Summary of Neuro-symbolic Evaluation Of Text-to-video Models Using Formal Verification, by S. P. Sharan et al.
Neuro-Symbolic Evaluation of Text-to-Video Models using Formal Verification
by S. P. Sharan, Minkyu Choi, Sahil Shah, Harsh Goel, Mohammad Omama, Sandeep Chinchali
First submitted to arxiv on: 22 Nov 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 This paper introduces a novel synthetic video evaluation metric called NeuS-V, which rigorously assesses text-to-video alignment using neuro-symbolic formal verification techniques. The authors highlight that existing metrics prioritize visual quality and smoothness over temporal fidelity and text-to-video alignment, which is crucial for safety-critical applications. They present a dataset of temporally extended prompts to evaluate state-of-the-art video generation models against their benchmark, finding that NeuS-V demonstrates a higher correlation by over 5x with human evaluations compared to existing metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it possible to check if videos are aligned correctly with what someone said. This is important because sometimes videos might not match up with the audio, which can be confusing or even dangerous in situations like self-driving cars. The authors create a new way to measure how well videos align with text using special techniques that involve formal verification and automata theory. They also test their method on current video generation models and find that they don’t do very well when faced with complex prompts. |
Keywords
» Artificial intelligence » Alignment
