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HAL is a multidisciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt età la diffusion… (More)

Attributes are an intermediate representation, which enables parameter sharing between classes, a must when training data is scarce. We propose to view attribute-based image classification as a label-embedding problem: each class is embedded in the space of attribute vectors. We introduce a function which measures the compatibility between an image and a… (More)

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… (More)

We consider the minimization of a smooth loss with trace-norm regularization, which is a natural objective in multi-class and multi-task learning. Even though the problem is convex, existing approaches rely on optimizing a non-convex variational bound, which is not guaranteed to converge, or repeatedly perform singular-value decomposition, which prevents… (More)

We propose a family of kernels between images, defined as kernels between their respective segmentation graphs. The kernels are based on soft matching of subtree-patterns of the respective graphs, leveraging the natural structure of images while remaining robust to the associated seg-mentation process uncertainty. Indeed, output from morphological… (More)

We present a novel linear clustering framework (DIFFRAC) which relies on a linear discriminative cost function and a convex relaxation of a combinatorial optimization problem. The large convex optimization problem is solved through a sequence of lower dimensional singular value decompositions. This framework has several attractive properties: (1) although… (More)

Although the structure of simple actions can be captured by rigid grids [3] or by sequences of short temporal parts [2], activities are composed of a variable number of sub-events connected by more complex spatio-temporal relations. In this paper, we learn how to automatically represent activities as a hierarchy of mid-level motion components in order to… (More)

Learning to localize objects with minimal supervision is an important problem in computer vision , since large fully annotated datasets are extremely costly to obtain. In this paper, we propose a new method that achieves this goal with only image-level labels of whether the objects are present or not. Our approach combines a dis-criminative submodular cover… (More)

An important goal in visual recognition is to devise image representations that are invariant to particular transformations. In this paper, we address this goal with a new type of convolutional neural network (CNN) whose invariance is encoded by a reproducing kernel. Unlike traditional approaches where neural networks are learned either to represent data or… (More)

Motivated by some applications in signal processing and machine learning, we consider two convex optimization problems where, given a cone K , a norm · · and a smooth convex function f , we want either (1) to minimize the norm over the intersection of the cone and a level set of f , or (2) to minimize over the cone the sum of f and a multiple of the norm.… (More)