 Subhabrata Choudhury, Iro Laina, Christian Rupprecht, Andrea Vedaldi

First, we construct a proxy task through a set of objectives that encourages the model to learn a meaningful decomposition of the image into its parts.

Representation Learning

The Curious Layperson: Fine-Grained Image Recognition without Expert Labels

1 code implementation5 Nov 2021 Subhabrata Choudhury, Iro Laina, Christian Rupprecht, Andrea Vedaldi

We then train a fine-grained textual similarity model that matches image descriptions with documents on a sentence-level basis.

Cross-Modal Retrieval Fine-Grained Image Recognition

NeuralDiff: Segmenting 3D objects that move in egocentric videos

no code implementations19 Oct 2021 Vadim Tschernezki, Diane Larlus, Andrea Vedaldi

Given a raw video sequence taken from a freely-moving camera, we study the problem of decomposing the observed 3D scene into a static background and a dynamic foreground containing the objects that move in the video sequence.

Neural Rendering Semantic Segmentation

Open-Set Recognition: a Good Closed-Set Classifier is All You Need?

1 code implementation ICLR 2022 Sagar Vaze, Kai Han, Andrea Vedaldi, Andrew Zisserman

In this paper, we first demonstrate that the ability of a classifier to make the 'none-of-above' decision is highly correlated with its accuracy on the closed-set classes.

Open Set Learning Out-of-Distribution Detection

PASS: An ImageNet replacement for self-supervised pretraining without humans

1 code implementation NeurIPS Workshop ImageNet_PPF 2021 Yuki M. Asano, Christian Rupprecht, Andrew Zisserman, Andrea Vedaldi

On the other hand, state-of-the-art pretraining is nowadays obtained with unsupervised methods, meaning that labelled datasets such as ImageNet may not be necessary, or perhaps not even optimal, for model pretraining.

Pose Estimation Transfer Learning

Lifting 2D Object Locations to 3D by Discounting LiDAR Outliers across Objects and Views

1 code implementation16 Sep 2021 Robert McCraith, Eldar Insafutdinov, Lukas Neumann, Andrea Vedaldi

We present a system for automatic converting of 2D mask object predictions and raw LiDAR point clouds into full 3D bounding boxes of objects.

DensePose 3D: Lifting Canonical Surface Maps of Articulated Objects to the Third Dimension

no code implementations ICCV 2021 Roman Shapovalov, David Novotny, Benjamin Graham, Patrick Labatut, Andrea Vedaldi

The method learns, in an end-to-end fashion, a soft partition of a given category-specific 3D template mesh into rigid parts together with a monocular reconstruction network that predicts the part motions such that they reproject correctly onto 2D DensePose-like surface annotations of the object.

3D Reconstruction

Augmenting Implicit Neural Shape Representations with Explicit Deformation Fields

no code implementations19 Aug 2021 Matan Atzmon, David Novotny, Andrea Vedaldi, Yaron Lipman

Implicit neural representation is a recent approach to learn shape collections as zero level-sets of neural networks, where each shape is represented by a latent code.

DOVE: Learning Deformable 3D Objects by Watching Videos

no code implementations22 Jul 2021 Shangzhe Wu, Tomas Jakab, Christian Rupprecht, Andrea Vedaldi

Learning deformable 3D objects from 2D images is an extremely ill-posed problem.

AutoNovel: Automatically Discovering and Learning Novel Visual Categories

no code implementations29 Jun 2021 Kai Han, Sylvestre-Alvise Rebuffi, Sébastien Ehrhardt, Andrea Vedaldi, Andrew Zisserman

We present a new approach called AutoNovel to address this problem by combining three ideas: (1) we suggest that the common approach of bootstrapping an image representation using the labelled 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 ranking 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.

Image Clustering Self-Supervised Learning

Pedestrian and Ego-Vehicle Trajectory Prediction From Monocular Camera

no code implementations CVPR 2021 Lukas Neumann, Andrea Vedaldi

Predicting future pedestrian trajectory is a crucial component of autonomous driving systems, as recognizing critical situations based only on current pedestrian position may come too late for any meaningful corrective action (e. g. breaking) to take place.

Autonomous Driving Pedestrian Trajectory Prediction +2

NeuroMorph: Unsupervised Shape Interpolation and Correspondence in One Go

no code implementations CVPR 2021 Marvin Eisenberger, David Novotny, Gael Kerchenbaum, Patrick Labatut, Natalia Neverova, Daniel Cremers, Andrea Vedaldi

We present NeuroMorph, a new neural network architecture that takes as input two 3D shapes and produces in one go, i. e. in a single feed forward pass, a smooth interpolation and point-to-point correspondences between them.

