Evaluation Pitfalls and Challenges in Multimedia Event Extraction

2026-06-25

AI Summary

This study addresses the pervasive issue of inconsistent and unreliable evaluation in multimedia event extraction research, which has led to inflated performance claims and hindered reproducibility and comparability. For the first time, we systematically identify and categorize three primary sources of evaluation pitfalls: data processing biases, divergent task assumptions, and non-uniform evaluation protocols. To mitigate these issues, we propose a unified and rigorous evaluation framework that integrates controlled experimental design, multimodal data alignment strategies, and standardized assessment protocols to systematically quantify the impact of evaluation choices on model performance. Our experiments demonstrate that minor variations in evaluation settings can induce substantial performance fluctuations, revealing a significant overestimation of existing methods’ true cross-modal event understanding capabilities and thereby establishing a more reliable benchmark for future research in this field.

Abstract

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.

Problem

• multimedia event extraction
• evaluation pitfalls
• cross-modal grounding
• evaluation consistency
• performance overestimation

Innovation

• multimedia event extraction
• evaluation pitfalls
• cross-modal grounding
• rigorous evaluation
• systematic analysis