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Communication Dans Un Congrès Année : 2023

iQPP: A Benchmark for Image Query Performance Prediction

Résumé

To date, query performance prediction (QPP) in the context of content-based image retrieval remains a largely unexplored task, especially in the query-by-example scenario, where the query is an image. To boost the exploration of the QPP task in image retrieval, we propose the first benchmark for image query performance prediction (iQPP). First, we establish a set of four data sets (PASCAL VOC 2012, Caltech-101, ROxford5k and RParis6k) and estimate the ground-truth difficulty of each query as the average precision or the precision@k, using two state-of-the-art image retrieval models. Next, we propose and evaluate novel pre-retrieval and post-retrieval query performance predictors, comparing them with existing or adapted (from text to image) predictors. The empirical results show that most predictors do not generalize across evaluation scenarios. Our comprehensive experiments indicate that iQPP is a challenging benchmark, revealing an important research gap that needs to be addressed in future work. We release our code and data as open source at https://github.com/Eduard6421/iQPP, to foster future research.
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Dates et versions

hal-04346953 , version 1 (15-12-2023)

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Eduard Poesina, Radu Tudor Ionescu, Josiane Mothe. iQPP: A Benchmark for Image Query Performance Prediction. 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2023), ACM SIGIR: Special Interest Group on Information Retrieval, Jul 2023, Taipei, Taiwan. pp.2953-2963, ⟨10.1145/3539618.3591901⟩. ⟨hal-04346953⟩
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