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WorldSense: Evaluating Real-world Omnimodal Understanding for Multimodal LLMs
🤖AI Summary
Researchers have introduced WorldSense, the first benchmark for evaluating multimodal AI systems that process visual, audio, and text inputs simultaneously. The benchmark contains 1,662 synchronized audio-visual videos across 67 subcategories and 3,172 QA pairs, revealing that current state-of-the-art models achieve only 65.1% accuracy on real-world understanding tasks.
Key Takeaways
- →WorldSense is the first comprehensive benchmark to evaluate AI models on combined visual, audio, and text understanding.
- →The benchmark includes 1,662 audio-visual videos categorized into 8 domains and 67 subcategories with expert-annotated QA pairs.
- →Current state-of-the-art multimodal AI models achieve only 65.1% accuracy, indicating significant room for improvement.
- →The benchmark requires strong coupling between audio and video inputs, testing true omnimodal perception capabilities.
- →All annotations were manually created by 80 expert annotators with multiple quality control rounds.
Read Original →via arXiv – CS AI
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