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Spatial-information Guided Adaptive Context-aware Network for Efficient RGB-D Semantic Segmentation vs SegPrompt: Boosting Open-world Segmentation via Category-level Prompt Learning
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Spatial-information Guided Adaptive Context-aware Network for Efficient RGB-D Semantic Segmentation
Efficient RGB-D semantic segmentation has received considerable attention in mobile robots, which plays a vital role in analyzing and recognizing environmental information.
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AI Tool
SegPrompt: Boosting Open-world Segmentation via Category-level Prompt Learning
In this work, we propose a novel training mechanism termed SegPrompt that uses category information to improve the model's class-agnostic segmentation ability for both known and unknown categories.
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Spatial-information Guided Adaptive Context-aware Network for Efficient RGB-D Semantic Segmentation vs Wax & Pigment
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