DiMP uses diffusion to infer clean masked positions from visible context and to model full distributions of point displacements rather than means, delivering 11.21% and 13.65% absolute gains on offline and online action segmentation.
Pointrft: Explicit reinforcement fine-tuning for point cloud few-shot learning
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Mantis is the first Mamba-native PEFT framework for 3D point cloud models, using state-aware adapters and dual-serialization distillation to match performance with only 5% trainable parameters.
CFMS is a coarse-to-fine framework that uses MLLMs to create a multi-perspective knowledge tuple as a reasoning map for symbolic table operations, yielding competitive accuracy on WikiTQ and TabFact.
citing papers explorer
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Diffusion Masked Pretraining for Dynamic Point Cloud
DiMP uses diffusion to infer clean masked positions from visible context and to model full distributions of point displacements rather than means, delivering 11.21% and 13.65% absolute gains on offline and online action segmentation.
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Mantis: Mamba-native Tuning is Efficient for 3D Point Cloud Foundation Models
Mantis is the first Mamba-native PEFT framework for 3D point cloud models, using state-aware adapters and dual-serialization distillation to match performance with only 5% trainable parameters.
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CFMS: A Coarse-to-Fine Multimodal Synthesis Framework for Enhanced Tabular Reasoning
CFMS is a coarse-to-fine framework that uses MLLMs to create a multi-perspective knowledge tuple as a reasoning map for symbolic table operations, yielding competitive accuracy on WikiTQ and TabFact.