Seek to Segment: Active Perception for Panoramic Referring Segmentation
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 ag
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- Linked via arxiv authorSong Tang →
Seek to Segment: Active Perception for Panoramic Referring Segmentation
- Linked via arxiv authorShuming Hu →
Seek to Segment: Active Perception for Panoramic Referring Segmentation
- Linked via arxiv authorXincheng Shuai →
Seek to Segment: Active Perception for Panoramic Referring Segmentation
- Linked via arxiv authorHenghui Ding →
Seek to Segment: Active Perception for Panoramic Referring Segmentation
- Linked via arxiv authorYu-Gang Jiang →
Seek to Segment: Active Perception for Panoramic Referring Segmentation