A Survey on Self-supervised Learning: Algorithms, Applications, and Future Trends
Deep supervised learning algorithms typically require a large volume of labeled data to
achieve satisfactory performance. However, the process of collecting and labeling such data …
achieve satisfactory performance. However, the process of collecting and labeling such data …
Masked modeling for self-supervised representation learning on vision and beyond
As the deep learning revolution marches on, self-supervised learning has garnered
increasing attention in recent years thanks to its remarkable representation learning ability …
increasing attention in recent years thanks to its remarkable representation learning ability …
Nerf-mae: Masked autoencoders for self-supervised 3d representation learning for neural radiance fields
Neural fields excel in computer vision and robotics due to their ability to understand the 3D
visual world such as inferring semantics, geometry, and dynamics. Given the capabilities of …
visual world such as inferring semantics, geometry, and dynamics. Given the capabilities of …
Fac: 3d representation learning via foreground aware feature contrast
Contrastive learning has recently demonstrated great potential for unsupervised pre-training
in 3D scene understanding tasks. However, most existing work randomly selects point …
in 3D scene understanding tasks. However, most existing work randomly selects point …
MeshCLIP: Efficient cross-modal information processing for 3D mesh data in zero/few-shot learning
Y Song, N Liang, Q Guo, J Dai, J Bai, F He - Information Processing & …, 2023 - Elsevier
Abstract Text, 2D, and 3D information are crucial information representations in modern
science and management disciplines. However, complex and irregular 3D data produce …
science and management disciplines. However, complex and irregular 3D data produce …
A survey on self-supervised learning: Algorithms, applications, and future trends
Deep supervised learning algorithms typically require a large volume of labeled data to
achieve satisfactory performance. However, the process of collecting and labeling such data …
achieve satisfactory performance. However, the process of collecting and labeling such data …
Unsupervised 3D skeleton-based action recognition using cross-attention with conditioned generation capabilities
Human action recognition plays a pivotal role in various real-world applications, including
surveillance systems, robotics, and occupant monitoring in the car interior. With such a …
surveillance systems, robotics, and occupant monitoring in the car interior. With such a …
Mesh neural networks based on dual graph pyramids
Deep neural networks (DNNs) have been widely used for mesh processing in recent years.
However, current DNNs can not process arbitrary meshes efficiently. On the one hand, most …
However, current DNNs can not process arbitrary meshes efficiently. On the one hand, most …
Self-Supervised Learning with Masked Autoencoders for Teeth Segmentation from Intra-oral 3D Scans
In modern dentistry, teeth localization, segmentation, and labeling from intra-oral 3D scans
are crucial for improving dental diagnostics, treatment planning, and population-based …
are crucial for improving dental diagnostics, treatment planning, and population-based …
Shapesplat: A large-scale dataset of gaussian splats and their self-supervised pretraining
3D Gaussian Splatting (3DGS) has become the de facto method of 3D representation in
many vision tasks. This calls for the 3D understanding directly in this representation space …
many vision tasks. This calls for the 3D understanding directly in this representation space …