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Found 4 papers, 3 papers with code
Fast-Poly: A Fast Polyhedral Framework For 3D Multi-Object Tracking
3D Multi-Object Tracking (MOT) captures stable and comprehensive motion states of surrounding obstacles, essential for robotic perception.
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3D Multi-Object Tracking
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OFVL-MS: Once for Visual Localization across Multiple Indoor Scenes
In this work, we seek to predict camera poses across scenes with a multi-task learning manner, where we view the localization of each scene as a new task.
Omni-Training: Bridging Pre-Training and Meta-Training for Few-Shot Learning
While pre-training and meta-training can create deep models powerful for few-shot generalization, we find that pre-training and meta-training focuses respectively on cross-domain transferability and cross-task transferability, which restricts their data efficiency in the entangled settings of domain shift and task shift.
Self-Tuning for Data-Efficient Deep Learning
Deep learning has made revolutionary advances to diverse applications in the presence of large-scale labeled datasets.
