Summary of Bridging the Gap Between 2d and 3d Visual Question Answering: a Fusion Approach For 3d Vqa, by Wentao Mo et al.
Bridging the Gap between 2D and 3D Visual Question Answering: A Fusion Approach for 3D VQA
by Wentao Mo, Yang Liu
First submitted to arxiv on: 24 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); 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 The proposed BridgeQA model tackles the challenges in 3D Visual Question Answering (VQA) by introducing a question-conditional 2D view selection procedure and a two-branch Transformer structure that combines 2D and 3D modalities. This approach allows for fine-grained correlations between modalities, enabling mutual augmentation. By leveraging this mechanism, BridgeQA achieves state-of-the-art performance on 3D-VQA datasets and outperforms existing solutions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary BridgeQA helps to answer questions about 3D scenes by using a special way of looking at the scene (called 2D view selection) that depends on the question. It then uses this information to help its answers, along with a special kind of computer model called a Transformer. This makes it really good at answering questions about 3D scenes. |
Keywords
* Artificial intelligence * Question answering * Transformer
