Evaluation Pitfalls and Challenges in Multimedia Event Extraction
For researchers in multimedia event extraction, this work highlights the need for rigorous evaluation standards to ensure reliable and comparable results.
The paper identifies three major evaluation pitfalls in multimedia event extraction—inconsistent data processing, task assumptions, and overly relaxed evaluation settings—and shows through controlled experiments that these can cause large performance variations and overestimate model capabilities.
Multimedia event extraction aims to jointly identify events and their arguments across multiple modalities, such as text and images, to support more comprehensive event understanding. While recent work reports steady and substantial progress, the reliability and comparability of these results critically depend on consistent and rigorous evaluation. In this work, we present the first systematic analysis of evaluation pitfalls in multimedia event extraction and identify three major sources of issues: inconsistent data processing, inconsistent task assumptions, and overly relaxed evaluation settings. We demonstrate, through a series of controlled experiments under a strict evaluation framework, that minor evaluation choices can cause large performance variations and lead to overestimation of a model's ability to ground real-world events across modalities. Our findings highlight the need for comparable evaluation standards and encourage a shift toward more rigorous evaluation in multimedia event extraction.