Summary of Mm-mixing: Multi-modal Mixing Alignment For 3d Understanding, by Jiaze Wang et al.
MM-Mixing: Multi-Modal Mixing Alignment for 3D Understanding
by Jiaze Wang, Yi Wang, Ziyu Guo, Renrui Zhang, Donghao Zhou, Guangyong Chen, Anfeng Liu, Pheng-Ann Heng
First submitted to arxiv on: 28 May 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 We present MM-Mixing, a novel framework for 3D object recognition that leverages multi-modal data. The approach combines feature-level and input-level mixing techniques to optimize the 3D encoder, improving alignment across modalities while promoting diversity and generalization. Our proposed two-stage training pipeline employs contrastive learning to align 3D features with their corresponding modalities. MM-Mixing demonstrates significant improvements in various learning scenarios, including zero-shot 3D classification, linear probing 3D classification, and cross-modal 3D shape retrieval. Notably, we achieve a 10.6% increase in zero-shot classification accuracy on ScanObjectNN from 51.3% to 61.9%, and a 4.6% improvement on Objaverse-LVIS from 46.8% to 51.4%. Our findings highlight the potential of multi-modal mixing-based alignment for advancing 3D object recognition. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to help computers understand 3D objects better. It uses special techniques to mix and match different types of data, like images and point clouds, to improve how well computers can recognize 3D shapes. The approach is designed to be easy to implement and integrate into existing computer vision systems. By combining different types of data, MM-Mixing allows computers to learn more about 3D objects and how they relate to each other. This has the potential to greatly improve our ability to recognize and understand 3D objects. |
Keywords
» Artificial intelligence » Alignment » Classification » Encoder » Generalization » Multi modal » Zero shot