Test Sample Accuracy Scales with Training Sample Density in Neural Networks

1 code implementation15 Jun 2021 Xu Ji, Razvan Pascanu, Devon Hjelm, Balaji Lakshminarayanan, Andrea Vedaldi

Intuitively, one would expect the accuracy of a trained neural network's prediction on a test sample to correlate with how densely that sample is surrounded by seen training samples in representation space.

Image Classification

Moving SLAM: Fully Unsupervised Deep Learning in Non-Rigid Scenes

no code implementations5 May 2021 Dan Xu, Andrea Vedaldi, Joao F. Henriques

We build on the idea of view synthesis, which uses classical camera geometry to re-render a source image from a different point-of-view, specified by a predicted relative pose and depth map.

Depth Estimation Semantic Segmentation

Unsupervised Learning of 3D Object Categories from Videos in the Wild

no code implementations CVPR 2021 Philipp Henzler, Jeremy Reizenstein, Patrick Labatut, Roman Shapovalov, Tobias Ritschel, Andrea Vedaldi, David Novotny

Our goal is to learn a deep network that, given a small number of images of an object of a given category, reconstructs it in 3D.

Space-Time Crop & Attend: Improving Cross-modal Video Representation Learning

1 code implementation ICCV 2021 Mandela Patrick, Yuki M. Asano, Bernie Huang, Ishan Misra, Florian Metze, Joao Henriques, Andrea Vedaldi

First, for space, we show that spatial augmentations such as cropping do work well for videos too, but that previous implementations, due to the high processing and memory cost, could not do this at a scale sufficient for it to work well.

Representation Learning Self-Supervised Learning

Continuous Surface Embeddings

1 code implementation NeurIPS 2020 Natalia Neverova, David Novotny, Vasil Khalidov, Marc Szafraniec, Patrick Labatut, Andrea Vedaldi

In this work, we focus on the task of learning and representing dense correspondences in deformable object categories.

Pose Estimation

Quantifying Learnability and Describability of Visual Concepts Emerging in Representation Learning

no code implementations NeurIPS 2020 Iro Laina, Ruth C. Fong, Andrea Vedaldi

The increasing impact of black box models, and particularly of unsupervised ones, comes with an increasing interest in tools to understand and interpret them.

Representation Learning

Support-set bottlenecks for video-text representation learning

no code implementations ICLR 2021 Mandela Patrick, Po-Yao Huang, Yuki Asano, Florian Metze, Alexander Hauptmann, João Henriques, Andrea Vedaldi

The dominant paradigm for learning video-text representations -- noise contrastive learning -- increases the similarity of the representations of pairs of samples that are known to be related, such as text and video from the same sample, and pushes away the representations of all other pairs.

Contrastive Learning Representation Learning +2

Multi-modal Self-Supervision from Generalized Data Transformations

no code implementations28 Sep 2020 Mandela Patrick, Yuki Asano, Polina Kuznetsova, Ruth Fong, Joao F. Henriques, Geoffrey Zweig, Andrea Vedaldi

In this paper, we show that, for videos, the answer is more complex, and that better results can be obtained by accounting for the interplay between invariance, distinctiveness, multiple modalities and time.

Audio Classification

Calibrating Self-supervised Monocular Depth Estimation

no code implementations16 Sep 2020 Robert McCraith, Lukas Neumann, Andrea Vedaldi

In the recent years, many methods demonstrated the ability of neural networks to learn depth and pose changes in a sequence of images, using only self-supervision as the training signal.

Monocular Depth Estimation

Canonical 3D Deformer Maps: Unifying parametric and non-parametric methods for dense weakly-supervised category reconstruction

1 code implementation NeurIPS 2020 David Novotny, Roman Shapovalov, Andrea Vedaldi

We propose the Canonical 3D Deformer Map, a new representation of the 3D shape of common object categories that can be learned from a collection of 2D images of independent objects.

3D Reconstruction

Labelling unlabelled videos from scratch with multi-modal self-supervision

1 code implementation NeurIPS 2020 Yuki M. Asano, Mandela Patrick, Christian Rupprecht, Andrea Vedaldi

A large part of the current success of deep learning lies in the effectiveness of data -- more precisely: labelled data.

