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.

The vitrivr multimedia retrieval stack has been shown to be versatile and effective in many areas and application domains. In this paper, we present vitrivr's new retrieval engine, the vitrivr-engine, a flexible and highly configurable information extraction and query processing solution with support for a broad range of media types.

Egocentric video has seen increased interest in recent years, as it is used in a range of areas. However, most existing datasets are limited to a single perspective. In this paper, we present the CASTLE 2024 dataset, a multimodal collection containing ego- and exo-centric (i.e., first- and third-person perspective) video and audio from 15 time-aligned sources, as well as other sensor streams and auxiliary data. The dataset was recorded by volunteer participants over four days in a common location and includes the point of view of 10 participants, with an additional 5 fixed cameras providing an exocentric perspective. The entire dataset contains over 600 hours of UHD video recorded at 50 frames per second. In contrast to other datasets, CASTLE 2024 does not contain any partial censoring, such as blurred faces or distorted audio. The dataset is available via https://castle-dataset.github.io/.

The inaugural edition of the CASTLE grand challenge was held at ACM Multimedia 2025. The focus of the CASTLE challenge is to advance the state-of-the-art in analysis and understanding of multimodal data, especially centered around multistream ego- and exo-centric video. In this first instance of the challenge, participants had to solve three types of tasks - event instance search, object instance search, and question answering - in a fully automatic or interactive setting.

Multimodal knowledge graphs often separate easily represented information (text) from that which is not (multimedia documents like images, videos, or audio). This severely limits query expressiveness, as the engines lack access to the node contents stored externally. We present MeGraS, the MediaGraph Store, a novel storage and query engine for multimodal knowledge graphs. By storing multimedia documents directly in the graph, MeGraS allows the query engine to leverage their content for enhanced capabilities, making it natively capable of performing operations such as k-NN, segmentation, or deriving non-materialized relations based on visual features. To demonstrate this, we incorporate and extend the pattern-matching query language SPARQL, resulting in a unified framework for storing and managing multimodal knowledge graphs with advanced expressiveness. MeGraS is available as open-source software: http://megras.org

The field of information retrieval, especially when targeting multimodal content, has found ways of satisfying a broad range of information needs, which can be expressed in a multitude of ways. In contrast to related fields, such as relational databases, no universal way of representing the queries to be answered by a retrieval system has emerged. In this paper, we present an initial proposal for a universal query representation mechanism for multimodal information retrieval. The proposed approach imperatively expresses arbitrary information needs, using a DAG of query primitives. We show how such a representation can be used for both feature extraction and query processing pipelines and how it can serve as a foundation towards a query language for information retrieval.

Most scholarly search services only provide basic text-matching or similarity-based searches, with limited operations that require manual configuration, such as sorting and filtering by specific metadata attributes. These capabilities are insufficient for researchers who often have queries that involve complex constraints and operations, such as ''enumerating the authors of a given paper along with the venues where they have published other papers.'' In this work, we develop an interactive and intuitive scholarly question answering system called NLQxform-UI, which allows users to pose complex queries in the form of natural language questions. It is capable of automatically translating these questions into SPARQL queries that can be executed over the DBLP knowledge graph to retrieve expected answers. Furthermore, the users can interact with each step of the answering process and browse the final results in a web-based interface. A video recording of our system is available at https://youtu.be/elq8CPykiyk Additionally, the system has been completely open-sourced: https://github.com/ruijie-wang-uzh/NLQxform-UI

Performance evaluation in multimedia retrieval, as in the information retrieval domain at large, relies heavily on retrieval experiments, employing a broad range of techniques and metrics. These can involve human-in-the-loop and machine-only settings for the retrieval process itself and the subsequent verification of results. Such experiments can be elaborate and use-case-specific, which can make them difficult to compare or replicate. In this article, we present a formal model to express all relevant aspects of such retrieval experiments, as well as a flexible open-source evaluation infrastructure that implements the model. These contributions intend to make a step towards lowering the hurdles for conducting retrieval experiments and improving their reproducibility.

