Summary of Deisam: Segment Anything with Deictic Prompting, by Hikaru Shindo et al.
DeiSAM: Segment Anything with Deictic Prompting
by Hikaru Shindo, Manuel Brack, Gopika Sudhakaran, Devendra Singh Dhami, Patrick Schramowski, Kristian Kersting
First submitted to arxiv on: 21 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 DeiSAM, a novel approach that combines large pre-trained neural networks with differentiable logic reasoners to enable reliable interpretation of deictic representations in complex scenes. Deictic representations are natural language descriptions that rely on context, such as “The object that is on the desk and behind the cup.” Deep learning approaches struggle to interpret these descriptions due to a lack of reasoning capabilities in complex scenarios. DeiSAM leverages Large Language Models (LLMs) to generate first-order logic rules and performs differentiable forward reasoning on generated scene graphs. The approach segments objects by matching them to logically inferred image regions. To evaluate DeiSAM, the authors propose the Deictic Visual Genome (DeiVG) dataset, which contains paired visual input and complex, deictic textual prompts. Empirical results demonstrate that DeiSAM is a significant improvement over purely data-driven baselines for deictic promptable segmentation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper develops a new way to understand natural language descriptions of objects in pictures. Right now, computers can’t easily figure out what you mean when you say something like “The object that is on the desk and behind the cup.” Humans use context to understand these kinds of descriptions, but computers don’t have this ability yet. The researchers propose a new approach called DeiSAM that combines powerful computer vision models with logical reasoning abilities. This allows DeiSAM to understand complex descriptions and segment objects in pictures more accurately. To test DeiSAM, the authors created a special dataset called Deictic Visual Genome, which includes pairs of pictures and descriptive text. The results show that DeiSAM is better than other approaches at understanding deictic representations. |
Keywords
* Artificial intelligence * Deep learning