Automatic Recall Machines: Internal Replay, Continual Learning and the Brain

1 code implementation22 Jun 2020 Xu Ji, Joao Henriques, Tinne Tuytelaars, Andrea Vedaldi

Replay in neural networks involves training on sequential data with memorized samples, which counteracts forgetting of previous behavior caused by non-stationarity.

Continual Learning

VGGSound: A Large-scale Audio-Visual Dataset

1 code implementation29 Apr 2020 Honglie Chen, Weidi Xie, Andrea Vedaldi, Andrew Zisserman

Our goal is to collect a large-scale audio-visual dataset with low label noise from videos in the wild using computer vision techniques.

Image Classification

Monocular Depth Estimation with Self-supervised Instance Adaptation

no code implementations13 Apr 2020 Robert McCraith, Lukas Neumann, Andrew Zisserman, Andrea Vedaldi

Recent advances in self-supervised learning havedemonstrated that it is possible to learn accurate monoculardepth reconstruction from raw video data, without using any 3Dground truth for supervision.

Monocular Depth Estimation Self-Supervised Learning

Exemplar Fine-Tuning for 3D Human Model Fitting Towards In-the-Wild 3D Human Pose Estimation

1 code implementation7 Apr 2020 Hanbyul Joo, Natalia Neverova, Andrea Vedaldi

Remarkably, the resulting annotations are sufficient to train from scratch 3D pose regressor networks that outperform the current state-of-the-art on in-the-wild benchmarks such as 3DPW.

3D Human Pose Estimation 3D Pose Estimation

There and Back Again: Revisiting Backpropagation Saliency Methods

1 code implementation CVPR 2020 Sylvestre-Alvise Rebuffi, Ruth Fong, Xu Ji, Andrea Vedaldi

Saliency methods seek to explain the predictions of a model by producing an importance map across each input sample.

Meta-Learning

Goal-Conditioned End-to-End Visuomotor Control for Versatile Skill Primitives

1 code implementation19 Mar 2020 Oliver Groth, Chia-Man Hung, Andrea Vedaldi, Ingmar Posner

Visuomotor control (VMC) is an effective means of achieving basic manipulation tasks such as pushing or pick-and-place from raw images.

Imitation Learning Meta-Learning

Fixing the train-test resolution discrepancy: FixEfficientNet

1 code implementation18 Mar 2020 Hugo Touvron, Andrea Vedaldi, Matthijs Douze, Hervé Jégou

An EfficientNet-L2 pre-trained with weak supervision on 300M unlabeled images and further optimized with FixRes achieves 88. 5% top-1 accuracy (top-5: 98. 7%), which establishes the new state of the art for ImageNet with a single crop.

Ranked #7 on Image Classification on ImageNet ReaL (using extra training data)

Data Augmentation Image Classification

Transferring Dense Pose to Proximal Animal Classes

1 code implementation CVPR 2020 Artsiom Sanakoyeu, Vasil Khalidov, Maureen S. McCarthy, Andrea Vedaldi, Natalia Neverova

Recent contributions have demonstrated that it is possible to recognize the pose of humans densely and accurately given a large dataset of poses annotated in detail.

Transfer Learning

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

Correlated Uncertainty for Learning Dense Correspondences from Noisy Labels

no code implementations NeurIPS 2019 Natalia Neverova, David Novotny, Andrea Vedaldi

We show that these models, by understanding uncertainty better, can solve the original DensePose task more accurately, thus setting the new state-of-the-art accuracy in this benchmark.

Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild

1 code implementation CVPR 2020 Shangzhe Wu, Christian Rupprecht, Andrea Vedaldi

We propose a method to learn 3D deformable object categories from raw single-view images, without external supervision.

Self-labelling via simultaneous clustering and representation learning

4 code implementations ICLR 2020 Yuki Markus Asano, Christian Rupprecht, Andrea Vedaldi

Combining clustering and representation learning is one of the most promising approaches for unsupervised learning of deep neural networks.

Image Clustering Representation Learning +2

Occlusions for Effective Data Augmentation in Image Classification

no code implementations23 Oct 2019 Ruth Fong, Andrea Vedaldi

Deep networks for visual recognition are known to leverage "easy to recognise" portions of objects such as faces and distinctive texture patterns.

Classification Data Augmentation +2

NormGrad: Finding the Pixels that Matter for Training

no code implementations19 Oct 2019 Sylvestre-Alvise Rebuffi, Ruth Fong, Xu Ji, Hakan Bilen, Andrea Vedaldi

In this paper, we are rather interested by the locations of an image that contribute to the model's training.

