Summary of No Captions, No Problem: Captionless 3d-clip Alignment with Hard Negatives Via Clip Knowledge and Llms, by Cristian Sbrolli and Matteo Matteucci
No Captions, No Problem: Captionless 3D-CLIP Alignment with Hard Negatives via CLIP Knowledge and LLMs
by Cristian Sbrolli, Matteo Matteucci
First submitted to arxiv on: 4 Jun 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 study introduces two unsupervised methods for contrastive text-image-3D alignment in the absence of textual descriptions. The methods leverage CLIP knowledge about textual and 2D data to compute neural perceived similarity between two 3D samples. This is done by mining 3D hard negatives using a custom loss function, which is then used in a multimodal contrastive pipeline. The approach is evaluated on 3D classification and cross-modal retrieval benchmark, showing comparable or superior performance on zero-shot and standard 3D classification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study uses special computer algorithms to help match pictures of objects with their shapes, even if there’s no text description available. It creates a new way to do this by using information from pictures and words together, which helps the algorithm learn what makes an object look like its shape. The result is better than other methods at matching pictures of objects with their shapes. |
Keywords
» Artificial intelligence » Alignment » Classification » Loss function » Unsupervised » Zero shot
