Summary of Gomaa-geo: Goal Modality Agnostic Active Geo-localization, by Anindya Sarkar et al.
GOMAA-Geo: GOal Modality Agnostic Active Geo-localization
by Anindya Sarkar, Srikumar Sastry, Aleksis Pirinen, Chongjie Zhang, Nathan Jacobs, Yevgeniy Vorobeychik
First submitted to arxiv on: 4 Jun 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 This research proposes a novel active geo-localization (AGL) agent, GOMAA-Geo, that can efficiently locate targets in various modalities, such as natural language descriptions, aerial imagery, or ground-level views. The AGL task involves using sequential visual cues to navigate and find a target specified through multiple possible modalities. To address the challenges of goal specification and limited localization time, the agent combines cross-modality contrastive learning with supervised foundation model pretraining and reinforcement learning. Experimental results show that GOMAA-Geo outperforms alternative approaches and generalizes well across datasets and goal modalities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GOMAA-Geo is a smart system that helps find things in different ways, like using words or pictures. It uses clues from aerial views to find something, like a lost person or a building. The system has to work fast because time is limited, so it needs to be good at figuring out what the clues mean and where to look next. The researchers came up with a new way for the system to learn using different types of information and practice. This helps the system get better at finding things even when it hasn’t seen that type of thing before. |
Keywords
» Artificial intelligence » Pretraining » Reinforcement learning » Supervised
