Summary of Unlocking Textual and Visual Wisdom: Open-vocabulary 3d Object Detection Enhanced by Comprehensive Guidance From Text and Image, By Pengkun Jiao et al.
Unlocking Textual and Visual Wisdom: Open-Vocabulary 3D Object Detection Enhanced by Comprehensive Guidance from Text and Image
by Pengkun Jiao, Na Zhao, Jingjing Chen, Yu-Gang Jiang
First submitted to arxiv on: 7 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposes Open-vocabulary 3D object detection (OV-3DDet), a task that aims to localize and recognize both seen and unseen object categories within any new 3D scene. While foundation models have achieved success in handling various open-vocabulary tasks, OV-3DDet faces the challenge of limited training data. The paper leverages language and vision foundation models to tackle this challenge, using a vision foundation model to provide image-wise guidance for discovering novel classes. Specifically, it uses an object detection vision foundation model to enable zero-shot discovery of objects in images, serving as initial seeds and filtering guidance. Additionally, the paper introduces a hierarchical alignment approach to align the 3D space with the powerful vision-language space using a pre-trained VLM at instance, category, and scene levels. Through experimentation, it demonstrates significant improvements in accuracy and generalization, highlighting the potential of foundation models in advancing open-vocabulary 3D object detection. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using artificial intelligence to find and recognize objects in 3D scenes, even if they’ve never been seen before. This is a big challenge because there’s limited training data available. The researchers use special kinds of AI models that combine language and vision abilities to solve this problem. They use one type of model to help discover new objects and another type of model to align the 3D space with the powerful language-visual space. By doing this, they’re able to improve accuracy and generalization, making it more useful in real-world scenarios. |
Keywords
» Artificial intelligence » Alignment » Generalization » Object detection » Zero shot
