VERGE in VBS 2022

Andreadis, Stelios; Moumtzidou, Anastasia; Galanopoulos, Damianos; Pantelidis, Nick; Apostolidis, Konstantinos; Τούσκα, Δέσποινα; Gkountakos, Konstantinos; Pegia, Maria; Gialampoukidis, Ilias; Vrochidis, Stefanos; Mezaris, Vasileios; Kompatsiaris, Ioannis

doi:10.1007/978-3-030-98355-0_50

Cited by 4 publications

(9 citation statements)

References 20 publications

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“…Verge [84] continued to build upon its previous version [33] by employing a 3D-CNN architecture for spatio-temporal human activity recognition [1]. The system followed a three-step pipeline [49] involving object detection, object tracking, and activity recognition.…”

Section: B Concept Searchmentioning

confidence: 99%

Evaluating Performance and Trends in Interactive Video Retrieval: Insights From the 12th VBS Competition

Vadicamo,

Arnold,

Bailer

et al. 2024

IEEE Access

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This paper conducts a thorough examination of the 12th Video Browser Showdown (VBS) competition, which is a well-established international benchmarking campaign for interactive video search systems. The annual VBS competition has witnessed a steep rise in the popularity of multimodal embedding-based approaches in interactive video retrieval. The majority of the thirteen systems participating in VBS 2023 utilized a CLIP-based cross-modal search model, allowing the specification of free-form text queries to search visual content. This shared emphasis on joint embedding models contributed to balanced performance across various teams. However, the distinguishing factors of the top-performing teams included the adept combination of multiple models and search modes, along with the capabilities of interactive interfaces to facilitate and refine the search process. Our work provides an overview of the state-of-the-art approaches employed by the participating systems and conducts a thorough analysis of their search logs, which record user interactions and results of their queries for each task. Our comprehensive examination of the VBS competition offers assessments of the effectiveness of the retrieval models employed, the browsing efficiency, and user query patterns. Additionally, it provides valuable insights into the evolving landscape of interactive video retrieval and its future challenges.

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Section: B Concept Searchmentioning

confidence: 99%

Evaluating Performance and Trends in Interactive Video Retrieval: Insights From the 12th VBS Competition

Vadicamo,

Arnold,

Bailer

et al. 2024

IEEE Access

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“…For additional detail about any system, please see the corresponding publication referenced beside the system name in the overview table. [42] KR 249 25 2 ✓ ✓ ✓ ✓ ✓ ✓ AVSEEKER [41] IE 207 25 2 [75] VN 200 26 2 ✓ ✓ ✓ ✓ ✓ ✓ VideoFall [60] IE 197 25 2 ✓ ✓ ✓ ✓ ✓ ✓ ✓ VERGE [6] GR 176 24 2 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ vitrivr [28] CH 175 21 2 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ VNUHCM [54] VN 161 22 2 ✓ ✓ ✓ ✓ VIREO [55] SG 158 16 2 [34] VN 146 22 2 ✓ ✓ ✓ ✓ ✓ vitrivr-VR [71] CH 137 20 2 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ diveXplore [43] AT 75 14 2 ✓ ✓ ✓ ✓ ✓ Exquisitor [40] DK 72 14 1…”

Section: Related Work Used By Participating Systemsmentioning

confidence: 99%

“…VERGE [6] involves spatio-temporal human activity recognition using a 3D-CNN architecture. This construction relies on a three-step pipeline [24]: object detector, object tracker, and activity recognizer to identify human-related activities effectively.…”

Section: Concept Searchmentioning

confidence: 99%

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Interactive video retrieval in the age of effective joint embedding deep models: lessons from the 11th VBS

Lokoč,

Andreadis,

Bailer

et al. 2023

Multimedia Systems

Self Cite

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This paper presents the findings of the eleventh Video Browser Showdown competition, where sixteen teams competed in known-item and ad-hoc search tasks. Many of the teams utilized state-of-the-art video retrieval approaches that demonstrated high effectiveness in challenging search scenarios. In the paper, a broad survey of all utilized approaches is presented in connection with an analysis of the performance of participating teams. Specifically, both high-level performance indicators are presented with overall statistics as well as an in-depth analysis of the performance of selected tools implementing result set logging. The analysis reveals evidence that the CLIP model represents a versatile tool for cross-modal video retrieval when combined with interactive search capabilities. Furthermore, the analysis investigates the effect of different users and text query properties on the performance in search tasks. Last but not least, lessons learned from search task preparation are presented, and a new direction for adhoc search based tasks at Video Browser Showdown is introduced.

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“…At VBS, in particular, systems are evaluated during online search sessions where human searchers are asked to use the system to find given shots in the shortest possible time. These challenges demonstrate the large and increasing research interest in the development of large-scale interactive multimedia retrieval systems [5,8,15,17,19].…”

Section: Introductionmentioning

confidence: 99%

VISIONE: A Large-Scale Video Retrieval System with Advanced Search Functionalities

Amato

Bolettieri

Carrara

et al. 2023

Proceedings of the 2023 ACM International Conference on Multimedia Retrieval

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VISIONE is a large-scale video retrieval system that integrates multiple search functionalities, including free text search, spatial color and object search, visual and semantic similarity search, and temporal search. The system leverages cutting-edge AI technology for visual analysis and advanced indexing techniques to ensure scalability. As demonstrated by its runner-up position in the 2023 Video Browser Showdown competition, VISIONE effectively integrates these capabilities to provide a comprehensive video retrieval solution. A system demo is available online, showcasing its capabilities on over 2300 hours of diverse video content (V3C1+V3C2 dataset) and 12 hours of highly redundant content (Marine dataset). The demo can be accessed at https://visione.isti.cnr.it/.

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VERGE in VBS 2022

Cited by 4 publications

References 20 publications

Evaluating Performance and Trends in Interactive Video Retrieval: Insights From the 12th VBS Competition

Evaluating Performance and Trends in Interactive Video Retrieval: Insights From the 12th VBS Competition

Interactive video retrieval in the age of effective joint embedding deep models: lessons from the 11th VBS

VISIONE: A Large-Scale Video Retrieval System with Advanced Search Functionalities

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