ViMU: Benchmarking Video Metaphorical Understanding
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| Authors | Qi Li & Xinchao Wang |
| Year | 2026 |
| HF Upvotes | 11 |
| arXiv | 2605.14607 |
| Download | |
| Code | https://github.com/LiQiiiii/Video-Metaphorical-Understanding |
Abstract
Any new medium, once it emerges, is used for more than the transmission of overt content alone. The information it carries typically operates on two levels: one is the content directly presented, while the other is the subtext beneath it-the implicit ideas and intentions the creator seeks to convey through the medium. Likewise, since video technologies became widely adopted, video has served not only as a powerful tool for recording and communicating visual information, but also as a vehicle for emotions, attitudes, and social meanings that are often difficult to articulate explicitly. Thus, the true meaning of many videos does not reside solely in what is shown on screen; it is often embedded in context, style of expression, and the viewer's social experience. Some forms of such video subtext are humorous, while others carry irony, mockery, or criticism. These implicit meanings can also be interpreted very differently across cultural backgrounds and social groups. However, most existing video understanding models still focus primarily on literal visual comprehension, such as recognizing objects, actions, or temporal relations, and lack a systematic ability to understand the metaphorical, ironic, and social meanings embedded in videos. To bridge this gap, we introduce ViMU, the first benchmark designed to systematically evaluate the subtext understanding capabilities of frontier models in videos. ViMU assesses whether video understanding models can go beyond literal perception to infer implicit meaning while grounding their interpretations in multimodal evidence and answering both open-ended and multiple-choice questions. Importantly, all questions are designed to be hint-free, ensuring that no key evidence is disclosed to models before answering.
Engineering Breakdown
Plain English
This paper introduces ViMU, a benchmark for evaluating how well AI models understand metaphorical and implicit meanings in videos—not just what's literally shown on screen, but the emotional subtext, social context, and artistic intent. The work addresses a gap in video understanding research: current benchmarks measure object detection and action recognition, but most videos communicate meaning through context and subtlety that standard metrics miss.
Key Engineering Insight
Video understanding requires models to capture multi-level semantics (literal content plus implicit meaning), which means single-task vision models trained on frame-level classification will underperform. Production systems need architectures that jointly model visual content, temporal context, and semantic relationships to handle real-world video interpretation tasks.
Why It Matters for Engineers
If you're building video search, content moderation, or video-to-text systems, your model probably works well on literal content but fails on nuanced videos—ads, social media, documentaries where meaning is conveyed through metaphor and implication. This benchmark lets you stress-test your system against cases where the real value is in understanding what's implied, not just what's shown, which is critical for user-facing products.
Research Context
Prior video benchmarks (like UCF101, Kinetics) focused on action recognition and object detection—engineering-friendly but semantically shallow. This paper extends video understanding beyond recognition tasks to interpretation tasks, similar to how VQA and image captioning forced vision models to reason about relationships rather than just classify. ViMU enables training and evaluation of models that can handle the semantic complexity of real-world video, not just academic lab conditions.
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