E-VAds: An E-commerce Short Videos Understanding Benchmark for MLLMs
E-commerce short videos represent a high-revenue segment of the online video industry characterized by a goal-driven format and dense multi-modal signals. Current models often struggle with these videos because existing benchmarks focus primarily on general-purpose tasks and neglect the reasoning of commercial intent. In this work, we first propose a multi-modal information density assessment framework to quantify the complexity of this domain. Our evaluation reveals that e-commerce content exhibits substantially higher density across visual, audio, and textual modalities compared to mainstream datasets, establishing a more challenging frontier for video understanding. To address this gap...
| Authors | Xianjie Liu, Yiman Hu, Liang Wu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 9 February 2026 |
| Last updated at source | 17 June 2026 |
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