Summary of Black Swan: Abductive and Defeasible Video Reasoning in Unpredictable Events, by Aditya Chinchure et al.
Black Swan: Abductive and Defeasible Video Reasoning in Unpredictable Events
by Aditya Chinchure, Sahithya Ravi, Raymond Ng, Vered Shwartz, Boyang Li, Leonid Sigal
First submitted to arxiv on: 7 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 paper investigates the abilities of vision-language models (VLMs) in commonsense reasoning, particularly in abductive and defeasible reasoning. Current benchmarks focus on typical visual scenarios, making it unclear whether VLM performance stems from keen perception or statistical recall. To gain insights into VLM core capabilities, the authors propose BlackSwanSuite, a benchmark that evaluates VLMs’ ability to reason about unexpected events through abductive and defeasible tasks. The suite consists of 4,900 generative, 6,700 yes/no, and 3,800 multiple-choice tasks across 1,655 videos. Extensive evaluations of state-of-the-art VLMs, including GPT-4o and Gemini 1.5 Pro, as well as open-source VLMs like LLaVA-Video, reveal significant performance gaps from humans (up to 32%). The findings highlight the need for enhanced model architectures and training strategies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how well artificial intelligence models called vision-language models can understand unexpected events. Most tests only look at typical situations, making it unclear whether the models are really understanding what they’re seeing or just remembering previous experiences. The authors created a new test that challenges these models to think about unusual events and come up with their own explanations. They tested many different models on this task and found that even the best ones still struggled to understand unexpected events as well as humans do. |
Keywords
» Artificial intelligence » Gemini » Gpt » Recall
