Abstract
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 \textbf{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, we introduce \textbf{E-commerce Video Ads Benchmark (E-VAds)}, which is the first benchmark specifically designed for e-commerce short video understanding. We curated 3,961 high-quality videos from Taobao covering a wide range of product categories and used a multi-agent system to generate 19,785 open-ended Q&A pairs. These questions are organized into two primary dimensions, namely Perception and Cognition and Reasoning, which consist of five distinct tasks. Finally, we develop \textbf{E-VAds-R1}, an RL-based reasoning model featuring a multi-grained reward design called \textbf{MG-GRPO}. This strategy provides smooth guidance for early exploration while creating a non-linear incentive for expert-level precision. Experimental results demonstrate that E-VAds-R1 achieves a 109.2% performance gain in commercial intent reasoning with only a few hundred training samples.
Abstract (translated)
电子商务短视频代表了在线视频行业中高收入的一部分，其特点是目标导向的格式和密集多模态信号。目前的模型在处理这些视频时常常遇到困难，因为现有的基准测试主要关注通用任务，并忽视了商业意图推理。在这项工作中，我们首先提出了一种**多模态信息密度评估框架**来量化该领域的复杂性。我们的评估表明，与主流数据集相比，电子商务内容在视觉、音频和文本模式上表现出更高的密集度，为视频理解建立了一个更具挑战性的前沿领域。 为了弥补这一差距，我们引入了**电子商务视频广告基准（E-VAds）**，这是首个专门针对电子商务短视频理解设计的基准测试。我们从淘宝收集了3,961条高质量视频，涵盖了广泛的商品类别，并使用多代理系统生成了19,785对开放式问答。这些问题按照感知和认知及推理两个主要维度组织，并包括五个不同的任务。 最后，我们开发了**E-VAds-R1**，这是一个基于强化学习（RL）的推理模型，具有称为**MG-GRPO**的多粒度奖励设计策略。这种策略为早期探索提供了平滑指导，同时创造了一个非线性的激励机制以达到专家级精度水平。实验结果表明，在只有几百个训练样本的情况下，E-VAds-R1在商业意图推理上实现了109.2%的性能提升。
URL
https://arxiv.org/abs/2602.08355
