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Found 4 papers, 4 papers with code
Point-In-Context: Understanding Point Cloud via In-Context Learning
With the emergence of large-scale models trained on diverse datasets, in-context learning has emerged as a promising paradigm for multitasking, notably in natural language processing and image processing.
ModelNet-O: A Large-Scale Synthetic Dataset for Occlusion-Aware Point Cloud Classification
Through extensive experiments, we demonstrate that our PointMLS achieves state-of-the-art results on ModelNet-O and competitive results on regular datasets, and it is robust and effective.
Skeleton-in-Context: Unified Skeleton Sequence Modeling with In-Context Learning
Under this setting, the model can perceive tasks from prompts and accomplish them without any extra task-specific head predictions or model fine-tuning.
Explore In-Context Learning for 3D Point Cloud Understanding
With the rise of large-scale models trained on broad data, in-context learning has become a new learning paradigm that has demonstrated significant potential in natural language processing and computer vision tasks.
