Unsupervised learning from narrated instruction videos project page
Abstract We address the problem of automatically learning the main steps to complete a certain task, such as changing a car tire, from a set of narrated instruction videos. The contributions of this paper are three-fold. First, we develop a new unsupervised learning approach that takes advantage of the complementary nature of the input video and the associated narration. The method solves two clus
NetVLAD: CNN architecture for weakly supervised place recognition
NetVLAD: CNN architecture for weakly supervised place recognition Our trained NetVLAD descriptor correctly recognizes the location (b) of the query photograph (a) despite the large amount of clutter (people, cars), changes in viewpoint and completely different illumination (night vs daytime). Abstract We tackle the problem of large scale visual place recognition, where the task is to quickly and a
A Theoretical Analysis of Feature Pooling in Visual Recognition
A Theoretical Analysis of Feature Pooling in Visual Recognition Y-Lan Boureau2,3 YLAN@CS.NYU.EDU Jean Ponce1,2 JEAN.PONCE@ENS.FR Yann LeCun3 YANN@CS.NYU.EDU 1 Laboratoire d’Informatique de l’Ecole Normale Supérieure, 45, rue d’Ulm 75230 Paris CEDEX 05, France 2 INRIA - WILLOW Project (INRIA/ENS/CNRS UMR 8548), 23, avenue d’Italie, 75214 Paris, France 3 Courant Institute of Mathematical Sciences N
Convolutional neural network architecture for geometric matching
Convolutional neural network architecture for geometric matching Our trained geometry estimation network automatically aligns two images with substantial appearance differences. It is able to estimate large deformable transformations robustly in the presence of clutter. The proposed CNN architecture is based on three main components that mimic the standard steps of feature extraction, matching and
title.intro.contents
I This book is currently out of print. Upon kind permission of the M.I.T.-Press, it is available on ftp.ens.fr/pub/dmi/users/longo/CategTypesStructures All references should be made to the published book. CATEGORIES TYPES AND STRUCTURES An Introduction to Category Theory for the working computer scientist Andrea Asperti Giuseppe Longo FOUNDATIONS OF COMPUTING SERIES M.I.T. PRESS, 1991 II INTRODUCT
fbach_tutorial_vr_nips_2016.dvi
Stochastic optimization: Beyond stochastic gradients and convexity Part I Francis Bach INRIA - Ecole Normale Supérieure, Paris, France É C O L E N O R M A L E S U P É R I E U R E Joint tutorial with Suvrit Sra, MIT - NIPS - 2016 Context Machine learning for large-scale data • Large-scale supervised machine learning: large d, large n – d : dimension of each observation (input) or number of paramet
Introduction to the Coq proof assistant
Modelling and verifying algorithms in Coq: an introduction CEA-EDF-INRIA summer school, Paris, 7-11 June 2010. Speakers: Yves Bertot, Pierre Casteran, Pierre Letouzey, Assia Mahboubi. Assistants: Stéphane Glondu, Francesco Zappa Nardelli. The Coq Proof Assistant can be downloaded from here. The files of these lectures have been tested with version 8.2pl1 but should work with any 8.2 (or later) ver
Synchronous Functional Programming: The Lucid Synchrone Experiment pdfauthor
Synchronous Functional Programming: The Lucid Synchrone Experiment ∗ Paul Caspi † VERIMAG Grenoble Grégoire Hamon The MathWorks Boston Marc Pouzet ‡ LRI, Univ. Paris-Sud 11 Orsay 1 Introduction Lucid Synchrone is a programming language dedicated to the design of reactive systems. It is based on the synchronous model of Lustre [25] which it extends with features usually found in functional languag
Painting-to-3D Model Alignment Via Discriminative Visual Elements
Painting-to-3D Model Alignment Via Discriminative Visual Elements This paper describes a technique that can reliably align arbitrary 2D depictions of an architectural site, including drawings, paintings and historical photographs, with a 3D model of the site. This is a tremendously difficult task as the appearance and scene structure in the 2D depictions can be very different from the appearance a
Ivan Laptev > Projects > Human Action Classification > Hollywood2
We provide a dataset with 12 classes of human actions and 10 classes of scenes distributed over 3669 video clips and approximately 20.1 hours of video in total. The dataset intends to provide a comprehensive benchmark for human action recognition in realistic and challenging settings. The dataset is composed of video clips from 69 movies (see the list of movies below). A part of this dataset was o
CMVS
Many multi-view stereo (MVS) algorithms do not scale well to a large number of input images (lack of computational and memory resources). This software (CMVS) takes the output of a structure-from-motion (SfM) software as input, then decomposes the input images into a set of image clusters of managable size. An MVS software can be used to process each cluster independently and in parallel, where th
Abstract Interpretation
Abstract Interpretation Web page maintained by P. Cousot Last update: Aug 5, 2008 Contents 1 Introduction to Abstract Interpretation 2 What can be formalized by abstract interpretation? 2.1 Syntax 2.2 Semantics 2.3 Proofs 2.4 Static analysis 2.5 Data-flow Analysis 2.6 Control-flow Analysis 2.7 Types 2.8 Model-checking 2.9 Predicate abstraction 2.10 Counter-example-based refinement 2.11
On-line materials CVML Summer School, Paris 2013
Visual Recognition and Machine Learning Summer School, Paris, 22-26 July 2013 Materials for practical sessions Instance-level recognition, Ivan Laptev, Cordelia Schmid, Josef Sivic and Andrew Zisserman Monday, July 22, 17.30-19.00 Recognition of Object Categories: Image classification, Ivan Laptev, Cordelia Schmid, Josef Sivic and Andrew Zisserman Wednesday, July 24, 16.00-17.30 Recognition of Obj
Learning with Structured Inputs and Outputs
Learning with Structured Inputs and Outputs Christoph Lampert Institute of Science and Technology Austria Vienna (Klosterneuburg), Austria INRIA Summer School 2010 Learning with Structured Inputs and Outputs Overview... 15:30–16:30 Intro + Conditional Random Fields 16:30–16:40 — mini-break — 16:40–17:30 Structured Support Vector Machines Slides and References: http://www.christoph-lampert.org "Nor
Propositions and Predicates
Propositions and Predicates Propositions and Predicates Pierre Letouzey (slides initially written by Pierre Casteran) Paris, November 2011 Propositions and Predicates In this class, we shall present how Coq’s type system allows us to express properties of programs and/or mathematical objects. We will try to show the great expressive power of this formalism, mostly by examples. Propositions and Pre
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Video Dataset Overview
https://www.di.ens.fr/~fbach/mairal_icml09.pdf
http://www.di.ens.fr/~fbach/ssra_tutorial_vr_nips_2016.pdf
http://www.di.ens.fr/~zappa/teaching/mpri/2006/pisyntax.pdf
Recursive functions in Coq