Summary of Rethinking 3d Dense Caption and Visual Grounding in a Unified Framework Through Prompt-based Localization, by Yongdong Luo et al.
Rethinking 3D Dense Caption and Visual Grounding in A Unified Framework through Prompt-based Localization
by Yongdong Luo, Haojia Lin, Xiawu Zheng, Yigeng Jiang, Fei Chao, Jie Hu, Guannan Jiang, Songan Zhang, Rongrong Ji
First submitted to arxiv on: 17 Apr 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 proposed 3DGCTR framework is a unified approach that jointly solves 3D Visual Grounding (3DVG) and 3D Dense Captioning (3DDC) tasks in an end-to-end fashion, inspired by DETR. It reconsiders the prompt-based localization ability of the 3DVG model to assist the 3DDC task, extracting localization information from the prompt. This integration enables simultaneous multi-task training on both tasks, enhancing their performance. The framework is evaluated on the ScanRefer dataset and outperforms state-of-the-art methods in both 3DVG and 3DDC tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to solve two important problems: recognizing objects in 3D images (3D Visual Grounding) and describing what we see in those images (3D Dense Captioning). These tasks are closely related, and the authors design a special framework that helps both tasks by sharing information. This approach does better than existing methods on these tasks. |
Keywords
» Artificial intelligence » Grounding » Multi task » Prompt
