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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 …
Nesf: Neural semantic fields for generalizable semantic segmentation of 3d scenes
We present NeSF, a method for producing 3D semantic fields from posed RGB images
alone. In place of classical 3D representations, our method builds on recent work in implicit …
alone. In place of classical 3D representations, our method builds on recent work in implicit …
Cross-modal center loss for 3D cross-modal retrieval
Cross-modal retrieval aims to learn discriminative and modal-invariant features for data from
different modalities. Unlike the existing methods which usually learn from the features …
different modalities. Unlike the existing methods which usually learn from the features …
Point cloud pre-training with natural 3d structures
The construction of 3D point cloud datasets requires a great deal of human effort. Therefore,
constructing a largescale 3D point clouds dataset is difficult. In order to remedy this issue …
constructing a largescale 3D point clouds dataset is difficult. In order to remedy this issue …