Interactive video retrieval is a cooperative process between humans and retrieval systems. Large-scale evaluation campaigns, however, often overlook human factors, such as the effects of perception, attention, and memory, when assessing media retrieval systems. Consequently, their setups fall short of emulating realistic retrieval scenarios. In this article, we study the effect of task target presentation on the rate of retrieval success in a large crowdsourced experiment. To do this, we design novel task presentation modes based on concepts in media memorability, implement the pipelines necessary for processing target video segments, and build a custom experimental platform for the final evaluation. Our findings demonstrate that the way in which the target of a video retrieval task is presented has a substantial influence on the difficulty of the retrieval task and that individuals can successfully retrieve a target video segment despite reducing or even altering the provided hints, opening up a discussion around future evaluation protocols in the domain of interactive media retrieval.

Adding explanations to recommender systems is said to have multiple benefits, such as increasing user trust or system transparency. Previous work from other application areas suggests that specific user characteristics impact the users’ perception of the explanation. However, we rarely find this type of evaluation for recommender systems explanations. This paper addresses this gap by surveying 124 papers in which recommender systems explanations were evaluated in user studies. We analyzed their participant descriptions and study results where the impact of user characteristics on the explanation effects was measured. Our findings suggest that the results from the surveyed studies predominantly cover specific users who do not necessarily represent the users of recommender systems in the evaluation domain. This may seriously hamper the generalizability of any insights we may gain from current studies on explanations in recommender systems. We further find inconsistencies in the data reporting, which impacts the reproducibility of the reported results. Hence, we recommend actions to move toward a more inclusive and reproducible evaluation.

Multimodal methods that align the semantics of two data modalities-usually text and image-have become increasingly widespread in recent years. While such bi-modal alignments have received much attention, less work has been done toward aligning more than two modalities. This paper explores several strategies for training late-fusion joint-embedding models with up to four simultaneous modalities. We show that, even on a comparatively small dataset, there are clear and disproportional advantages when considering all modalities concurrently rather than consecutively. The results indicate that, when aligning multiple modalities, a training set with even weak alignment between all modalities substantially outperforms the use of several pair-wise alignments and that the addition of a modality can disproportionally increase alignment quality across all modalities.

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.

The capture and subsequent analysis of egocentric imagery in the form of Lifelogs can be useful in several application areas. However, suitable hardware to record such data is not always available or can be cost-prohibitive. This paper introduced the OpenLifelogCam, an open-source hardware wearable camera platform that is designed to be customizable enough to cover a broad range of possible applications while being as cheap as possible to construct even in low volumes.

To support the replication of "MMSF: A Multimodal Sentiment-Fused Method to Recognize Video Speaking Style", which was presented at ICMR'23, this companion paper provides the details of the artifacts. Speaking style recognition is aimed at recognizing the styles of conversations, which provides a fine-grained description about talking. In the original paper, we proposed a novel multimodal sentiment-fused method, MMSF, which extracts and integrates visual, audio and textual features of videos and introduced sentiment in MMSF with cross-attention mechanism to enhance the video feature to recognize speaking styles. In this paper, we explain the details of the implement code and the dataset used for experiments.

Image descriptions provide precious information for a myriad of visual media management tasks ranging from image classification to image search. The value of such curated collections comes from their diverse content and their accompanying extensive annotations. Such annotations are typically supplied by communities, where users (often volunteers) curate labels and/or descriptions of images. Supporting users in their quest to increase (overall) description completeness where possible is, therefore, of utmost importance.

Video retrieval still offers many challenges which can so far only be effectively mediated through interactive, human-in-the-loop retrieval approaches. The vitrivr multimedia retrieval stack offers a broad range of query mechanisms to enable users to perform such interactive retrieval. While these multiple mechanisms offer various options to experienced users, they can be difficult to use for novices. In this paper, we present a minimal user interface geared towards novice users that only exposes a subset of vitrivr’s functionality but simplifies user interaction.

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.

Interactive retrieval with user-friendly and performant interfaces remains a necessity for video retrieval, even in light of significant gains in retrieval performance through multi-modal encoders. In recent years, novel interaction modalities such as virtual reality (VR) and augmented reality (AR) have gained popularity, but the best way to adapt paradigms from traditional retrieval interfaces, especially for result browsing and interaction, remains an open research question. In this paper, we compare two video retrieval interfaces in a controlled setting to gain insight into the differences in video browsing between VR and desktop interfaces. We formulate hypotheses explaining why there might be performance differences between the two interfaces, define metrics to test the hypotheses, and show results based on data gathered at an evaluation campaign. Our results show that VR interfaces can be competitive in browsing performance and indicate that there can even be an advantage when browsing larger result sets in VR.

