Summary of Open-world Visual Reasoning by a Neuro-symbolic Program Of Zero-shot Symbols, By Gertjan Burghouts et al.
Open-World Visual Reasoning by a Neuro-Symbolic Program of Zero-Shot Symbols
by Gertjan Burghouts, Fieke Hillerström, Erwin Walraven, Michael van Bekkum, Frank Ruis, Joris Sijs, Jelle van Mil, Judith Dijk
First submitted to arxiv on: 18 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 This paper proposes a novel approach to solving the problem of identifying spatial configurations of multiple objects in images. By combining neuro-symbolic programming with language-vision models, the authors aim to locate objects such as abandoned tools on floors and leaking pipes. The methodology involves defining the spatial configuration using first-order logic and then matching logic formulas to probabilistic object proposals generated by language-vision models. This hybrid approach has been shown to be effective in an open-world setting, with most prediction errors attributed to biases in the language-vision model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a robot that can find lost tools on a factory floor or identify pipes leaking water. This paper explains how to make such robots possible by combining two powerful technologies: language and vision. The authors create a special program that uses rules and logic to identify objects in images, similar to how we use words to describe things. They show that this approach can be very effective at finding objects in real-world scenarios. |
