Seek to Segment: Active Perception for Panoramic Referring Segmentation

• URL: http://arxiv.org/abs/2607.02497v1
• Date: Thu, 02 Jul 2026 17:56:49 GMT
• Title: Seek to Segment: Active Perception for Panoramic Referring Segmentation
• Abstract summary: Existing referring segmentation models passively process static images captured from fixed perspectives.<n>We propose PanoSeeker, a memory-augmented agent for efficient Active Panoramic Referring.<n>Experiments on our newly established APRS benchmark demonstrate that PanoSeeker achieves superior search efficiency and segmentation accuracy.
• Score: 76.39419654494505
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing referring segmentation models passively process static images captured from fixed perspectives, limiting their applicability in Embodied AI, where agents must perform active perception in the continuous 360$^\circ$ environments. To bridge this gap, we introduce a novel task: Active Panoramic Referring Segmentation (APRS). In this setting, an agent is required to adjust its viewing direction ($Δθ, Δφ$) to explore the 360$^\circ$ environment, seeking the object specified by a user instruction for segmentation. To tackle this challenging task, we propose PanoSeeker, a memory-augmented agent for efficient APRS. Rather than relying on heuristic scanning, PanoSeeker integrates a Vision-Language Model (VLM) with EgoSphere, an explicit spatial visual memory. By progressively integrating sequential local observations into a unified 360$^\circ$ representation, EgoSphere enables the agent to plan efficient and non-redundant search trajectories. Once the target is found, the agent performs active viewpoint alignment and outputs the segmentation mask. Furthermore, we curate an expert-annotated search trajectory dataset with memory timelines for Supervised Fine-Tuning, followed by Reinforcement Learning post-training to explicitly optimize PanoSeeker's exploration efficiency. Extensive experiments on our newly established APRS benchmark demonstrate that PanoSeeker achieves superior search efficiency and segmentation accuracy, significantly outperforming adapted state-of-the-art baselines.

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