For the fifth time since 2018, the Lifelog Search Challenge (LSC) facilitated a benchmarking exercise to compare interactive search systems designed for multimodal lifelogs. LSC'22 attracted nine participating research groups who developed interactive lifelog retrieval systems enabling fast and effective access to lifelogs. The systems competed in front of a hybrid audience at the LSC workshop at ACM ICMR'22. This paper presents an introduction to the LSC workshop, the new (larger) dataset used in the competition, and introduces the participating lifelog search systems.

The automatic annotation of higher-level semantic information in long-form video content is still a challenging task. The Deep Video Understanding (DVU) Challenge aims at catalyzing progress in this area by offering common data and tasks. In this paper, we present our contribution to the 3rd DVU challenge. Our approach consists of multiple information streams extracted from both the visual and the audio modality. The streams can build on information generated by previous streams to increase their semantic descriptiveness. Finally, the output of all streams can be aggregated in order to produce a graph representation of the input movie to represent the semantic relationships between the relevant characters.

During the last 10 years of Video Browser Showdown (VBS), there were many different approaches tested for known-item search and ad-hoc search tasks. Undoubtedly, teams incorporating state-of-the-art models from the machine learning domain had an advantage over teams focusing just on interactive interfaces. On the other hand, VBS results indicate that effective means of interaction with a search system is still necessary to accomplish challenging search tasks. In this tutorial, we summarize successful deep models tested at the Video Browser Showdown as well as interfaces designed on top of corresponding distance/similarity spaces. Our broad experience with competition organization and evaluation will be presented as well, focusing on promising findings and also challenging problems from the most recent iterations of the Video Browser Showdown.

The retrieval of multimedia content remains a difficult problem where a high accuracy or specificity can often only be achieved interactively, with a user working closely and iteratively with a retrieval system. While there exist several venues for the exchange of insights in the area of information retrieval in general and multimedia retrieval specifically, there is little discussion on such interactive retrieval approaches. The Workshop on Interactive Multimedia Retrieval offers such a venue. Held for the 2nd time in 2022, it attracted a diverse set of contributions, six of which were accepted for presentation. The following provides a brief overview of the workshop itself as well as the contributions of 2022.

In real-world datasets, specifically in TV recordings, videos are often multi-person and multi-angle, which poses significant challenges for gesture recognition and retrieval. In addition to being of interest to linguists, gesture retrieval is a novel and challenging application for multimedia retrieval. In this paper, we propose a novel method for spatio-temporal gesture retrieval based on visual and pose information which can retrieve similar gestures in multi-person scenes through continuous shots. The attention-aware features, extracted from human pose key-points, together with a sophisticated pre-processing module, alleviate the susceptibility of gesture retrieval to background noise and occlusion. We have evaluated our method on a subset of the NewsScape Dataset. Our experimental results demonstrate the effectiveness of the proposed method in retrieving similar results in occluded scenes as measured by the quality of the top 5 results.

The Lifelog Search Challenge (LSC) is an annual benchmarking challenge for comparing approaches to interactive retrieval from multi-modal lifelogs. LSC'21, the fourth challenge, attracted sixteen participants, each of which had developed interactive retrieval systems for large multimodal lifelogs. These interactive retrieval systems participated in a comparative evaluation in front of an online live-audience at the LSC workshop at ACM ICMR'21. This overview presents the motivation for LSC'21, the lifelog dataset used in the competition, and the participating systems.

Despite the fact that automatic content analysis has made remarkable progress over the last decade - mainly due to significant advances in machine learning - interactive video retrieval is still a very challenging problem, with an increasing relevance in practical applications. The Video Browser Showdown (VBS) is an annual evaluation competition that pushes the limits of interactive video retrieval with state-of-the-art tools, tasks, data, and evaluation metrics. In this paper, we analyse the results and outcome of the 8th iteration of the VBS in detail. We first give an overview of the novel and considerably larger V3C1 dataset and the tasks that were performed during VBS 2019. We then go on to describe the search systems of the six international teams in terms of features and performance. And finally, we perform an in-depth analysis of the per-team success ratio and relate this to the search strategies that were applied, the most popular features, and problems that were experienced. A large part of this analysis was conducted based on logs that were collected during the competition itself. This analysis gives further insights into the typical search behavior and differences between expert and novice users. Our evaluation shows that textual search and content browsing are the most important aspects in terms of logged user interactions. Furthermore, we observe a trend towards deep learning based features, especially in the form of labels generated by artificial neural networks. But nevertheless, for some tasks, very specific content-based search features are still being used. We expect these findings to contribute to future improvements of interactive video search systems.

