Deep Shape Matching

We cast shape matching as metric learning with convolutional networks. We break the end-to-end process of image representation into two parts. Firstly, well established efficient methods are chosen to turn the images into edge maps. Secondly, the network is trained with edge maps of landmark images, which are automatically obtained by a structure-from-motion pipeline. The learned representation is evaluated on a range of different tasks, providing improvements on challenging cases of domain generalization, generic sketch-based image retrieval or its fine-grained counterpart. In contrast to other methods that learn a different model per task, object category, or domain, we use the same network throughout all our experiments, achieving state-of-the-art results in multiple benchmarks.Comment: ECCV 201

Landmark-based approaches for goal recognition as planning

This article is a revised and extended version of two papers published at AAAI 2017 (Pereira et al., 2017b) and ECAI 2016 (Pereira and Meneguzzi, 2016). We thank the anonymous reviewers that helped improve the research in this article. The authors thank Shirin Sohrabi for discussing the way in which the algorithms of Sohrabi et al. (2016) should be configured, and Yolanda Escudero-Martın for providing code for the approach of E-Martın et al. (2015) and engaging with us. We also thank Miquel Ramırez and Mor Vered for various discussions, and Andre Grahl Pereira for a discussion of properties of our algorithm. Felipe thanks CNPq for partial financial support under its PQ fellowship, grant number 305969/2016-1.Peer reviewedPostprin

Planning Landmark Based Goal Recognition Revisited: Does Using Initial State Landmarks Make Sense?

Goal recognition is an important problem in many application domains (e.g., pervasive computing, intrusion detection, computer games, etc.). In many application scenarios, it is important that goal recognition algorithms can recognize goals of an observed agent as fast as possible. However, many early approaches in the area of Plan Recognition As Planning, require quite large amounts of computation time to calculate a solution. Mainly to address this issue, recently, Pereira et al. developed an approach that is based on planning landmarks and is much more computationally efficient than previous approaches. However, the approach, as proposed by Pereira et al., also uses trivial landmarks (i.e., facts that are part of the initial state and goal description are landmarks by definition). In this paper, we show that it does not provide any benefit to use landmarks that are part of the initial state in a planning landmark based goal recognition approach. The empirical results show that omitting initial state landmarks for goal recognition improves goal recognition performance.Comment: Will be presented at KI 202

Appearance-Based Gaze Estimation in the Wild

Appearance-based gaze estimation is believed to work well in real-world settings, but existing datasets have been collected under controlled laboratory conditions and methods have been not evaluated across multiple datasets. In this work we study appearance-based gaze estimation in the wild. We present the MPIIGaze dataset that contains 213,659 images we collected from 15 participants during natural everyday laptop use over more than three months. Our dataset is significantly more variable than existing ones with respect to appearance and illumination. We also present a method for in-the-wild appearance-based gaze estimation using multimodal convolutional neural networks that significantly outperforms state-of-the art methods in the most challenging cross-dataset evaluation. We present an extensive evaluation of several state-of-the-art image-based gaze estimation algorithms on three current datasets, including our own. This evaluation provides clear insights and allows us to identify key research challenges of gaze estimation in the wild

Online Goal Recognition in Discrete and Continuous Domains Using a Vectorial Representation

Preprin

Explainable Goal Recognition: A Framework Based on Weight of Evidence

We introduce and evaluate an eXplainable Goal Recognition (XGR) model that uses the Weight of Evidence (WoE) framework to explain goal recognition problems. Our model provides human-centered explanations that answer why? and why not? questions. We computationally evaluate the performance of our system over eight different domains. Using a human behavioral study to obtain the ground truth from human annotators, we further show that the XGR model can successfully generate human-like explanations. We then report on a study with 60 participants who observe agents playing Sokoban game and then receive explanations of the goal recognition output. We investigate participants' understanding obtained by explanations through task prediction, explanation satisfaction, and trust.Comment: 11 pages, 5 figure

L'apprentissage profond, une puissante alternative pour la reconnaissance d'intention

Ce mémoire s'inscrit dans la lignée d'une avancée de connaissances en reconnaissance d'intention, une discipline de recherche en intelligence artificielle visant à inférer les buts poursuivis par un individu à l'aide d'observations de son comportement. Ce problème, du fait de sa complexité, reste irrésolu dans les domaines réels: les voitures autonomes, les instruments de détection d'intrusion, les conseillers virtuels par messagerie et tant d'autres profiteraient encore actuellement d'une capacité de reconnaissance d'intention. Longtemps abordé sous l'angle de considérations symboliques spécifiées par des experts humains, le problème commence à être résolu par des approches récentes usant d'algorithmes d'apprentissage dans des contextes simples. Nous nous inspirons ici des progrès de l'apprentissage profond dans des domaines connexes pour en faire usage à des fins de reconnaissance de but à long-terme. Encore sous-exploité pour cette catégorie de problèmes, nous l'avons mis à l'épreuve pour résoudre les problèmes traités dans la littérature et cherchons à améliorer les performances de l'état de l'art. Pour ce faire, nous présentons trois articles de recherche. Le premier, accepté au workshop PAIR (Plan, Activity and Intent Recognition) lors de la conférence AAAI 2018 (Association for the Advancement of Artificial Intelligence), propose une comparaison expérimentale entre différentes architectures d'apprentissage profond et les méthodes symboliques de l'état de l'art. Nous montrons de ce fait que nos meilleurs résultats surpassent ces méthodes symboliques dans les domaines considérés. Le deuxième, publié sur arXiv, introduit une méthode pour permettre à un réseau de neurones de généraliser rapidement à plusieurs environnements grâce à une projection des données sur un espace intermédiaire et en s'inspirant des progrès du few-shot transfer learning. Enfin, le troisième, soumis à ICAPS 2020 (International Conference on Automated Planning and Scheduling), améliore encore les résultats précédents en fournissant aux réseaux des caractéristiques supplémentaires leur permettant de se projeter dans le futur avec une capacité d'imagination et de résoudre le principal défaut inhérent aux approches symboliques de l'état de l'art, à savoir la dépendance à une représentation approximée de l'environnement