Summary of To Ask or Not to Ask? Detecting Absence Of Information in Vision and Language Navigation, by Savitha Sam Abraham et al.
To Ask or Not to Ask? Detecting Absence of Information in Vision and Language Navigation
by Savitha Sam Abraham, Sourav Garg, Feras Dayoub
First submitted to arxiv on: 6 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 addresses a crucial gap in Vision Language Navigation (VLN) research by developing agents that can recognize when they lack sufficient information and ask clarifying questions accordingly. Specifically, the authors propose an attention-based module that learns associations between instructions and agent trajectories to estimate instruction vagueness. This enhancement enables more efficient navigation by reducing potential digressions and seeking timely assistance. The proposed module improves precision-recall balance by around 52% compared to baseline methods. Additionally, ablative experiments demonstrate the effectiveness of incorporating the instruction-to-path attention network alongside cross-modal attention networks within the navigator module. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about helping machines navigate through complex tasks while understanding when they need more information. Right now, these machines don’t know when they’re missing important details and might get stuck or ask silly questions. The researchers want to fix this by teaching machines to recognize when they need help. They developed a special tool that looks at the instructions and the machine’s actions to figure out when it’s unsure. This makes the machine more efficient and less likely to make mistakes. |
Keywords
» Artificial intelligence » Attention » Precision » Recall
