Improving Semantic Embedding Consistency by Metric Learning for Zero-Shot Classification

This paper addresses the task of zero-shot image classification. The key contribution of the proposed approach is to control the semantic embedding of images -- one of the main ingredients of zero-shot learning -- by formulating it as a metric learning problem. The optimized empirical criterion associates two types of sub-task constraints: metric discriminating capacity and accurate attribute prediction. This results in a novel expression of zero-shot learning not requiring the notion of class in the training phase: only pairs of image/attributes, augmented with a consistency indicator, are given as ground truth. At test time, the learned model can predict the consistency of a test image with a given set of attributes , allowing flexible ways to produce recognition inferences. Despite its simplicity, the proposed approach gives state-of-the-art results on four challenging datasets used for zero-shot recognition evaluation.Comment: in ECCV 2016, Oct 2016, amsterdam, Netherlands. 201

Generating Visual Representations for Zero-Shot Classification

This paper addresses the task of learning an image clas-sifier when some categories are defined by semantic descriptions only (e.g. visual attributes) while the others are defined by exemplar images as well. This task is often referred to as the Zero-Shot classification task (ZSC). Most of the previous methods rely on learning a common embedding space allowing to compare visual features of unknown categories with semantic descriptions. This paper argues that these approaches are limited as i) efficient discrimi-native classifiers can't be used ii) classification tasks with seen and unseen categories (Generalized Zero-Shot Classification or GZSC) can't be addressed efficiently. In contrast , this paper suggests to address ZSC and GZSC by i) learning a conditional generator using seen classes ii) generate artificial training examples for the categories without exemplars. ZSC is then turned into a standard supervised learning problem. Experiments with 4 generative models and 5 datasets experimentally validate the approach, giving state-of-the-art results on both ZSC and GZSC

Robust abandoned object detection integrating wide area visual surveillance and social context

This paper presents a video surveillance framework that robustly and efficiently detects abandoned objects in surveillance scenes. The framework is based on a novel threat assessment algorithm which combines the concept of ownership with automatic understanding of social relations in order to infer abandonment of objects. Implementation is achieved through development of a logic-based inference engine based on Prolog. Threat detection performance is conducted by testing against a range of datasets describing realistic situations and demonstrates a reduction in the number of false alarms generated. The proposed system represents the approach employed in the EU SUBITO project (Surveillance of Unattended Baggage and the Identification and Tracking of the Owner)

Vers des systèmes perceptifs autonomes

The objective of this document is to introduce the principle of Autonomous PErceptual System (APES) as an object of study.The functionalities of artificial perception, in particular vision, have become both easier to design and more efficient through the use of a set of techniques and development environments grouped under the term "Deep Learning". They have reached a certain level of maturity making it possible to envisage their use for real or even critical applications.The research direction proposed here is to provide perception with a certain degree of autonomy envisaged as a means of guaranteeing its reliability.The introduction of such a property implies to reconsider the status of perception no longer as passive functionality but as an activity involving as explicit stakeholders the environment to be perceived but also the recipient of the perceptual products with which the system maintains a contractual relationship determining the nature of the expected service and the means to guarantee it.The study of autonomous perceptual systems thus leads to a research program organized along three axes: the design of a perceptual activity articulating functional dynamics and learning processes, the development of an inherent intelligibility of the mechanisms of perception for monitoring, specifying or justifying their behavior, and the implementation of a general approach to guarantee their safe and controlled use.L'objectif de ce mémoire est d'introduire le principe de système perceptif autonome comme objet d'étude.Les fonctionnalités de perception artificielle, en particulier de vision, sont devenues à la fois plus faciles à concevoir et plus performantes par l'utilisation d'un ensemble de techniques et d'environnements de développement regroupés sous l'expression apprentissage profond ("Deep Learning"). Elles ont atteint un certain niveau de maturité permettant d'envisager leur utilisation pour des application réelles voire critiques.La direction de recherche proposée ici est de munir la perception d'un certain degré d'autonomie considéré comme moyen de garantir sa fiabilité.L'introduction d'une telle propriété implique de reconsidérer le statut de la perception non plus comme fonctionnalité passive mais comme une activité impliquant comme parties prenantes explicites l'environnement à percevoir mais également le destinataire des produits perceptifs avec lequel le système entretient une relation contractuelle déterminant la nature du service attendu et les moyens de le garantir.L'étude des systèmes perceptifs autonomes conduit ainsi à un programme de recherche organisé selon trois axes: la conception d'une activité perceptive articulant dynamique fonctionnelle et processus d'apprentissage, le développement d'une intelligibilité propre des mécanismes de perception pour surveiller, spécifier ou justifier leur comportement, et la mise en œuvre d'une démarche générale permettant de garantir leur utilisation sûre et maîtrisée

L’Objet de l’exposition : L’architecture exposée

Cet ouvrage est issu d'un programme de rencontres mené conjointement par l’Ecole Nationale Supérieure d’Art de Bourges et le département d’histoire de l’art de l’Université François-Rabelais (Tours) entre 2012 et 2014.International audienceL’Objet de l’exposition : l’architecture exposée est le premier ouvrage collectif sur l’exposition d’architecture publié en langue française. Il fait suite à un programme de rencontres mené conjointement par l’École Nationale Supérieure d’Art de Bourges et le département d’histoire de l’art de l’Université François-Rabelais (Tours), en partenariat avec le Frac Centre, entre 2012 et 2014.En rassemblant des contributions de chercheurs, d’architectes et de commissaires d’exposition, cet ouvrage propose une lecture ouverte de l’exposition d’architecture. Arpentant un territoire qui s’étend du musée à la ville, en passant par l’installation, la photographie et le cinéma, il explore les différentes problématiques qui caractérisent ce domaine encore largement méconnu, sur une chronologie qui s’étend du tout début du XXe siècle à nos jours

Combining geometric and probabilistic structure for active recognition of 3D objects

Direct perception is incomplete: objects may show ambiguous appearances, and sensors have a limited sensitivity. Consequently, the recognition of complex 3D objects nevessitate an exploratory phase to be able to deal with complex scenes or objects. The variation of object appearance when the viewpoint is modified or when the sensor parameters are changed is an idiosyncratic feature which can be organized in the form of an aspect graph. Standard geometric aspect graphs are difficult to build. This article presents a generalized probabilistic version of this concept. When fitted with a Markov chain dependance, the aspect graph acquires a quantitative predictive power. Tri-dimensional object recognition becomes translated into a problem of Markov chain discrimination. The asymptotic theory of hypothesis testing, in its relation to the theory of large deviations, gives then a global evaluation of the statistical complexity of the recognition problem. Keywords: 3D object recognition, acti..

Explaining object detectors: the case of transformer architectures

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