• Publications
  • Influence
Region filling and object removal by exemplar-based image inpainting
The simultaneous propagation of texture and structure information is achieved by a single, efficient algorithm that combines the advantages of two approaches: exemplar-based texture synthesis and block-based sampling process. Expand
Aggregating local descriptors into a compact image representation
This work proposes a simple yet efficient way of aggregating local image descriptors into a vector of limited dimension, which can be viewed as a simplification of the Fisher kernel representation, and shows how to jointly optimize the dimension reduction and the indexing algorithm. Expand
Aggregating Local Image Descriptors into Compact Codes
This paper first presents and evaluates different ways of aggregating local image descriptors into a vector and shows that the Fisher kernel achieves better performance than the reference bag-of-visual words approach for any given vector dimension. Expand
Poisson image editing
Using generic interpolation machinery based on solving Poisson equations, a variety of novel tools are introduced for seamless editing of image regions. The first set of tools permits the seamlessExpand
Color-Based Probabilistic Tracking
This work introduces a new Monte Carlo tracking technique based on the same principle of color histogram distance, but within a probabilistic framework, and introduces the following ingredients: multi-part color modeling to capture a rough spatial layout ignored by global histograms, incorporation of a background color model when relevant, and extension to multiple objects. Expand
Object removal by exemplar-based inpainting
A best-first algorithm in which the confidence in the synthesized pixel values is propagated in a manner similar to the propagation of information in inpainting, which demonstrates the effectiveness of the algorithm in removing large occluding objects as well as thin scratches. Expand
ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation
This work proposes two novel, complementary methods using (i) entropy loss and (ii) adversarial loss respectively for unsupervised domain adaptation in semantic segmentation with losses based on the entropy of the pixel-wise predictions. Expand
MoFA: Model-Based Deep Convolutional Face Autoencoder for Unsupervised Monocular Reconstruction
A novel model-based deep convolutional autoencoder that addresses the highly challenging problem of reconstructing a 3D human face from a single in-the-wild color image and can be trained end-to-end in an unsupervised manner, which renders training on very large real world data feasible. Expand
Retrieving actions in movies
  • I. Laptev, P. Pérez
  • Computer Science
  • IEEE 11th International Conference on Computer…
  • 26 December 2007
A new annotated human action dataset is introduced and a new "keyframe priming" that combines discriminative models of human motion and shape within an action is shown to significantly improve the performance of action detection. Expand
Maintaining multimodality through mixture tracking
This paper proposes to model the target distribution as a nonparametric mixture model, and presents the general tracking recursion in this case, and shows how a Monte Carlo implementation of the general recursion leads to a mixture of particle filters that interact only in the computation of the mixture weights, leading to an efficient numerical algorithm. Expand