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.
Efficient RGB-D semantic segmentation has received considerable attention in mobile robots, which plays a vital role in analyzing and recognizing environmental information.
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.
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.
Cerelyze - Enabling engineers to rapidly reproduce scientific research
Video ReTalking, advanced real-world talking head video according to input audio, producing a high-quality
Then transplant it to the real world to solve complex problems
Quick compare routes for nearby alternatives.
Compare Isomer: Isomerous Transformer for Zero-shot Video Object Segmentation with Spatial-information Guided Adaptive Context-aware Network for Efficient RGB-D Semantic Segmentation and jump into the preserved compare route.
Open compare route →Compare Isomer: Isomerous Transformer for Zero-shot Video Object Segmentation with SegPrompt: Boosting Open-world Segmentation via Category-level Prompt Learning and jump into the preserved compare route.
Open compare route →Compare Isomer: Isomerous Transformer for Zero-shot Video Object Segmentation with Free Google Gemini: the best largest and most capable AI model and jump into the preserved compare route.
Open compare route →