Performance Evaluation in Multimedia Retrieval
<|reference_start|>Performance Evaluation in Multimedia Retrieval: Performance evaluation in multimedia retrieval, as in the information retrieval domain at large, relies heavily on retrieval experiments, employing a broad range of techniques and metrics. These can involve human-in-the-loop and machine-only settings for the retrieval process itself and the subsequent verification of results. Such experiments can be elaborate and use-case-specific, which can make them difficult to compare or replicate. In this paper, we present a formal model to express all relevant aspects of such retrieval experiments, as well as a flexible open-source evaluation infrastructure that implements the model. These contributions intend to make a step towards lowering the hurdles for conducting retrieval experiments and improving their reproducibility.<|reference_end|>
@article{sauter2024performance,
    title={Performance Evaluation in Multimedia Retrieval},
    author={Loris Sauter, Ralph Gasser, Heiko Schuldt, Abraham Bernstein, Luca
  Rossetto},
    journal={arXiv preprint arXiv:2410.06654},
    year={2024},
    doi={10.1145/3678881},
    archivePrefix={arXiv},
    eprint={2410.06654},
    primaryClass={cs.IR cs.MM}
}