Multimodal deep-learning models power interactive video retrieval by ranking keyframes in response to textual queries. Despite these advances, users must still browse ranked candidates manually to locate a target. Keyframe arrangement within the search grid highly affects browsing effectiveness and user efficiency, yet remains underexplored. We report a study with 49 participants evaluating seven keyframe layouts for the Visual Known-Item Search task. Beyond efficiency and accuracy, we relate browsing phenomena, such as overlooks, to layout characteristics. Our results show that a video-grouped layout is the most efficient, while a four-column, rank-preserving grid achieves the highest accuracy. Sorted grids reveal potential and trade-offs, enabling rapid scanning of uninteresting regions but down-ranking relevant targets to less prominent positions, delaying first arrival times and increasing overlooks. These findings motivate hybrid designs that preserve positions of top-ranked items while sorting or grouping the remainder, and offer guidance for searching in grids beyond video retrieval.

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.

Searching in highly homogeneous video domains is challenging, especially when relying solely on human memory. This difficulty arises because homogeneous content often requires domain-specific vocabulary to describe effectively, and a single text label might fit a substantial subset of the database. This paper investigates supplementing text queries with spatial location information to better address specific search intents—a strategy applicable when users possess strong visual memory of a target object, or have external knowledge of its position. To process this location information effectively, we formally define and evaluate static grid and dynamic segmentation strategies for video frame partitioning. Furthermore, we present a large-scale cognitive user study involving 220 participants, designed to simulate realistic memory constraints of Known-Item Search (KIS) tasks commonly found in interactive retrieval benchmarks. The study utilizes standard working memory interference techniques, comprising a target exposure phase, a distraction period, and subsequent query specification from memory. These queries are then evaluated against the proposed spatial retrieval models to determine how human spatial memory decay impacts retrieval effectiveness. Our results reveal that while dynamic segmentation models achieve the highest theoretical retrieval bounds under perfect conditions, their strict geometric boundaries are severely affected by human memory decay. Consequently, static overlapping grids demonstrate high robustness, outperforming dynamic models under more realistic, memory-driven search constraints. Finally, our experiments show that the observed spatial annotation perturbations can be modeled using a four-dimensional Kernel Density Estimation (KDE) method, enabling the simulation of realistic human memory decay on ideal bounding boxes.

Treemaps are a powerful tool for representing hierarchical data in a space-efficient manner and are used in various domains, including network security or software development. However, interpreting the topology encoded by nested rectangles can be challenging, particularly compared to tree-structured representations like node-link diagrams or icicle plots. To address this challenge, we introduce TreEducation, a visual education platform designed to improve the visualization literacy skills required for reading treemaps among non-expert users. TreEducation is an online application that combines visualizations, interactions, and gamification elements to facilitate understanding of eight different treemap layout algorithms and enhance students' learning process. We evaluated TreEducation in a classroom setting and a controlled environment. Our results indicate a significant knowledge gain of students training exclusively with TreEducation and the usefulness of competition as a social gamification element included in our competitive quiz.

Deep learning has revolutionized multimedia retrieval, yet effectively searching within large video collections remains a complex challenge. This paper focuses on the design and evaluation of known-item search systems, leveraging the strengths of CLIP-based deep neural networks for ranking. At events like the Video Browser Showdown, these models have shown promise in effectively ranking the video frames. While ranking models can be pre-selected automatically based on a benchmark collection, the selection of an optimal browsing interface, crucial for refining top-ranked items, is complex and heavily influenced by user behavior. Our study addresses this by presenting an eye tracking-based analysis of user interaction with different image grid layouts. This approach offers novel insights into search patterns and user preferences, particularly examining the trade-off between displaying fewer but larger images versus more but smaller images. Our findings reveal a preference for grids with fewer images and detail how image similarity and grid position affect user search behavior. These results not only enhance our understanding of effective video retrieval interface design but also set the stage for future advancements in the field.