Summary of Delan: Dual-level Alignment For Vision-and-language Navigation by Cross-modal Contrastive Learning, By Mengfei Du et al.
DELAN: Dual-Level Alignment for Vision-and-Language Navigation by Cross-Modal Contrastive Learning
by Mengfei Du, Binhao Wu, Jiwen Zhang, Zhihao Fan, Zejun Li, Ruipu Luo, Xuanjing Huang, Zhongyu Wei
First submitted to arxiv on: 2 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 This paper proposes a novel framework called Dual-levEL AligNment (DELAN) to improve vision-and-Language navigation (VLN) by aligning various modalities before fusion. The DELAN framework uses cross-modal contrastive learning to enhance cross-modal interaction and action decision-making in VLN tasks, such as R2R, R4R, RxR, and CVDN benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps agents navigate unseen environments by following natural language instructions more effectively. It creates a new way of combining information from different sources, like what the agent is told to do, what it sees, and what it has done before. This lets the agent make better decisions and take more accurate actions. |
Keywords
* Artificial intelligence * Alignment
