Two Decades of Maude

This paper is a tribute to José Meseguer, from the rest of us in the Maude team, reviewing the past, the present, and the future of the language and system with which we have been working for around two decades under his leadership. After reviewing the origins and the language's main features, we present the latest additions to the language and some features currently under development. This paper is not an introduction to Maude, and some familiarity with it and with rewriting logic are indeed assumed.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Learning to Refine Human Pose Estimation

Multi-person pose estimation in images and videos is an important yet challenging task with many applications. Despite the large improvements in human pose estimation enabled by the development of convolutional neural networks, there still exist a lot of difficult cases where even the state-of-the-art models fail to correctly localize all body joints. This motivates the need for an additional refinement step that addresses these challenging cases and can be easily applied on top of any existing method. In this work, we introduce a pose refinement network (PoseRefiner) which takes as input both the image and a given pose estimate and learns to directly predict a refined pose by jointly reasoning about the input-output space. In order for the network to learn to refine incorrect body joint predictions, we employ a novel data augmentation scheme for training, where we model "hard" human pose cases. We evaluate our approach on four popular large-scale pose estimation benchmarks such as MPII Single- and Multi-Person Pose Estimation, PoseTrack Pose Estimation, and PoseTrack Pose Tracking, and report systematic improvement over the state of the art.Comment: To appear in CVPRW (2018). Workshop: Visual Understanding of Humans in Crowd Scene and the 2nd Look Into Person Challenge (VUHCS-LIP

Peirce, meaning and the semantic web

The so-called ‘Semantic Web’ is phase II of Tim Berners-Lee’s original vision for the WWW, whereby resources would no longer be indexed merely ‘syntactically’, via opaque character-strings, but via their meanings. We argue that one roadblock to Semantic Web development has been researchers’ adherence to a Cartesian, ‘private’ account of meaning, which has been dominant for the last 400 years, and which understands the meanings of signs as what their producers intend them to mean. It thus strives to build ‘silos of meaning’ which explicitly and antecedently determine what signs on the Web will mean in all possible situations. By contrast, the field is moving forward insofar as it embraces Peirce’s ‘public’, evolutionary account of meaning, according to which the meaning of signs just is the way they are interpreted and used to produce further signs. Given the extreme interconnectivity of the Web, it is argued that silos of meaning are unnecessary as plentiful machine-understandable data about the meaning of Web resources exists already in the form of those resources themselves, for applications that are able to leverage it, and it is Peirce’s account of meaning which can best make sense of the recent explosion in ‘user-defined content’ on the Web, and its relevance to achieving Semantic Web goals