Comprehensive and fair performance evaluation of information retrieval systems represents an essential task for the current information age. Whereas Cranfield-based evaluations with benchmark datasets support development of retrieval models, significant evaluation efforts are required also for user-oriented systems that try to boost performance with an interactive search approach. This article presents findings from the 9th Video Browser Showdown, a competition that focuses on a legitimate comparison of interactive search systems designed for challenging known-item search tasks over a large video collection. During previous installments of the competition, the interactive nature of participating systems was a key feature to satisfy known-item search needs, and this article continues to support this hypothesis. Despite the fact that top-performing systems integrate the most recent deep learning models into their retrieval process, interactive searching remains a necessary component of successful strategies for known-item search tasks. Alongside the description of competition settings, evaluated tasks, participating teams, and overall results, this article presents a detailed analysis of query logs collected by the top three performing systems, SOMHunter, VIRET, and vitrivr. The analysis provides a quantitative insight to the observed performance of the systems and constitutes a new baseline methodology for future events. The results reveal that the top two systems mostly relied on temporal queries before a correct frame was identified. An interaction log analysis complements the result log findings and points to the importance of result set and video browsing approaches. Finally, various outlooks are discussed in order to improve the Video Browser Showdown challenge in the future.

Gestures play an important role in our daily communications. However, recognizing and retrieving gestures in-the-wild is a challenging task which is not explored thoroughly in literature. In this paper, we explore the problem of identifying and retrieving gestures in a large-scale video dataset provided by the computer vision community and based on queries recorded in-the-wild. Our proposed pipeline, I3DEF, is based on the extraction of spatio-temporal features from intermediate layers of an I3D network, a state-of-the-art network for action recognition, and the fusion of the output of feature maps from RGB and optical flow input. The obtained embeddings are used to train a triplet network to capture the similarity between gestures. We further explore the effect of a person and body part masking step for improving both retrieval performance and recognition rate. Our experiments show the ability of I3DEF to recognize and retrieve gestures which are similar to the queries independently of the depth modality. This performance holds both for queries taken from the test data, and for queries using recordings from different people performing relevant gestures in a different setting.

While a multitude of approaches for extracting semantic information from multimedia documents has emerged in recent years, isolating any form of holistic semantic representation from a larger type of document, such as a movie, is not yet feasible. In this paper we present our approaches used in the first instance of the Deep Video Understanding Challenge, using a combination of several multi-modal detectors and an integration scheme informed by methods from the semantic web context in order to determine the capabilities limitations of currently available methods for the extraction of semantic relations between the characters and locations relevant to the narrative of a movie.

Multimedia retrieval and analysis are two important areas in "Big data" research. They have in common that they work with feature vectors as proxies for the media objects themselves. Together with metadata such as textual descriptions or numbers, these vectors describe a media object in its entirety, and must therefore be considered jointly for both storage and retrieval.

In this work we analyze content statistics of the V3C1 dataset, which is the first partition of theVimeo Creative Commons Collection (V3C). The dataset has been designed to represent true web videos in the wild, with good visual quality and diverse content characteristics, and will serve as evaluation basis for the Video Browser Showdown 2019-2021 and TREC Video Retrieval (TRECVID) Ad-Hoc Video Search tasks 2019-2021. The dataset comes with a shot segmentation (around 1 million shots) for which we analyze content specifics and statistics. Our research shows that the content of V3C1 is very diverse, has no predominant characteristics and provides a low self-similarity. Thus it is very well suited for video retrieval evaluations as well as for participants of TRECVID AVS or the VBS.