Meta-Learning

Understanding Deep Networks via Extremal Perturbations and Smooth Masks

1 code implementation ICCV 2019 Ruth Fong, Mandela Patrick, Andrea Vedaldi

In this paper, we discuss some of the shortcomings of existing approaches to perturbation analysis and address them by introducing the concept of extremal perturbations, which are theoretically grounded and interpretable.

Interpretable Machine Learning

C3DPO: Canonical 3D Pose Networks for Non-Rigid Structure From Motion

2 code implementations ICCV 2019 David Novotny, Nikhila Ravi, Benjamin Graham, Natalia Neverova, Andrea Vedaldi

We propose C3DPO, a method for extracting 3D models of deformable objects from 2D keypoint annotations in unconstrained images.

Unsupervised Learning of Landmarks by Descriptor Vector Exchange

1 code implementation ICCV 2019 James Thewlis, Samuel Albanie, Hakan Bilen, Andrea Vedaldi

Equivariance to random image transformations is an effective method to learn landmarks of object categories, such as the eyes and the nose in faces, without manual supervision.

Unsupervised Facial Landmark Detection

AutoCorrect: Deep Inductive Alignment of Noisy Geometric Annotations

no code implementations14 Aug 2019 Honglie Chen, Weidi Xie, Andrea Vedaldi, Andrew Zisserman

We propose AutoCorrect, a method to automatically learn object-annotation alignments from a dataset with annotations affected by geometric noise.

Self-supervised Learning of Interpretable Keypoints from Unlabelled Videos

no code implementations CVPR 2020 Tomas Jakab, Ankush Gupta, Hakan Bilen, Andrea Vedaldi

We propose KeypointGAN, a new method for recognizing the pose of objects from a single image that for learning uses only unlabelled videos and a weak empirical prior on the object poses.

Facial Landmark Detection Image-to-Image Translation +4

Fixing the train-test resolution discrepancy

2 code implementations NeurIPS 2019 Hugo Touvron, Andrea Vedaldi, Matthijs Douze, Hervé Jégou

Conversely, when training a ResNeXt-101 32x48d pre-trained in weakly-supervised fashion on 940 million public images at resolution 224x224 and further optimizing for test resolution 320x320, we obtain a test top-1 accuracy of 86. 4% (top-5: 98. 0%) (single-crop).

Ranked #2 on Fine-Grained Image Classification on Birdsnap (using extra training data)

Data Augmentation Fine-Grained Image Classification +1

Photo-Geometric Autoencoding to Learn 3D Objects from Unlabelled Images

no code implementations4 Jun 2019 Shangzhe Wu, Christian Rupprecht, Andrea Vedaldi

Specifically, given a single image of the object seen from an arbitrary viewpoint, our model predicts a symmetric canonical view, the corresponding 3D shape and a viewpoint transformation, and trains with the goal of reconstructing the input view, resembling an auto-encoder.

Unsupervised Intuitive Physics from Past Experiences

no code implementations26 May 2019 Sébastien Ehrhardt, Aron Monszpart, Niloy J. Mitra, Andrea Vedaldi

We are interested in learning models of intuitive physics similar to the ones that animals use for navigation, manipulation and planning.

Meta-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

Guiding Physical Intuition with Neural Stethoscopes

no code implementations ICLR 2019 Fabian Fuchs, Oliver Groth, Adam Kosiorek, Alex Bewley, Markus Wulfmeier, Andrea Vedaldi, Ingmar Posner

Using an adversarial stethoscope, the network is successfully de-biased, leading to a performance increase from 66% to 88%.

Modelling and unsupervised learning of symmetric deformable object categories

no code implementations NeurIPS 2018 James Thewlis, Hakan Bilen, Andrea Vedaldi

We propose a new approach to model and learn, without manual supervision, the symmetries of natural objects, such as faces or flowers, given only images as input.

Frame

Gather-Excite: Exploiting Feature Context in Convolutional Neural Networks

5 code implementations NeurIPS 2018 Jie Hu, Li Shen, Samuel Albanie, Gang Sun, Andrea Vedaldi

We also propose a parametric gather-excite operator pair which yields further performance gains, relate it to the recently-introduced Squeeze-and-Excitation Networks, and analyse the effects of these changes to the CNN feature activation statistics.

