# Performance Evaluation in Multimedia Retrieval

arXiv: https://arxiv.org/abs/2410.06654 · HTML: https://mlsanity.com/paper/2410.06654/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Oct 2024.

## Built on by (most cited first, 1 of 1)

- [Experimental Evaluation of Static Image Sub-Region-Based Search Models Using CLIP](https://mlsanity.com/paper/2506.06938/) (2025)

## Abstract (arXiv)

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.