Determining whether multimedia content related to a specific retrieval topic or a developing news story contains novel information is a crucial task in media monitoring and production. While visual content gains importance, most of the existing work focuses on text documents. We propose a method for determining novel visual information in videos, embedded in a workflow using multimodal topic threading and video to text, and compare two approaches to measure novelty. We present a web application for determining the novelty in multimedia content being ingested into a collection, and evaluate the approach on a subset of the FIVR-200K dataset.

The ACM Lifelog Search Challenge (LSC) is an annual comparative benchmarking exercise that brings together researchers in the field of multimedia retrieval to evaluate interactive search systems using a large-scale multimodal lifelog dataset. This paper presents an overview of the ninth edition of the challenge (LSC’26), held as a workshop during the ACM International Conference on Multimedia Retrieval (ICMR ’26) in Amsterdam. To broaden its scope, the workshop now features three submission tracks: the traditional Challenge Track for real-time search performance, a new General Lifelog Research Track for theoretical and architectural advancements, and an additional Open Source Track aimed at enhancing reproducibility and reducing barriers to entry for new participants.

For the eighth time since 2018, the ACM Lifelog Search Challenge (LSC) was run to compare interactive lifelog search systems in a live metrics-based challenge. The goal of the LSC workshop is to comparatively evaluate the capabilities of systems accessing a large multimodal lifelog. LSC'25 attracted eleven participating teams, each of which had developed an innovative interactive lifelog retrieval system. The benchmark was organised in a hybrid manner (due to political issues in 2025) at the LSC workshop at ACM ICMR'25 in Chicago, USA. This short paper summarises the LSC workshop setting and presents the participating lifelog search systems.

For the seventh time since 2018, the Lifelog Search Challenge (LSC) benchmarked interactive lifelog search systems in a live challenge. The LSC goal is to comparatively evaluate system capabilities to access large multimodal lifelogs comprising hundreds of thousands of records. LSC'24 attracted an unprecedented record number of twenty-one participating teams, where each team proposes innovative ideas implemented to new or already established interactive lifelog retrieval systems. The benchmark was organised in front of a live audience at the LSC workshop at ACM ICMR'24 in Phuket, Thailand. This short paper summarises the LSC workshop setting and presents the participating lifelog search systems.

This tutorial provides an overview of multimedia retrieval in the context of eXtended Reality (XR), including using virtual and augmented/mixed reality as a user interface for multimedia retrieval, as well as multimedia search tasks addressing content needs for the creation of XR experiences.It will discuss the opportunities and limitations of XR-based search, the evaluation of XR-based multimedia retrieval systems, the demonstration of selected research systems, and open research challenges.

Different task interpretations are a highly undesired element in interactive video retrieval evaluations. When a participating team focuses partially on a wrong goal, the evaluation results might become partially misleading. In this paper, we propose a process for refining known-item and open-set type queries, and preparing the assessors that judge the correctness of submissions to open-set queries. Our findings from recent years reveal that a proper methodology can lead to objective query quality improvements and subjective participant satisfaction with query clarity.

For the sixth time since 2018, the Lifelog Search Challenge (LSC) was organized as a comparative benchmarking exercise for various interactive lifelog search systems. The goal of this international competition is to test system capabilities to access large multimodal lifelogs. LSC’23 attracted twelve participanting teams, each of whom had developed a competitive interactive lifelog retrieval system. The benchmark was organized in front of live audience at the LSC workshop at ACM ICMR’23. As in previous editions, this introductory paper presents the LSC workshop and introduces the participating lifelog search systems.

The Video Browser Showdown (VBS) has influenced the Multimedia community already for 10 years now. More than 30 unique teams from over 21 countries participated in the VBS since 2012 already. In 2021, we are celebrating the 10th anniversary of VBS, where 17 international teams compete against each other in an unprecedented contest of fast and accurate multimedia retrieval. In this tutorial we discuss the motivation and details of the VBS contest, including its history, rules, evaluation metrics, and achievements for multimedia retrieval. We talk about the properties of specific VBS retrieval systems and their unique characteristics, as well as existing open-source tools that can be used as a starting point for participating for the first time. Participants of this tutorial get a detailed understanding of the VBS and its search systems, and see the latest developments of interactive video retrieval.

We present a tutorial focusing on video retrieval tasks, where state-of-the-art deep learning approaches still benefit from interactive decisions of users. The tutorial covers general introduction to the interactive video retrieval research area, state-of-the-art video retrieval systems, evaluation campaigns and recently observed results. Moreover, a significant part of the tutorial is dedicated to a practical exercise with three selected state-of-the-art systems in the form of an interactive video retrieval competition. Participants of this tutorial will gain a practical experience and also a general insight of the interactive video retrieval topic, which is a good start to focus their research on unsolved challenges in this area.

Information or feedback about users' information needs beyond a brief query is crucial for improving the effectiveness of video search. One paradigm that addresses this issue is search and linking, i.e., after an initial search the user selects an item and requests a set of items that share properties with the one selected. We propose an approach for the linking step using both textual and visual features. We first evaluate the proposed method on the MediaEval 2013 Search and Hyperlinking data set. The video segment from which linking starts cannot be assumed to be relevant and to have well-defined boundaries in a real-world setting, in contrast to the benchmarking data set. We thus investigate the performance of the proposed linking method on actual search results of different systems and using only a coarse estimate of a contextual segment (e.g., scene, story). The linking results starting from a related segment among the top-ranked search results are nearly as good as when using the correct segment as starting point. The results show that contextual information surrounding the segment used for generating the linking results plays a crucial role. When starting from a relevant or at least related item in the search result, the use of context information significantly improves results. For segments related to the query, the linking results are similar in terms of performance to those starting from relevant segments (without context), but contextual information does not improve the results.