In 2026, the Multimedia Modeling conference in Prague hosted the fifteenth edition of the Video Browser Showdown (VBS) competition. Yet, for the first time, two participating systems implemented full-scale interaction logging frameworks, enabling a detailed analysis of the search process beyond traditional score-based evaluation. In this paper, both systems are introduced and described with a focus on their user interactions. To enable compact presentation and analysis of logs, all interaction types are further grouped into more abstract events, forming an interaction taxonomy hierarchy. Finally, we reveal the applied search strategies and analyze which factors drive success and failure. The results reveal a clear dominance of iterative, high-frequency text query reformulation with result set inspection, leveraging the power of modern CLIP-based models across most competition categories. In around 10% of cases, users also relied on advanced system features to achieve good performance at VBS, mostly on challenging homogeneous datasets.

Unlike traditional retrieval, dynamic exploration of multimedia collections may require complex aggregation queries whose performance is highly sensitive to dataset size, filters and grouping applied at any time. Such queries often yield unstable response times, thus undermining interactivity. Inspired by the online aggregation approach from the database community, we investigate whether progressively refined intermediate results, accompanied by quality estimates, can enable users to decide whether to accept partial results or continue processing. We evaluate this approach within the Multidimensional Media Model, where media collections are explored through metadata tagsets and hierarchies. Our study analyses operator placement, showing that moving deduplication and grouping from the database to the server reduces time-to-first-result by over 90% while maintaining steady quality improvement. We further examine join ordering and selectivity estimation for reliable progress prediction. Our results demonstrate that online aggregation principles can substantially improve responsiveness in multimedia exploration systems.

This companion paper supports the replication of the development and evaluation of “MeTILDA - Platform for Melodic Transcription in Language Documentation and Application” that we presented in the ICMR 2021. MeTILDA aims to help document and analyze pitch patterns of endangered languages including Blackfoot, whose prosodic system is characterized by pitch movements. It develops a new form of audio analysis (termed MeT scale which is a perceptual scale) and automates the process of creating visual aids (Pitch Art) to provide more effective visuals of perceived changes in pitch movement. In this paper, we explain the file structure of the source code and publish the details of our data as well as system operations. Moreover, we provide a link to the demo video for facilitating the use of our platform.

User Relevance Feedback (URF) is a class of interactive learning methods that rely on the interaction between a human user and a system to analyze a media collection. To improve URF system evaluation and design better systems, it is important to understand the impact that different interaction strategies can have. Based on the literature and observations from real user sessions from the Lifelog Search Challenge and Video Browser Showdown, we analyze interaction strategies related to (a) labeling positive and negative examples, and (b) applying filters based on users' domain knowledge. Experiments show that there is no single optimal labeling strategy, as the best strategy depends on both the collection and the task. In particular, our results refute the common assumption that providing more training examples is always beneficial: strategies with a smaller number of prototypical examples lead to better results in some cases. We further observe that while expert filtering is unsurprisingly beneficial, aggressive filtering, especially by novice users, can hinder the completion of tasks. Finally, we observe that combining URF with filters leads to better results than using filters alone.

Analyzing multimedia collections in order to gain insight is a common desire amongst industry and society. Recent research has shown that while machines are getting better at analyzing multimedia data, they still lack the understanding and flexibility of humans. A central conjecture in Multimedia Analytics is that interactive learning is a key method to bridge the gap between human and machine. We investigate the requirements and design of the Exquisitor system, a very large-scale interactive learning system that aims to verify the validity of this conjecture. We describe the architecture and initial scalability results for Exquisitor, and propose research directions related to both performance and result quality.