Summary of Is Contrastive Distillation Enough For Learning Comprehensive 3d Representations?, by Yifan Zhang et al.
Is Contrastive Distillation Enough for Learning Comprehensive 3D Representations?
by Yifan Zhang, Junhui Hou
First submitted to arxiv on: 12 Dec 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 a new framework, CMCR, for learning effective 3D representations through cross-modal contrastive distillation. Existing methods focus on modality-shared features, neglecting modality-specific features during pre-training, leading to suboptimal representations. CMCR improves upon traditional methods by integrating both modality-shared and modality-specific features. It introduces masked image modeling and occupancy estimation tasks to learn comprehensive modality-specific features and proposes a multi-modal unified codebook that learns an embedding space shared across modalities. Additionally, it introduces geometry-enhanced masked image modeling to boost 3D representation learning. The method consistently outperforms existing image-to-LiDAR contrastive distillation methods in downstream tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about finding better ways to learn 3D representations from different types of data. Right now, the best methods focus on what’s common between these types of data, but they don’t pay much attention to what’s unique about each one. The new method, called CMCR, tries to fix this by learning more about both the shared and specific features. It does this by giving the network some extra tasks to do, like filling in missing parts of an image or estimating how full a space is. This helps the network learn more complete and useful 3D representations. The results show that CMCR is better than existing methods at doing things like recognizing objects in 3D images. |
Keywords
» Artificial intelligence » Attention » Distillation » Embedding space » Multi modal » Representation learning
