MetaphorVU: Towards Metaphorical Video Understanding
For researchers evaluating high-order cognitive capabilities of MLLMs, this work provides a systematic benchmark and reveals a critical bottleneck in metaphorical understanding.
This paper introduces MetaphorVU-Bench, the first benchmark for metaphorical video understanding, and finds that current MLLMs significantly underperform humans due to poor cross-domain mapping. The proposed MetaphorBoost framework, using a metaphor knowledge graph, yields consistent performance improvements.
Metaphorical videos are prevalent across various real-world scenarios to convey complex ideas, and understanding them typically requires high-order cognitive capabilities. The lack of systematic studies on metaphorical video understanding not only constrains the real-world applicability of MLLMs but also impedes the thorough assessment of their high-order cognitive capabilities. To bridge this gap, we propose MetaphorVU-Bench, the first systematic and comprehensive benchmark dedicated to metaphorical video understanding. Through experiments, we find current MLLMs struggle with accurate metaphorical video understanding, lagging far behind human level, primarily due to defective cross-domain mapping. Motivated by this finding, we construct a metaphor knowledge graph as mapping augmentation and propose MetaphorBoost, an inference-time enhancement framework achieving consistent performance improvement. Our benchmark, analysis, and method provide useful insights and a foundation for future research on advancing MLLMs.