Journal Article

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

Duane, Aaron, Nguyen, Thao-Nhu, Sauter, Loris, Vadicamo, Lucia, Vrochidis, Stefanos, Lokoč, Jakub, Messina, Nicola, Schall, Konstantin, Bailer, Werner, Spiess, Florian, Peška, Ladislav, Andreadis, Stelios, Ma, Zhixin, Schoeffmann, Klaus, Gurrin, Cathal, Khan, Omar Shahbaz, Rossetto, Luca

2023

Overview

This paper presents 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 this 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 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 ad-hoc search based tasks at Video Browser Showdown is introduced.

• Publisher: Springer Berlin Heidelberg,Springer Nature B.V
• Subject: Annual reports / Computer Communication Networks / Computer Graphics / Computer Science / Cryptology / Data Storage Representation / Datasets / Effectiveness / Multimedia / Multimedia Information Systems / Operating Systems / Queries / Regular Paper / Retrieval / Searching / Semantics / Teams