Learning to Read by Spelling: Towards Unsupervised Text Recognition

no code implementations23 Sep 2018 Ankush Gupta, Andrea Vedaldi, Andrew Zisserman

This work presents a method for visual text recognition without using any paired supervisory data.

Supervising the new with the old: learning SFM from SFM

no code implementations ECCV 2018 Maria Klodt, Andrea Vedaldi

First, since such self-supervised approaches are based on the brightness constancy assumption, which is valid only for a subset of pixels, we propose a probabilistic learning formulation where the network predicts distributions over variables rather than specific values.

Motion Estimation

Emotion Recognition in Speech using Cross-Modal Transfer in the Wild

no code implementations16 Aug 2018 Samuel Albanie, Arsha Nagrani, Andrea Vedaldi, Andrew Zisserman

We make the following contributions: (i) we develop a strong teacher network for facial emotion recognition that achieves the state of the art on a standard benchmark; (ii) we use the teacher to train a student, tabula rasa, to learn representations (embeddings) for speech emotion recognition without access to labelled audio data; and (iii) we show that the speech emotion embedding can be used for speech emotion recognition on external benchmark datasets.

Ranked #3 on Facial Expression Recognition on FERPlus (using extra training data)

Facial Emotion Recognition Facial Expression Recognition +1

Inductive Visual Localisation: Factorised Training for Superior Generalisation

no code implementations21 Jul 2018 Ankush Gupta, Andrea Vedaldi, Andrew Zisserman

End-to-end trained Recurrent Neural Networks (RNNs) have been successfully applied to numerous problems that require processing sequences, such as image captioning, machine translation, and text recognition.

Image Captioning Machine Translation +1

Large scale evaluation of local image feature detectors on homography datasets

1 code implementation20 Jul 2018 Karel Lenc, Andrea Vedaldi

The new protocol is better for assessment on a large number of images and reduces the dependency of the results on unwanted distractors such as the number of detected features and the feature magnification factor.

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

Cross Pixel Optical Flow Similarity for Self-Supervised Learning

no code implementations15 Jul 2018 Aravindh Mahendran, James Thewlis, Andrea Vedaldi

We propose a novel method for learning convolutional neural image representations without manual supervision.

Image Classification Optical Flow Estimation +2

Scrutinizing and De-Biasing Intuitive Physics with Neural Stethoscopes

no code implementations14 Jun 2018 Fabian B. Fuchs, Oliver Groth, Adam R. Kosiorek, Alex Bewley, Markus Wulfmeier, Andrea Vedaldi, Ingmar Posner

Conversely, training on an easy dataset where visual cues are positively correlated with stability, the baseline model learns a bias leading to poor performance on a harder dataset.

MapNet: An Allocentric Spatial Memory for Mapping Environments

no code implementations CVPR 2018 João F. Henriques, Andrea Vedaldi

The module contains an allocentric spatial memory that can be accessed associatively by feeding to it the current sensory input, resulting in localization, and then updated using an LSTM or similar mechanism.

Frame

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.

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.

Few-Shot Learning

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.

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.

Net2Vec: Quantifying and Explaining how Concepts are Encoded by Filters in Deep Neural Networks

1 code implementation CVPR 2018 Ruth Fong, Andrea Vedaldi

By studying such embeddings, we are able to show that 1., in most cases, multiple filters are required to code for a concept, that 2., often filters are not concept specific and help encode multiple concepts, and that 3., compared to single filter activations, filter embeddings are able to better characterize the meaning of a representation and its relationship to other concepts.

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

Deep Image Prior

11 code implementations CVPR 2018 Dmitry Ulyanov, Andrea Vedaldi, Victor Lempitsky

In this paper, we show that, on the contrary, the structure of a generator network is sufficient to capture a great deal of low-level image statistics prior to any learning.

Image Denoising Image Inpainting +3

DeepRadiologyNet: Radiologist Level Pathology Detection in CT Head Images

no code implementations26 Nov 2017 Jameson Merkow, Robert Lufkin, Kim Nguyen, Stefano Soatto, Zhuowen Tu, Andrea Vedaldi

Thus, DeepRadiologyNet enables significant reduction in the workload of human radiologists by automatically filtering studies and reporting on the high-confidence ones at an operating point well below the literal error rate for US Board Certified radiologists, estimated at 0. 82%.

