Summary of Dual-view Visual Contextualization For Web Navigation, by Jihyung Kil et al.
Dual-View Visual Contextualization for Web Navigation
by Jihyung Kil, Chan Hee Song, Boyuan Zheng, Xiang Deng, Yu Su, Wei-Lun Chao
First submitted to arxiv on: 6 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 approach in this paper aims to improve automatic web navigation by contextualizing HTML elements through their “dual views” in webpage screenshots. The authors build upon the insight that web developers tend to arrange task-related elements nearby on webpages and propose to contextualize each element with its neighbor elements using both textual and visual features. This results in more informative representations of HTML elements, which can help a web agent take action. The method is validated on the Mind2Web dataset, featuring diverse navigation domains and tasks on real-world websites. The authors’ approach consistently outperforms the baseline in various scenarios, including cross-task, cross-website, and cross-domain ones. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps build a web agent that can follow language instructions to execute complex tasks on real-world websites. Right now, most work starts with simple HTML documents, but this isn’t always enough information for the agent to make good choices. The authors have an idea to help – they take a screenshot of each webpage and use that to figure out what each part does. This is based on the way web developers arrange important elements close together to make things easier for users. By combining text and image features, the agent can get better information about what each element does. The authors tested their idea with the Mind2Web dataset and found it worked well in many different situations. |
