EpicFlow: Edge-Preserving Interpolation of Correspondences for Optical Flow

We propose a novel approach for optical flow estimation , targeted at large displacements with significant oc-clusions. It consists of two steps: i) dense matching by edge-preserving interpolation from a sparse set of matches; ii) variational energy minimization initialized with the dense matches. The sparse-to-dense interpolation relies on an appropriate choice of the distance, namely an edge-aware geodesic distance. This distance is tailored to handle occlusions and motion boundaries -- two common and difficult issues for optical flow computation. We also propose an approximation scheme for the geodesic distance to allow fast computation without loss of performance. Subsequent to the dense interpolation step, standard one-level variational energy minimization is carried out on the dense matches to obtain the final flow estimation. The proposed approach, called Edge-Preserving Interpolation of Correspondences (EpicFlow) is fast and robust to large displacements. It significantly outperforms the state of the art on MPI-Sintel and performs on par on Kitti and Middlebury

DeepMatching: Hierarchical Deformable Dense Matching

We introduce a novel matching algorithm, called DeepMatching, to compute dense correspondences between images. DeepMatching relies on a hierarchical, multi-layer, correlational architecture designed for matching images and was inspired by deep convolutional approaches. The proposed matching algorithm can handle non-rigid deformations and repetitive textures and efficiently determines dense correspondences in the presence of significant changes between images. We evaluate the performance of DeepMatching, in comparison with state-of-the-art matching algorithms, on the Mikolajczyk (Mikolajczyk et al 2005), the MPI-Sintel (Butler et al 2012) and the Kitti (Geiger et al 2013) datasets. DeepMatching outperforms the state-of-the-art algorithms and shows excellent results in particular for repetitive textures.We also propose a method for estimating optical flow, called DeepFlow, by integrating DeepMatching in the large displacement optical flow (LDOF) approach of Brox and Malik (2011). Compared to existing matching algorithms, additional robustness to large displacements and complex motion is obtained thanks to our matching approach. DeepFlow obtains competitive performance on public benchmarks for optical flow estimation

Beat-Event Detection in Action Movie Franchises

While important advances were recently made towards temporally localizing and recognizing specific human actions or activities in videos, efficient detection and classification of long video chunks belonging to semantically defined categories such as "pursuit" or "romance" remains challenging.We introduce a new dataset, Action Movie Franchises, consisting of a collection of Hollywood action movie franchises. We define 11 non-exclusive semantic categories - called beat-categories - that are broad enough to cover most of the movie footage. The corresponding beat-events are annotated as groups of video shots, possibly overlapping.We propose an approach for localizing beat-events based on classifying shots into beat-categories and learning the temporal constraints between shots. We show that temporal constraints significantly improve the classification performance. We set up an evaluation protocol for beat-event localization as well as for shot classification, depending on whether movies from the same franchise are present or not in the training data

Circulant temporal encoding for video retrieval and temporal alignment

We address the problem of specific video event retrieval. Given a query video of a specific event, e.g., a concert of Madonna, the goal is to retrieve other videos of the same event that temporally overlap with the query. Our approach encodes the frame descriptors of a video to jointly represent their appearance and temporal order. It exploits the properties of circulant matrices to efficiently compare the videos in the frequency domain. This offers a significant gain in complexity and accurately localizes the matching parts of videos. The descriptors can be compressed in the frequency domain with a product quantizer adapted to complex numbers. In this case, video retrieval is performed without decompressing the descriptors. We also consider the temporal alignment of a set of videos. We exploit the matching confidence and an estimate of the temporal offset computed for all pairs of videos by our retrieval approach. Our robust algorithm aligns the videos on a global timeline by maximizing the set of temporally consistent matches. The global temporal alignment enables synchronous playback of the videos of a given scene

DeepFlow: Large displacement optical flow with deep matching

International audienceOptical flow computation is a key component in many computer vision systems designed for tasks such as action detection or activity recognition. However, despite several major advances over the last decade, handling large displacement in optical flow remains an open problem. Inspired by the large displacement optical flow of Brox and Malik, our approach, termed DeepFlow, blends a matching algorithm with a variational approach for optical flow. We propose a descriptor matching algorithm, tailored to the optical flow problem, that allows to boost performance on fast motions. The matching algorithm builds upon a multi-stage architecture with 6 layers, interleaving convolutions and max-pooling, a construction akin to deep convolutional nets. Using dense sampling, it allows to efficiently retrieve quasi-dense correspondences, and enjoys a built-in smoothing effect on descriptors matches, a valuable asset for integration into an energy minimization framework for optical flow estimation. DeepFlow efficiently handles large displacements occurring in realistic videos, and shows competitive performance on optical flow benchmarks. Furthermore, it sets a new state-of-the-art on the MPI-Sintel dataset

