DiffVAS: Diffusion-Guided Visual Active Search in Partially Observable Environments
The paper introduces DiffVAS, a novel approach to visual active search in partially observable environments. This method enhances the ability to search for diverse objects simultaneously, addressing limitations of previous models that assumed complete knowledge of the search space. Extensive experiments show that DiffVAS outperforms existing methods in various datasets, making it suitable for real-world applications such as wildlife protection and search-and-rescue missions.
- ▪DiffVAS is a target-conditioned policy designed for searching diverse objects in partially observable environments.
- ▪The method uses a diffusion model to reconstruct geospatial areas from partial observations.
- ▪DiffVAS significantly surpasses state-of-the-art methods in extensive experiments across several datasets.
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Computer Science > Computer Vision and Pattern Recognition arXiv:2605.15519 (cs) [Submitted on 15 May 2026] Title:DiffVAS: Diffusion-Guided Visual Active Search in Partially Observable Environments Authors:Anindya Sarkar, Srikumar Sastry, Aleksis Pirinen, Nathan Jacobs, Yevgeniy Vorobeychik View a PDF of the paper titled DiffVAS: Diffusion-Guided Visual Active Search in Partially Observable Environments, by Anindya Sarkar and 4 other authors View PDF HTML (experimental) Abstract:Visual active search (VAS) has been introduced as a modeling framework that leverages visual cues to direct aerial (e.g., UAV-based) exploration and pinpoint areas of interest within extensive geospatial regions.
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