Summary of Velociti: Can Video-language Models Bind Semantic Concepts Through Time?, by Darshana Saravanan et al.
VELOCITI: Can Video-Language Models Bind Semantic Concepts through Time?
by Darshana Saravanan, Darshan Singh, Varun Gupta, Zeeshan Khan, Vineet Gandhi, Makarand Tapaswi
First submitted to arxiv on: 16 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 research paper proposes VELOCITI, a new benchmark for evaluating video language models’ ability to understand complex video scenes and associate entities through relationships. The existing benchmarks focus on perception capabilities, but VELOCITI also assesses the model’s binding capacity by testing its ability to identify the correct entity in a given situation while ignoring other plausible entities in the same video. Current state-of-the-art models perform well on perception tests but struggle with binding tests, indicating a significant gap between human accuracy and machine learning capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary VELOCITI is a new benchmark that helps us understand how well machines can watch videos and talk about what’s happening. Right now, most machines are good at recognizing things in videos, like people or actions. But they struggle to figure out which person is doing something or which action is happening. This paper shows that current machines are not very good at this “binding” task, even though they’re great at the easier “perception” task. |
Keywords
* Artificial intelligence * Machine learning
