A two-stage framework enables multimodal LLMs to learn shared latent representations from pairwise modality data and achieve cross-modal generation when incorporating new modalities.
In: Proceedings of the IEEE conference on computer vision and pattern recognition
4 Pith papers cite this work. Polarity classification is still indexing.
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Proposes Spatial Group Convolution to accelerate 3D semantic scene completion networks via grouped sparse operations, reporting state-of-the-art accuracy and speed on SUNCG.
Presents an SSM-based hierarchical feature learning method for medical point clouds that reports superior performance on classification, completion, and segmentation using a new dataset MedPointS.
citing papers explorer
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Multimodal LLMs under Pairwise Modalities
A two-stage framework enables multimodal LLMs to learn shared latent representations from pairwise modality data and achieve cross-modal generation when incorporating new modalities.
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Efficient Semantic Scene Completion Network with Spatial Group Convolution
Proposes Spatial Group Convolution to accelerate 3D semantic scene completion networks via grouped sparse operations, reporting state-of-the-art accuracy and speed on SUNCG.
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Hierarchical Feature Learning for Medical Point Clouds via State Space Model
Presents an SSM-based hierarchical feature learning method for medical point clouds that reports superior performance on classification, completion, and segmentation using a new dataset MedPointS.
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