Seek to Segment: Active Perception for Panoramic Referring Segmentation

2026-07-02 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Computer Vision · Depth: Expert, quick

Summary

PanoSeeker introduces Active Panoramic Referring Segmentation (APRS), a novel task addressing the limitations of passive referring segmentation models in Embodied AI. Unlike static image processing, APRS requires an agent to actively adjust its viewing direction (Δθ, Δφ) to explore 360° environments, locate a user-specified object, and segment it. PanoSeeker, a memory-augmented agent, tackles this by integrating a Vision-Language Model (VLM) with EgoSphere, an explicit spatial visual memory. EgoSphere builds a unified 360° representation from sequential observations, enabling efficient, non-redundant search trajectories. The agent then performs active viewpoint alignment and outputs the segmentation mask. Trained with an expert-annotated dataset via Supervised Fine-Tuning and Reinforcement Learning, PanoSeeker demonstrates superior search efficiency and segmentation accuracy on the new APRS benchmark, outperforming adapted baselines.

Key takeaway

For Robotics Engineers developing embodied AI agents for complex, dynamic environments, PanoSeeker's approach to Active Panoramic Referring Segmentation offers a critical advancement. You should consider integrating explicit spatial visual memory like EgoSphere with Vision-Language Models to enable active, non-redundant exploration and precise object segmentation. This method significantly improves search efficiency and accuracy in 360° settings, moving beyond passive perception limitations.

Key insights

Active Panoramic Referring Segmentation (APRS) enables embodied agents to actively explore 360° environments for object segmentation using memory-augmented perception.

Principles

• Integrating VLM with spatial memory optimizes exploration.
• Unified 360° representation prevents redundant searches.
• Reinforcement Learning refines exploration efficiency.

Method

PanoSeeker integrates a VLM with EgoSphere, an explicit spatial visual memory, to build a unified 360° representation from sequential observations. This enables planning efficient search trajectories, followed by active viewpoint alignment and segmentation.

In practice

• Implement EgoSphere for 360° scene understanding.
• Combine VLM with spatial memory for active search.
• Utilize RL for optimizing agent exploration.

Topics

• Active Perception
• Referring Segmentation
• Embodied AI
• Vision-Language Models
• Spatial Memory
• Reinforcement Learning
• Panoramic Environments

Best for: Research Scientist, AI Scientist, Computer Vision Engineer, Robotics Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.