Summary of Spatialvlm: Endowing Vision-language Models with Spatial Reasoning Capabilities, by Boyuan Chen et al.
SpatialVLM: Endowing Vision-Language Models with Spatial Reasoning Capabilities
by Boyuan Chen, Zhuo Xu, Sean Kirmani, Brian Ichter, Danny Driess, Pete Florence, Dorsa Sadigh, Leonidas Guibas, Fei Xia
First submitted to arxiv on: 22 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG); Robotics (cs.RO)
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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 The paper aims to improve the spatial reasoning capabilities of Vision Language Models (VLMs) for tasks like Visual Question Answering (VQA) and robotics. VLMs have excelled in certain benchmarks, but struggle with 3D spatial reasoning, such as recognizing distances or size differences between physical objects. The authors hypothesize that this limitation is due to the lack of 3D spatial knowledge in training data. To address this issue, they propose a system for training VLMs on internet-scale spatial reasoning data. They develop an automatic framework for generating large-scale VQA examples based on real-world images and investigate various factors influencing the training process. The authors present the first internet-scale 3D spatial reasoning dataset in metric space and demonstrate significant improvements in VLM performance on both qualitative and quantitative spatial VQA tasks. Finally, they show that this trained VLM enables novel applications in chain-of-thought spatial reasoning and robotics due to its quantitative estimation capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps robots and computers better understand spatial relationships by training special computer models. These models, called Vision Language Models (VLMs), are good at answering questions about pictures, but struggle with understanding distances and sizes between objects. The authors think this is because the models don’t have enough information about 3D spaces. To fix this, they created a system that lets VLMs learn from huge amounts of data showing how objects relate in space. They also made a big database of examples for this training process. By doing this, they improved the VLM’s ability to understand spatial relationships and even opened up new possibilities for robots and computers to reason about physical spaces. |
Keywords
* Artificial intelligence * Question answering
