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.

Known-item search in large video collections still represents a challenging task for current video retrieval systems that have to rely both on state-of-the-art ranking models and interactive means of retrieval. We present a general overview of the current version of the VIRET tool, an interactive video retrieval system that successfully participated at several international evaluation campaigns. The system is based on multi-modal search and convenient inspection of results. Based on collected query logs of four users controlling instances of the tool at the Video Browser Showdown 2019, we highlight query modification statistics and a list of successful query formulation strategies. We conclude that the VIRET tool represents a competitive reference interactive system for effective known-item search in one thousand hours of video.

Searching for one particular scene in a large video collection (known-item search) represents a challenging task for video retrieval systems. According to the recent results reached at evaluation campaigns, even respected approaches based on machine learning do not help to solve the task easily in many cases. Hence, in addition to effective automatic multimedia annotation and embedding, interactive search is recommended as well. This paper presents a comprehensive description of an interactive video retrieval framework VIRET that successfully participated at several recent evaluation campaigns. Utilized video analysis, feature extraction and retrieval models are detailed as well as several experiments evaluating effectiveness of selected system components. The results of the prototype at the Video Browser Showdown 2019 are highlighted in connection with an analysis of collected query logs. We conclude that the framework comprise a set of effective and efficient models for most of the evaluated known-item search tasks in 1000 hours of video and could serve as a baseline reference approach. The analysis also reveals that the result presentation interface needs improvements for better performance of future VIRET prototypes.

This work summarizes the findings of the 7th iteration of the Video Browser Showdown (VBS) competition organized as a workshop at the 24th International Conference on Multimedia Modeling in Bangkok. The competition focuses on video retrieval scenarios in which the searched scenes were either previously observed or described by another person (i.e., an example shot is not available). During the event, nine teams competed with their video retrieval tools in providing access to a shared video collection with 600 hours of video content. Evaluation objectives, rules, scoring, tasks, and all participating tools are described in the article. In addition, we provide some insights into how the different teams interacted with their video browsers, which was made possible by a novel interaction logging mechanism introduced for this iteration of the VBS. The results collected at the VBS evaluation server confirm that searching for one particular scene in the collection when given a limited time is still a challenging task for many of the approaches that were showcased during the event. Given only a short textual description, finding the correct scene is even harder. In ad hoc search with multiple relevant scenes, the tools were mostly able to find at least one scene, whereas recall was the issue for many teams. The logs also reveal that even though recent exciting advances in machine learning narrow the classical semantic gap problem, user-centric interfaces are still required to mediate access to specific content. Finally, open challenges and lessons learned are presented for future VBS events.

In this demo paper, we present a prototype web application of a product search engine of a fashion e-shop. Although e-shop products consist of full-text description, relational attributes (e.g., price, type, size, color, etc.) as well as visual information (product photo), traditional search engines in e-shops only provide full-text and relational attributes for product filtering. In our retrieval model, we incorporate also the visual information into the search by extracting visual-semantic features using deep convolutional neural networks. Furthermore, visual exploration of the product space using the visual-semantic features (multi-example queries) is used to dynamically discover latent visual attributes that could enhance the original relational schema by fuzzy attributes (e.g., a floral pattern in product). In the demo, we show how these latent attributes could be used to recommend the user preferred products and even outfits (e.g., shoes, bag, jacket) that fit a certain visual style.