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Bringing masked autoencoders explicit contrastive properties for point cloud self-supervised learning
Contrastive learning (CL) for Vision Transformers (ViTs) in image domains has achieved
performance comparable to CL for traditional convolutional backbones. However, in 3D …
performance comparable to CL for traditional convolutional backbones. However, in 3D …
Point-jepa: A joint embedding predictive architecture for self-supervised learning on point cloud
Recent advancements in self-supervised learning in the point cloud domain have
demonstrated significant potential. However, these methods often suffer from drawbacks …
demonstrated significant potential. However, these methods often suffer from drawbacks …
ESP-Zero: Unsupervised enhancement of zero-shot classification for Extremely Sparse Point cloud
J Han, Z Cao, W Zheng, X Zhou, X He, Y Zhang… - arxiv preprint arxiv …, 2024 - arxiv.org
In recent years, zero-shot learning has attracted the focus of many researchers, due to its
flexibility and generality. Many approaches have been proposed to achieve the zero-shot …
flexibility and generality. Many approaches have been proposed to achieve the zero-shot …
GSTran: Joint Geometric and Semantic Coherence for Point Cloud Segmentation
Learning meaningful local and global information remains a challenge in point cloud
segmentation tasks. When utilizing local information, prior studies indiscriminately …
segmentation tasks. When utilizing local information, prior studies indiscriminately …