Summary of Clevr-poc: Reasoning-intensive Visual Question Answering in Partially Observable Environments, by Savitha Sam Abraham and Marjan Alirezaie and Luc De Raedt
CLEVR-POC: Reasoning-Intensive Visual Question Answering in Partially Observable Environments
by Savitha Sam Abraham, Marjan Alirezaie, Luc De Raedt
First submitted to arxiv on: 5 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 A novel benchmark called CLEVR-POC is introduced for reasoning-intensive visual question answering (VQA) in partially observable environments under constraints. This requires leveraging background knowledge in the form of logical constraints to generate plausible answers to questions about a hidden object. Pre-trained vision language models like CLIP and large language models like GPT-4 struggle on CLEVR-POC, demonstrating the need for frameworks that can handle reasoning-intensive tasks with environment-specific background knowledge. A neuro-symbolic model integrating an LLM with visual perception and formal logical reasoner exhibits exceptional performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Humans use existing knowledge to answer questions about scenes. The paper introduces a benchmark called CLEVR-POC, where you need to use constraints about objects to answer questions about hidden objects. It’s like figuring out what color an occluded cup is based on what you can see of other cups. Current models don’t do well on this task, showing the importance of frameworks that can handle reasoning and background knowledge. The paper shows a special model that does really well by combining language understanding with visual perception and logical thinking. |
Keywords
* Artificial intelligence * Gpt * Language understanding * Question answering
