Summary of Humanvbench: Exploring Human-centric Video Understanding Capabilities Of Mllms with Synthetic Benchmark Data, by Ting Zhou et al.
HumanVBench: Exploring Human-Centric Video Understanding Capabilities of MLLMs with Synthetic Benchmark Data
by Ting Zhou, Daoyuan Chen, Qirui Jiao, Bolin Ding, Yaliang Li, Ying Shen
First submitted to arxiv on: 23 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed benchmark, HumanVBench, addresses the limitations of existing benchmarks in evaluating Multimodal Large Language Models (MLLMs) for human-centric video understanding. By focusing on nuances such as emotions, behaviors, and speech-visual alignment, HumanVBench comprises 16 carefully designed tasks that explore two primary dimensions: inner emotion and outer manifestations. The benchmark utilizes advanced automated pipelines for video annotation and distractor-included QA generation to streamline data synthesis and quality assessment, minimizing human annotation dependency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary For curious learners or general audiences, this paper is about creating a new way to test how well AI models can understand human emotions and behaviors in videos. Currently, most tests focus on recognizing objects and actions in videos, but this benchmark aims to bridge the gap by testing more complex aspects like facial expressions, body language, and speech patterns. By doing so, we hope to create AI models that better understand humans and can be used for real-world applications. |
Keywords
» Artificial intelligence » Alignment