The steady growth of multimedia collections - both in terms of size and heterogeneity - necessitates systems that are able to conjointly deal with several types of media as well as large volumes of data. This is especially true when it comes to satisfying a particular information need, i.e., retrieving a particular object of interest from a large collection. Nevertheless, existing multimedia management and retrieval systems are mostly organized in silos and treat different media types separately. Hence, they are limited when it comes to crossing these silos for accessing objects. In this paper, we present vitrivr, a general-purpose content-based multimedia retrieval stack. In addition to the keyword search provided by most media management systems, vitrivr also exploits the object's content in order to facilitate different types of similarity search. This can be done within and, most importantly, across different media types giving rise to new, interesting use cases. To the best of our knowledge, the full vitrivr stack is unique in that it seamlessly integrates support for four different types of media, namely images, audio, videos, and 3D models.

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.

The IMOTION system is a multimodal content-based video search and browsing application offering a rich set of query modes on the basis of a broad range of different features. It is able to scale with the size of the collection due to its underlying flexible polystore called ADAMpro and its very effective retrieval engine Cineast, optimized for multi-feature fusion. IMOTION is simultaneously geared towards precision-focused searches, i.e., known-item search with image or text queries, and recall-focused, exploratory searches. In this demo, we will present the 2017 IMOTION system deployed on the IACC.3 collection consisting of 600 hours of Internet Archive video, which was also used in the TRECVID 2016 Ad-Hoc Video Search and in the 2017 Video Browser Showdown (VBS) challenge in which IMOTION ranked first. Conference attendees will have the chance to interact with the 2017 IMOTION system and quickly solve various retrieval tasks.

Interactive video retrieval tools developed over the past few years are emerging as powerful alternatives to automatic retrieval approaches by giving the user more control as well as more responsibilities. Current research tries to identify the best combinations of image, audio and text features that combined with innovative UI design maximize the tools performance. We present the last installment of the Video Browser Showdown 2015 which was held in conjunction with the International Conference on MultiMedia Modeling 2015 (MMM 2015) and has the stated aim of pushing for a better integration of the user into the search process. The setup of the competition including the used dataset and the presented tasks as well as the participating tools will be introduced . The performance of those tools will be thoroughly presented and analyzed. Interesting highlights will be marked and some predictions regarding the research focus within the field for the near future will be made.

vitrivr is an open source full-stack content-based multimedia retrieval system with focus on video. Unlike the majority of the existing multimedia search solutions, vitrivr is not limited to searching in metadata, but also provides content-based search and thus offers a large variety of different query modes which can be seamlessly combined: Query by sketch, which allows the user to draw a sketch of a query image and/or sketch motion paths, Query by example, keyword search, and relevance feedback. The vitrivr architecture is self-contained and addresses all aspects of multimedia search, from offline feature extraction, database management to frontend user interaction. The system is composed of three modules: a web-based frontend which allows the user to input the query (e.g., add a sketch) and browse the retrieved results (vitrivr-ui), a database system designed for interactive search in large-scale multimedia collections (ADAM), and a retrieval engine that handles feature extraction and feature-based retrieval (Cineast). The vitrivr source is available on GitHub under the MIT open source (and similar) licenses and is currently undergoing several upgrades as part of the Google Summer of Code 2016.

Despite the tremendous importance and availability of large video collections, support for video retrieval is still rather limited and is mostly tailored to very concrete use cases and collections. In image retrieval, for instance, standard keyword search on the basis of manual annotations and content-based image retrieval, based on the similarity to query image (s), are well established search paradigms, both in academic prototypes and in commercial search engines. Recently, with the proliferation of sketch-enabled devices, also sketch-based retrieval has received considerable attention. The latter two approaches are based on intrinsic image features and rely on the representation of the objects of a collection in the feature space. In this paper, we present Cineast, a multi-feature sketch-based video retrieval engine. The main objective of Cineast is to enable a smooth transition from content-based image retrieval to content-based video retrieval and to support powerful search paradigms in large video collections on the basis of user-provided sketches as query input. Cineast is capable of retrieving video sequences based on edge or color sketches as query input and even supports one or multiple exemplary video sequences as query input. Moreover, Cineast also supports a novel approach to sketch-based motion queries by allowing a user to specify the motion of objects within a video sequence by means of (partial) flow fields, also specified via sketches. Using an emergent combination of multiple different features, Cineast is able to universally retrieve video (sequences) without the need for prior knowledge or semantic understanding. The evaluation with a general purpose video collection has shown the effectiveness and the efficiency of the Cineast approach.