Unsupervised learning of object frames by dense equivariant image labelling

no code implementations NeurIPS 2017 James Thewlis, Hakan Bilen, Andrea Vedaldi

One of the key challenges of visual perception is to extract abstract models of 3D objects and object categories from visual measurements, which are affected by complex nuisance factors such as viewpoint, occlusion, motion, and deformations.

Frame Optical Flow Estimation +1

Learning to Represent Mechanics via Long-term Extrapolation and Interpolation

no code implementations6 Jun 2017 Sébastien Ehrhardt, Aron Monszpart, Andrea Vedaldi, Niloy Mitra

While the basic laws of Newtonian mechanics are well understood, explaining a physical scenario still requires manually modeling the problem with suitable equations and associated parameters.

Learning 3D Object Categories by Looking Around Them

no code implementations ICCV 2017 David Novotny, Diane Larlus, Andrea Vedaldi

Traditional approaches for learning 3D object categories use either synthetic data or manual supervision.

Data Augmentation

AnchorNet: A Weakly Supervised Network to Learn Geometry-sensitive Features For Semantic Matching

no code implementations CVPR 2017 David Novotny, Diane Larlus, Andrea Vedaldi

Despite significant progress of deep learning in recent years, state-of-the-art semantic matching methods still rely on legacy features such as SIFT or HoG.

Interpretable Explanations of Black Boxes by Meaningful Perturbation

6 code implementations ICCV 2017 Ruth Fong, Andrea Vedaldi

As machine learning algorithms are increasingly applied to high impact yet high risk tasks, such as medical diagnosis or autonomous driving, it is critical that researchers can explain how such algorithms arrived at their predictions.

Interpretable Machine Learning

It Takes (Only) Two: Adversarial Generator-Encoder Networks

1 code implementation7 Apr 2017 Dmitry Ulyanov, Andrea Vedaldi, Victor Lempitsky

Unlike previous hybrids of autoencoders and adversarial networks, the adversarial game in our approach is set up directly between the encoder and the generator, and no external mappings are trained in the process of learning.

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.

Universal representations:The missing link between faces, text, planktons, and cat breeds

no code implementations25 Jan 2017 Hakan Bilen, Andrea Vedaldi

With the advent of large labelled datasets and high-capacity models, the performance of machine vision systems has been improving rapidly.

Continual Learning

Improved Texture Networks: Maximizing Quality and Diversity in Feed-forward Stylization and Texture Synthesis

1 code implementation CVPR 2017 Dmitry Ulyanov, Andrea Vedaldi, Victor Lempitsky

The recent work of Gatys et al., who characterized the style of an image by the statistics of convolutional neural network filters, ignited a renewed interest in the texture generation and image stylization problems.

Image Generation Image Stylization +1

Action Recognition with Dynamic Image Networks

3 code implementations2 Dec 2016 Hakan Bilen, Basura Fernando, Efstratios Gavves, Andrea Vedaldi

This is a powerful idea because it allows to convert any video to an image so that existing CNN models pre-trained for the analysis of still images can be immediately extended to videos.

Action Recognition Optical Flow Estimation

Learning Grimaces by Watching TV

no code implementations7 Oct 2016 Samuel Albanie, Andrea Vedaldi

As a starting point, we consider the problem of relating facial expressions to objectively measurable events occurring in videos.

Emotion Recognition Face Verification +1

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.

Translation

Fully-Trainable Deep Matching

1 code implementation12 Sep 2016 James Thewlis, Shuai Zheng, Philip H. S. Torr, Andrea Vedaldi

Deep Matching (DM) is a popular high-quality method for quasi-dense image matching.

Semantic Segmentation

Learning the semantic structure of objects from Web supervision

no code implementations5 Jul 2016 David Novotny, Diane Larlus, Andrea Vedaldi

While recent research in image understanding has often focused on recognizing more types of objects, understanding more about the objects is just as important.

Fully-Convolutional Siamese Networks for Object Tracking

6 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.

Frame Object Detection +1

Integrated perception with recurrent multi-task neural networks

no code implementations NeurIPS 2016 Hakan Bilen, Andrea Vedaldi

Modern discriminative predictors have been shown to match natural intelligences in specific perceptual tasks in image classification, object and part detection, boundary extraction, etc.

Image Classification

Dynamic Image Networks for Action Recognition

1 code implementation CVPR 2016 Hakan Bilen, Basura Fernando, Efstratios Gavves, Andrea Vedaldi, Stephen Gould

We introduce the concept of dynamic image, a novel compact representation of videos useful for video analysis especially when convolutional neural networks (CNNs) are used.