Selection itérative de transformations pour la classification d'images

National audienceEn classification d'images, une stratégie efficace pour apprendre un classifieur invariant à certaines transformations consiste à augmenter l'échantillon d'apprentissage par le même ensemble d'exemples mais auxquels les transformations ont été appliquées. Néanmoins, lorsque l'ensemble des transformations possibles est grand, il peut s'avérer difficile de sélectionner un petit nombre de transformations pertinentes parmi elles tout en conservant une taille d'échantillon d'apprentissage raisonnable. optimal. En effet, toutes les transformations n'apportent pas le même impact sur la performance ; certains peuvent même dégrader la performance. Nous proposons un algorithme de sélection automatique de transformations : à chaque itération, la transformation qui donne le plus grand gain en performance est sélectionnée. Nous évaluons notre approche sur les images de la compétition ImageNet 2010 et améliorons la performance en top-5 accuracy de 70.1% à 74.9%

SACReg: Scene-Agnostic Coordinate Regression for Visual Localization

Scene coordinates regression (SCR), i.e., predicting 3D coordinates for every pixel of a given image, has recently shown promising potential. However, existing methods remain mostly scene-specific or limited to small scenes and thus hardly scale to realistic datasets. In this paper, we propose a new paradigm where a single generic SCR model is trained once to be then deployed to new test scenes, regardless of their scale and without further finetuning. For a given query image, it collects inputs from off-the-shelf image retrieval techniques and Structure-from-Motion databases: a list of relevant database images with sparse pointwise 2D-3D annotations. The model is based on the transformer architecture and can take a variable number of images and sparse 2D-3D annotations as input. It is trained on a few diverse datasets and significantly outperforms other scene regression approaches on several benchmarks, including scene-specific models, for visual localization. In particular, we set a new state of the art on the Cambridge localization benchmark, even outperforming feature-matching-based approaches

Selection itérative de transformations pour la classification d'images

National audienceEn classification d'images, une stratégie efficace pour apprendre un classifieur invariant à certaines transformations consiste à augmenter l'échantillon d'apprentissage par le même ensemble d'exemples mais auxquels les transformations ont été appliquées. Néanmoins, lorsque l'ensemble des transformations possibles est grand, il peut s'avérer difficile de sélectionner un petit nombre de transformations pertinentes parmi elles tout en conservant une taille d'échantillon d'apprentissage raisonnable. optimal. En effet, toutes les transformations n'apportent pas le même impact sur la performance ; certains peuvent même dégrader la performance. Nous proposons un algorithme de sélection automatique de transformations : à chaque itération, la transformation qui donne le plus grand gain en performance est sélectionnée. Nous évaluons notre approche sur les images de la compétition ImageNet 2010 et améliorons la performance en top-5 accuracy de 70.1% à 74.9%

La pédagogie freinet, adaptation en classe de fle

Ce mémoire tend à apporter des pistes d’analyse sur les nouvelles pédagogies en cours de FLE et plus précisément sur la pédagogie Freinet. Ce mémoire retrace les débuts d’une recherche en pédagogie Freinet jusqu’à son aboutissement. Il permet de voir l’évolution d’un travail de recherche sur une année. Cet écrit est centré sur l’enseignement du FLE en pédagogie Freinet dans l’enseignement secondaire. A travers un ensemble de multiples données, ce mémoire cherche à comprendre ce qui différencie un cours de FLE classique d’un cours en pédagogie Freinet. On passe pour cela de la vie de Célestin Freinet à l’adaptation de sa pédagogie aujourd’hui.Este TFM tende a dar pistas de análisis sobre nuevas pedagogías en clase de FLE y precisamente sobre la pedagogía Freinet. Este TFM describe los principios de una búsqueda en pedagogía Freinet hasta su culminación. Este escrito esta centrada sobre enseñanza del FLE en pedagogía Freinet en la enseñanza secundaria. A través un conjunto de datos multiples, este TFM busca entender lo que diferencia una clase de FLE tradicional de una clase en pedagogía Freinet. Pasamos así de la vida de Célestin Freinet hasta la adaptación de su pedagogía hoy.Departamento de Filología Francesa y AlemanaMáster en Profesor de Educación Secundaria Obligatoria y Bachillerato, Formación Profesional y Enseñanzas de Idioma