Summary of Robin3d: Improving 3d Large Language Model Via Robust Instruction Tuning, by Weitai Kang et al.
Robin3D: Improving 3D Large Language Model via Robust Instruction Tuning
by Weitai Kang, Haifeng Huang, Yuzhang Shang, Mubarak Shah, Yan Yan
First submitted to arxiv on: 30 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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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 A novel Large Language Model called Robin3D is introduced for building general-purpose agents in the 3D real world, leveraging a large-scale instruction-following dataset generated by the Robust Instruction Generation (RIG) engine. The RIG engine produces two types of instructions: adversarial and diverse, which are used to train Robin3D to understand complex spatial relationships through Relation-Augmented Projector and object referring and grounding through ID-Feature Bonding. This approach leads to significant improvements in tasks such as grounding and captioning on five widely-used 3D multimodal learning benchmarks, without requiring task-specific fine-tuning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers create a new AI model called Robin3D that can understand and follow instructions in the 3D world. They make it better by training it with lots of data that includes different types of instructions. This helps the model learn to understand complex spatial relationships and refer to objects correctly. The result is a very good model that can do tasks like grounding (figuring out what an object is) and captioning (writing a description of something). It’s better than other models without needing extra training. |
Keywords
» Artificial intelligence » Fine tuning » Grounding » Large language model