Action Recognition

Learning Covariant Feature Detectors

1 code implementation4 May 2016 Karel Lenc, Andrea Vedaldi

We support these ideas theoretically, proposing a novel analysis of local features in term of geometric transformations, and we show that all common and many uncommon detectors can be derived in this framework.

Translation

Texture Networks: Feed-forward Synthesis of Textures and Stylized Images

11 code implementations10 Mar 2016 Dmitry Ulyanov, Vadim Lebedev, Andrea Vedaldi, Victor Lempitsky

Gatys et al. recently demonstrated that deep networks can generate beautiful textures and stylized images from a single texture example.

Style Transfer

Visualizing Deep Convolutional Neural Networks Using Natural Pre-Images

no code implementations7 Dec 2015 Aravindh Mahendran, Andrea Vedaldi

Image representations, from SIFT and bag of visual words to Convolutional Neural Networks (CNNs) are a crucial component of almost all computer vision systems.

Weakly Supervised Deep Detection Networks

2 code implementations CVPR 2016 Hakan Bilen, Andrea Vedaldi

Weakly supervised learning of object detection is an important problem in image understanding that still does not have a satisfactory solution.

Classification Data Augmentation +2

Deep filter banks for texture recognition, description, and segmentation

no code implementations9 Jul 2015 Mircea Cimpoi, Subhransu Maji, Iasonas Kokkinos, Andrea Vedaldi

Visual textures have played a key role in image understanding because they convey important semantics of images, and because texture representations that pool local image descriptors in an orderless manner have had a tremendous impact in diverse applications.

R-CNN minus R

no code implementations23 Jun 2015 Karel Lenc, Andrea Vedaldi

In object detection, methods such as R-CNN have obtained excellent results by integrating CNNs with region proposal generation algorithms such as selective search.

Object Detection Region Proposal

Deep Filter Banks for Texture Recognition and Segmentation

1 code implementation CVPR 2015 Mircea Cimpoi, Subhransu Maji, Andrea Vedaldi

Research in texture recognition often concentrates on the problem of material recognition in uncluttered conditions, an assumption rarely met by applications.

Material Recognition Scene Recognition

Understanding the Fisher Vector: a multimodal part model

no code implementations18 Apr 2015 David Novotný, Diane Larlus, Florent Perronnin, Andrea Vedaldi

Fisher Vectors and related orderless visual statistics have demonstrated excellent performance in object detection, sometimes superior to established approaches such as the Deformable Part Models.

Object Detection

Automatic Discovery and Optimization of Parts for Image Classification

no code implementations20 Dec 2014 Sobhan Naderi Parizi, Andrea Vedaldi, Andrew Zisserman, Pedro Felzenszwalb

First, a collection of informative parts is discovered, using heuristics that promote part distinctiveness and diversity, and then classifiers are trained on the vector of part responses.

Classification General Classification +2

Deep Structured Output Learning for Unconstrained Text Recognition

no code implementations18 Dec 2014 Max Jaderberg, Karen Simonyan, Andrea Vedaldi, Andrew Zisserman

We develop a representation suitable for the unconstrained recognition of words in natural images: the general case of no fixed lexicon and unknown length.

Language Modelling Multi-Task Learning

MatConvNet - Convolutional Neural Networks for MATLAB

no code implementations15 Dec 2014 Andrea Vedaldi, Karel Lenc

MatConvNet is an implementation of Convolutional Neural Networks (CNNs) for MATLAB.

Reading Text in the Wild with Convolutional Neural Networks

no code implementations4 Dec 2014 Max Jaderberg, Karen Simonyan, Andrea Vedaldi, Andrew Zisserman

In this work we present an end-to-end system for text spotting -- localising and recognising text in natural scene images -- and text based image retrieval.

Image Retrieval Region Proposal +2

Understanding Deep Image Representations by Inverting Them

7 code implementations CVPR 2015 Aravindh Mahendran, Andrea Vedaldi

Image representations, from SIFT and Bag of Visual Words to Convolutional Neural Networks (CNNs), are a crucial component of almost any image understanding system.

Deep convolutional filter banks for texture recognition and segmentation

no code implementations25 Nov 2014 Mircea Cimpoi, Subhransu Maji, Andrea Vedaldi

Research in texture recognition often concentrates on the problem of material recognition in uncluttered conditions, an assumption rarely met by applications.

