Overview of NTCIR-12 Lifelog Task

In this paper we review the NTCIR12-Lifelog pilot task, which ran at NTCIR-12. We outline the test collection employed, along with the tasks, the eight submissions and the findings from this pilot task. We finish by suggesting future plans for the task

Experiments in lifelog organisation and retrieval at NTCIR

Lifelogging can be described as the process by which individuals use various software and hardware devices to gather large archives of multimodal personal data from multiple sources and store them in a personal data archive, called a lifelog. The Lifelog task at NTCIR was a comparative benchmarking exercise with the aim of encouraging research into the organisation and retrieval of data from multimodal lifelogs. The Lifelog task ran for over 4 years from NTCIR-12 until NTCIR-14 (2015.02–2019.06); it supported participants to submit to five subtasks, each tackling a different challenge related to lifelog retrieval. In this chapter, a motivation is given for the Lifelog task and a review of progress since NTCIR-12 is presented. Finally, the lessons learned and challenges within the domain of lifelog retrieval are presented

Advances in lifelog data organisation and retrieval at the NTCIR-14 Lifelog-3 task

Lifelogging refers to the process of digitally capturing a continuous and detailed trace of life activities in a passive manner. In order to assist the research community to make progress in the organisation and retrieval of data from lifelog archives, a lifelog task was organised at NTCIR since edition 12. Lifelog-3 was the third running of the lifelog task (at NTCIR-14) and the Lifelog-3 task explored three different lifelog data access related challenges, the search challenge, the annotation challenge and the insights challenge. In this paper we review the dataset created for this activity, activities of participating teams who took part in these challenges and we highlight learnings for the community from the NTCIR-Lifelog challenges

VRLE: Lifelog Interaction Prototype in Virtual Reality:Lifelog Search Challenge at ACM ICMR 2020

The Lifelog Search Challenge (LSC) invites researchers to share their prototypes for interactive lifelog retrieval and encourages competition to develop and evaluate effective methodologies to achieve this. With this paper we present a novel approach to visual lifelog exploration based on our research to date utilising virtual reality as a medium for interactive information retrieval. The VRLE prototype presented is an iteration on a previous system which won the first LSC competition at ACM ICMR 2018

Evaluating Information Retrieval and Access Tasks

This open access book summarizes the first two decades of the NII Testbeds and Community for Information access Research (NTCIR). NTCIR is a series of evaluation forums run by a global team of researchers and hosted by the National Institute of Informatics (NII), Japan. The book is unique in that it discusses not just what was done at NTCIR, but also how it was done and the impact it has achieved. For example, in some chapters the reader sees the early seeds of what eventually grew to be the search engines that provide access to content on the World Wide Web, today’s smartphones that can tailor what they show to the needs of their owners, and the smart speakers that enrich our lives at home and on the move. We also get glimpses into how new search engines can be built for mathematical formulae, or for the digital record of a lived human life. Key to the success of the NTCIR endeavor was early recognition that information access research is an empirical discipline and that evaluation therefore lay at the core of the enterprise. Evaluation is thus at the heart of each chapter in this book. They show, for example, how the recognition that some documents are more important than others has shaped thinking about evaluation design. The thirty-three contributors to this volume speak for the many hundreds of researchers from dozens of countries around the world who together shaped NTCIR as organizers and participants. This book is suitable for researchers, practitioners, and students—anyone who wants to learn about past and present evaluation efforts in information retrieval, information access, and natural language processing, as well as those who want to participate in an evaluation task or even to design and organize one

Visual object detection from lifelogs using visual non-lifelog data

Limited by the challenge of insufficient training data, research into lifelog analysis, especially visual lifelogging, has not progressed as fast as expected. To advance research on object detection on visual lifelogs, this thesis builds a deep learning model to enhance visual lifelogs by utilizing other sources of visual (non-lifelog) data which is more readily available. By theoretical analysis and empirical validation, the first step of the thesis identifies the close connection and relation between lifelog images and non-lifelog images. Following that, the second phase employs a domain-adversarial convolutional neural network to trans- fer knowledge from the domain of visual non-lifelog data to the domain of visual lifelogs. In the end, the third section of this work considers the task of visual object detection of lifelog, which could be easily extended to other related lifelog tasks. One intended outcome of the study, on a theoretical level of lifelog research, is to iden- tify the relationship between visual non-lifelog data and visual lifelog data from the perspective of computer vision. On a practical point of view, a second intended outcome of the research is to demonstrate how to apply domain adaptation to enhance learning on visual lifelogs by transferring knowledge from visual non-lifelogs. Specifically, the thesis utilizes variants of convolutional neural networks. Furthermore, a third intended outcome contributes to the release of the corresponding visual non-lifelog dataset which corresponds to an existing visual lifelog one. Finally, another output from this research is the suggestion that visual object detection from lifelogs could be seamlessly used in other tasks on visual lifelogging

LIG-MRIM at NTCIR-12 Lifelog Semantic Access Task

International audienceThis paper describes the participation of the LIG-MRIM research team to the Lifelog Semantic Access sub-task of the NTCIR-12 (2016). Our approach mainly relies on mapping the query terms to visual concepts computed on the Lifel-ogs images according to two separated learning schemes. A post-processing is then performed if the topic is related to temporal, location or activity information associated with the images. The results obtained are promising for a first participation to such a task, with event-based MAP above 29% and an event-based nDCG value close to 39%. Team Name MRIM Subtasks Lifelog Semantic Access Tas