VQPP: Video Query Performance Prediction Benchmark
Adrian Catalin Lutu, Eduard Poesina, Radu Tudor Ionescu

TL;DR
This paper introduces the first benchmark for video query performance prediction (VQPP), providing datasets and evaluation tools to advance research in content-based video retrieval and enabling new applications like query reformulation.
Contribution
It presents the first VQPP benchmark with datasets, evaluation splits, and baseline predictors, facilitating reproducible research and exploration of performance prediction in video retrieval.
Findings
Pre-retrieval predictors perform competitively, enabling early prediction applications.
VQPP benchmark supports future research in video retrieval performance prediction.
Best pre-retrieval predictor can be used as a reward model for training language models.
Abstract
Query performance prediction (QPP) is an important and actively studied information retrieval task, having various applications, such as query reformulation, query expansion, and retrieval system selection, among many others. The task has been primarily studied in the context of text and image retrieval, whereas QPP for content-based video retrieval (CBVR) remains largely underexplored. To this end, we propose the first benchmark for video query performance prediction (VQPP), comprising two text-to-video retrieval datasets and two CBVR systems, respectively. VQPP contains a total of 56K text queries and 51K videos, and comes with official training, validation and test splits, fostering direct comparisons and reproducible results. We explore multiple pre-retrieval and post-retrieval performance predictors, creating a representative benchmark for future exploration of QPP in the video…
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Taxonomy
TopicsMultimodal Machine Learning Applications · Information Retrieval and Search Behavior · Advanced Image and Video Retrieval Techniques
