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.
Efficient RGB-D semantic segmentation has received considerable attention in mobile robots, which plays a vital role in analyzing and recognizing environmental information.
Recent leading zero-shot video object segmentation (ZVOS) works devote to integrating appearance and motion information by elaborately designing feature fusion modules and identically applying them in multiple feature stages.
Google Gemini, a multimodal AI by DeepMind, processes text, audio, images, and more. Gemini outperforms in AI benchmarks, is optimized for varied devices, and has been tested for safety and bias, adhering to responsible AI practices.
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Then transplant it to the real world to solve complex problems
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