Summary of Evaluating Zero-shot Gpt-4v Performance on 3d Visual Question Answering Benchmarks, by Simranjit Singh et al.
Evaluating Zero-Shot GPT-4V Performance on 3D Visual Question Answering Benchmarks
by Simranjit Singh, Georgios Pavlakos, Dimitrios Stamoulis
First submitted to arxiv on: 29 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 medium-difficulty summary of this paper assesses the zero-shot performance of foundational models on well-established 3D Visual Question Answering (VQA) benchmarks. Specifically, it evaluates GPT-4 Vision and GPT-4 on 3D-VQA and ScanQA, comparing their performance to traditional modeling approaches. The study finds that GPT-based agents without fine-tuning perform similarly to closed-vocabulary approaches, corroborating recent results. It also demonstrates the benefits of scene-specific vocabulary via in-context textual grounding, informing ongoing efforts to refine multi-modal 3D benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper studies how new “reformulating” methods affect existing 3D VQA datasets. Researchers tested GPT-based models on two benchmark tests: 3D-VQA and ScanQA. They found that these models worked just as well without extra training, which is a surprising result! The study also showed that adding special words for specific scenes helps the models understand what’s happening better. |
Keywords
» Artificial intelligence » Fine tuning » Gpt » Grounding » Multi modal » Question answering » Zero shot
