Summary of Multi-modal Relation Distillation For Unified 3d Representation Learning, by Huiqun Wang et al.
Multi-modal Relation Distillation for Unified 3D Representation Learning
by Huiqun Wang, Yiping Bao, Panwang Pan, Zeming Li, Xiao Liu, Ruijie Yang, Di Huang
First submitted to arxiv on: 19 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 Multi-modal Relation Distillation (MRD) is a pre-training framework that aims to capture intricate structural relations among 3D point clouds, their corresponding 2D images, and language descriptions. Building on recent advancements in multi-modal pre-training, MRD distills reputable large Vision-Language Models (VLMs) into 3D backbones to produce more discriminative shape representations. This approach achieves significant improvements in zero-shot classification tasks and cross-modality retrieval tasks, setting new state-of-the-art performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Recent research has made progress in aligning features across 3D shapes, images, and language descriptions. However, this progress often overlooks important structural relations among samples. A new method called Multi-modal Relation Distillation (MRD) aims to solve this problem by creating a framework that captures both intra-relations within each modality and cross-relations between different modalities. This helps produce better representations of 3D shapes. The result is better performance in tasks like classifying shapes and retrieving information from different sources. |
Keywords
* Artificial intelligence * Classification * Distillation * Multi modal * Zero shot