Material Recognition Scene Recognition

Understanding image representations by measuring their equivariance and equivalence

no code implementations CVPR 2015 Karel Lenc, Andrea Vedaldi

Despite the importance of image representations such as histograms of oriented gradients and deep Convolutional Neural Networks (CNN), our theoretical understanding of them remains limited.

Understanding Objects in Detail with Fine-Grained Attributes

no code implementations CVPR 2014 Andrea Vedaldi, Siddharth Mahendran, Stavros Tsogkas, Subhransu Maji, Ross Girshick, Juho Kannala, Esa Rahtu, Iasonas Kokkinos, Matthew B. Blaschko, David Weiss, Ben Taskar, Karen Simonyan, Naomi Saphra, Sammy Mohamed

We show that the collected data can be used to study the relation between part detection and attribute prediction by diagnosing the performance of classifiers that pool information from different parts of an object.

Object Detection

A Compact and Discriminative Face Track Descriptor

no code implementations CVPR 2014 Omkar M. Parkhi, Karen Simonyan, Andrea Vedaldi, Andrew Zisserman

Our goal is to learn a compact, discriminative vector representation of a face track, suitable for the face recognition tasks of verification and classification.

Binarization Dimensionality Reduction +4

Speeding up Convolutional Neural Networks with Low Rank Expansions

no code implementations15 May 2014 Max Jaderberg, Andrea Vedaldi, Andrew Zisserman

The focus of this paper is speeding up the evaluation of convolutional neural networks.

Model Compression

Return of the Devil in the Details: Delving Deep into Convolutional Nets

no code implementations14 May 2014 Ken Chatfield, Karen Simonyan, Andrea Vedaldi, Andrew Zisserman

In particular, we show that the data augmentation techniques commonly applied to CNN-based methods can also be applied to shallow methods, and result in an analogous performance boost.

Data Augmentation Object Detection

Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

19 code implementations20 Dec 2013 Karen Simonyan, Andrea Vedaldi, Andrew Zisserman

This paper addresses the visualisation of image classification models, learnt using deep Convolutional Networks (ConvNets).

Classification General Classification +2

Deep Fisher Networks for Large-Scale Image Classification

no code implementations NeurIPS 2013 Karen Simonyan, Andrea Vedaldi, Andrew Zisserman

As massively parallel computations have become broadly available with modern GPUs, deep architectures trained on very large datasets have risen in popularity.

Classification General Classification +1

Describing Textures in the Wild

4 code implementations CVPR 2014 Mircea Cimpoi, Subhransu Maji, Iasonas Kokkinos, Sammy Mohamed, Andrea Vedaldi

Patterns and textures are defining characteristics of many natural objects: a shirt can be striped, the wings of a butterfly can be veined, and the skin of an animal can be scaly.

Material Recognition Object Recognition

Fine-Grained Visual Classification of Aircraft

1 code implementation21 Jun 2013 Subhransu Maji, Esa Rahtu, Juho Kannala, Matthew Blaschko, Andrea Vedaldi

This paper introduces FGVC-Aircraft, a new dataset containing 10, 000 images of aircraft spanning 100 aircraft models, organised in a three-level hierarchy.

Classification Fine-Grained Image Classification +1

Blocks That Shout: Distinctive Parts for Scene Classification

no code implementations CVPR 2013 Mayank Juneja, Andrea Vedaldi, C. V. Jawahar, Andrew Zisserman

The automatic discovery of distinctive parts for an object or scene class is challenging since it requires simultaneously to learn the part appearance and also to identify the part occurrences in images.

Classification General Classification +1

Pylon Model for Semantic Segmentation

no code implementations NeurIPS 2011 Victor Lempitsky, Andrea Vedaldi, Andrew Zisserman

Often, the random field is applied over a flat partitioning of the image into non-intersecting elements, such as pixels or super-pixels.

Semantic Segmentation

Simultaneous Object Detection and Ranking with Weak Supervision

no code implementations NeurIPS 2010 Matthew Blaschko, Andrea Vedaldi, Andrew Zisserman

A standard approach to learning object category detectors is to provide strong supervision in the form of a region of interest (ROI) specifying each instance of the object in the training images.

Object Detection Pedestrian Detection

Structured output regression for detection with partial truncation

no code implementations NeurIPS 2009 Andrea Vedaldi, Andrew Zisserman

We develop a structured output model for object category detection that explicitly accounts for alignment, multiple aspects and partial truncation in both training and inference.

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