Performance Evaluation in Multimedia Retrieval
October 09, 2024 Β· Declared Dead Β· π ACM Trans. Multim. Comput. Commun. Appl.
"No code URL or promise found in abstract"
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Authors
Loris Sauter, Ralph Gasser, Heiko Schuldt, Abraham Bernstein, Luca Rossetto
arXiv ID
2410.06654
Category
cs.IR: Information Retrieval
Cross-listed
cs.MM
Citations
13
Venue
ACM Trans. Multim. Comput. Commun. Appl.
Last Checked
4 months ago
Abstract
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.
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