Going Deeper into Action Recognition: A Survey

Understanding human actions in visual data is tied to advances in complementary research areas including object recognition, human dynamics, domain adaptation and semantic segmentation. Over the last decade, human action analysis evolved from earlier schemes that are often limited to controlled environments to nowadays advanced solutions that can learn from millions of videos and apply to almost all daily activities. Given the broad range of applications from video surveillance to human-computer interaction, scientific milestones in action recognition are achieved more rapidly, eventually leading to the demise of what used to be good in a short time. This motivated us to provide a comprehensive review of the notable steps taken towards recognizing human actions. To this end, we start our discussion with the pioneering methods that use handcrafted representations, and then, navigate into the realm of deep learning based approaches. We aim to remain objective throughout this survey, touching upon encouraging improvements as well as inevitable fallbacks, in the hope of raising fresh questions and motivating new research directions for the reader

Spatial groundings for meaningful symbols

The increasing availability of ontologies raises the need to establish relationships and make inferences across heterogeneous knowledge models. The approach proposed and supported by knowledge representation standards consists in establishing formal symbolic descriptions of a conceptualisation, which, it has been argued, lack grounding and are not expressive enough to allow to identify relations across separate ontologies. Ontology mapping approaches address this issue by exploiting structural or linguistic similarities between symbolic entities, which is costly, error-prone, and in most cases lack cognitive soundness. We argue that knowledge representation paradigms should have a better support for similarity and propose two distinct approaches to achieve it. We first present a representational approach which allows to ground symbolic ontologies by using Conceptual Spaces (CS), allowing for automated computation of similarities between instances across ontologies. An alternative approach is presented, which considers symbolic entities as contextual interpretations of processes in spacetime or Differences. By becoming a process of interpretation, symbols acquire the same status as other processes in the world and can be described (tagged) as well, which allows the bottom-up production of meaning

Excitation Backprop for RNNs

Deep models are state-of-the-art for many vision tasks including video action recognition and video captioning. Models are trained to caption or classify activity in videos, but little is known about the evidence used to make such decisions. Grounding decisions made by deep networks has been studied in spatial visual content, giving more insight into model predictions for images. However, such studies are relatively lacking for models of spatiotemporal visual content - videos. In this work, we devise a formulation that simultaneously grounds evidence in space and time, in a single pass, using top-down saliency. We visualize the spatiotemporal cues that contribute to a deep model's classification/captioning output using the model's internal representation. Based on these spatiotemporal cues, we are able to localize segments within a video that correspond with a specific action, or phrase from a caption, without explicitly optimizing/training for these tasks.Comment: CVPR 2018 Camera Ready Versio

Geocoding health data with Geographic Information Systems: a pilot study in northeast Italy for developing a standardized data-acquiring format

Introduction. Geographic Information Systems (GIS) have become an innovative and somewhat crucial tool for analyzing relationships between public health data and environment. This study, though focusing on a Local Health Unit of northeastern Italy, could be taken as a benchmark for developing a standardized national data-acquiring format, providing a step-by-step instructions on the manipulation of address elements specific for Italian language and traditions. Methods. Geocoding analysis was carried out on a health database comprising 268,517 records of the Local Health Unit of Rovigo in the Veneto region, covering a period of 10 years, starting from 2001 up to 2010. The Map Service provided by the Environmental Research System Institute (ESRI, Redlands, CA), and ArcMap 10.0 by ESRI\uae were, respectively, the reference data and the GIS software, employed in the geocoding process. Results. The first attempt of geocoding produced a poor quality result, having about 40% of the addresses matched. A procedure of manual standardization was performed in order to enhance the quality of the results, consequently a set of guiding principle were expounded which should be pursued for geocoding health data. High-level geocoding detail will provide a more precise geographic representation of health related events. Conclusions. The main achievement of this study was to outline some of the difficulties encountered during the geocoding of health data and to put forward a set of guidelines, which could be useful to facilitate the process and enhance the quality of the results. Public health informatics represents an emerging specialty that highlights on the application of information science and technology to public health practice and research. Therefore, this study could draw the attention of the National Health Service to the underestimated problem of geocoding accuracy in health related data for environmental risk assessment

An Experience-Connected e-Learning System with a Personalization Mechanism for Learners’ Situations and Preferences

This paper presents an “experience-connected” e- Learning system that facilitates users to learn practical skills of foreign language by associating knowledge and daily-life experiences. “Experience-Connected” means that the users of this system receive personalized and situation-dependent learning materials automatically. Knowledge associated to users’ daily-life has the following advantages: 1) provides opportunities to learn frequently, and 2) provides clear and practical context information about foreign language usage. The unique feature of this system is a dynamic relevance computation mechanism that retrieves learning materials according to both preference relevance and spatiotemporal relevance. Users of this system obtain appropriate learning materials, without manual and time-consuming search processes. This paper proves the feasibility of the system by showing the actual system implementation that automatically broadcasts the media-data of foreign language learning materials to smart-phones