DiffVAS: Diffusion-Guided Visual Active Search in Partially Observable Environments

• URL: http://arxiv.org/abs/2605.15519v1
• Date: Fri, 15 May 2026 01:30:07 GMT
• Title: DiffVAS: Diffusion-Guided Visual Active Search in Partially Observable Environments
• Abstract summary: DiffVAS is a target-conditioned policy that searches for diverse objects simultaneously according to task requirements in partially observable environments.<n>It excels in searching diverse objects in partially observable environments, significantly surpassing state-of-the-art methods on several datasets.
• Score: 38.91206454694548
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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. Potential applications of VAS include detecting hotspots for rare wildlife poaching, aiding search-and-rescue missions, and uncovering illegal trafficking of weapons, among other uses. Previous VAS approaches assume that the entire search space is known upfront, which is often unrealistic due to constraints such as a restricted field of view and high acquisition costs, and they typically learn policies tailored to specific target objects, which limits their ability to search for multiple target categories simultaneously. In this work, we propose DiffVAS, a target-conditioned policy that searches for diverse objects simultaneously according to task requirements in partially observable environments, which advances the deployment of visual active search policies in real-world applications. DiffVAS leverages a diffusion model to reconstruct the entire geospatial area from sequentially observed partial glimpses, which enables a target-conditioned reinforcement learning-based planning module to effectively reason and guide subsequent search steps. Extensive experiments demonstrate that DiffVAS excels in searching diverse objects in partially observable environments, significantly surpassing state-of-the-art methods on several datasets.

Related papers

• Generalization-Enhanced Few-Shot Object Detection in Remote Sensing [22.411751110592842]
  Few-shot object detection (FSOD) targets object detection challenges in data-limited conditions.<n>We propose the Generalization-Enhanced Few-Shot Object Detection (GE-FSOD) model to improve the generalization capability in remote sensing tasks.<n>Our model introduces three key innovations: the Cross-Level Fusion Pyramid Attention Network (CFPAN), the Multi-Stage Refinement Region Proposal Network (MRRPN), and the Generalized Classification Loss (GCL)
  arXiv  Detail & Related papers  (2025-01-05T08:12:25Z)
• Action abstractions for amortized sampling [49.384037138511246]
  We propose an approach to incorporate the discovery of action abstractions, or high-level actions, into the policy optimization process. Our approach involves iteratively extracting action subsequences commonly used across many high-reward trajectories and chunking' them into a single action that is added to the action space.
  arXiv  Detail & Related papers  (2024-10-19T19:22:50Z)
• GOMAA-Geo: GOal Modality Agnostic Active Geo-localization [49.599465495973654]
  We consider the task of active geo-localization (AGL) in which an agent uses a sequence of visual cues observed during aerial navigation to find a target specified through multiple possible modalities. GOMAA-Geo is a goal modality active geo-localization agent for zero-shot generalization between different goal modalities.
  arXiv  Detail & Related papers  (2024-06-04T02:59:36Z)
• Semantic-Based Active Perception for Humanoid Visual Tasks with Foveal Sensors [49.99728312519117]
  The aim of this work is to establish how accurately a recent semantic-based active perception model is able to complete visual tasks that are regularly performed by humans. This model exploits the ability of current object detectors to localize and classify a large number of object classes and to update a semantic description of a scene across multiple fixations. In the task of scene exploration, the semantic-based method demonstrates superior performance compared to the traditional saliency-based model.
  arXiv  Detail & Related papers  (2024-04-16T18:15:57Z)
• A Partially Supervised Reinforcement Learning Framework for Visual Active Search [36.966522001393734]
  Visual active search (VAS) has been proposed as a modeling framework in which visual cues are used to guide exploration. We propose an approach that combines the strength of both DRL and conventional active search by decomposing the search policy into a prediction module. We develop a novel meta-learning approach for jointly learning the resulting combined policy that can make effective use of supervised information obtained both at training and decision time.
  arXiv  Detail & Related papers  (2023-10-15T00:29:35Z)
• ECEA: Extensible Co-Existing Attention for Few-Shot Object Detection [52.16237548064387]
  Few-shot object detection (FSOD) identifies objects from extremely few annotated samples. Most existing FSOD methods, recently, apply the two-stage learning paradigm, which transfers the knowledge learned from abundant base classes to assist the few-shot detectors by learning the global features. We propose an Extensible Co-Existing Attention (ECEA) module to enable the model to infer the global object according to the local parts.
  arXiv  Detail & Related papers  (2023-09-15T06:55:43Z)
• A Visual Active Search Framework for Geospatial Exploration [36.31732056074638]
  Many problems can be viewed as forms of geospatial search aided by aerial imagery. We model this class of problems in a visual active search (VAS) framework, which has three key inputs. We propose a reinforcement learning approach for VAS that learns a meta-search policy from a collection of fully annotated search tasks.
  arXiv  Detail & Related papers  (2022-11-28T21:53:05Z)
• Aerial View Goal Localization with Reinforcement Learning [6.165163123577484]
  We present a framework that emulates a search-and-rescue (SAR)-like setup without requiring access to actual UAVs. In this framework, an agent operates on top of an aerial image (proxy for a search area) and is tasked with localizing a goal that is described in terms of visual cues. We propose AiRLoc, a reinforcement learning (RL)-based model that decouples exploration (searching for distant goals) and exploitation (localizing nearby goals)
  arXiv  Detail & Related papers  (2022-09-08T10:27:53Z)
• Deep Learning for UAV-based Object Detection and Tracking: A Survey [25.34399619170044]
  Unmanned aerial vehicle (UAV) has recently become a hotspot across the fields of computer vision (CV) and remote sensing (RS) Inspired by recent success of deep learning (DL), many advanced object detection and tracking approaches have been widely applied to various UAV-related tasks. This paper provides a comprehensive survey on the research progress and prospects of DL-based UAV object detection and tracking methods.
  arXiv  Detail & Related papers  (2021-10-25T04:43:24Z)
• Batch Exploration with Examples for Scalable Robotic Reinforcement Learning [63.552788688544254]
  Batch Exploration with Examples (BEE) explores relevant regions of the state-space guided by a modest number of human provided images of important states. BEE is able to tackle challenging vision-based manipulation tasks both in simulation and on a real Franka robot.
  arXiv  Detail & Related papers  (2020-10-22T17:49:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.