Contrastive Embedding Distribution Refinement and Entropy-Aware Attention for 3D Point Cloud Classification

• URL: http://arxiv.org/abs/2201.11388v1
• Date: Thu, 27 Jan 2022 09:10:28 GMT
• Title: Contrastive Embedding Distribution Refinement and Entropy-Aware Attention for 3D Point Cloud Classification
• Authors: Feng Yang, Yichao Cao, Qifan Xue, Shuai Jin, Xuanpeng Li, and Weigong Zhang
• Abstract summary: This work offers a new strategy for learning powerful representations via a contrastive learning approach that can be embedded into any point cloud classification network. Our method achieves 82.9% accuracy on the real-world ScanObjectNN dataset and substantial performance gains up to 2.9% in DCGNN, 3.1% in PointNet++, and 2.4% in GBNet.
• Score: 3.710922682020501
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning a powerful representation from point clouds is a fundamental and challenging problem in the field of computer vision. Different from images where RGB pixels are stored in the regular grid, for point clouds, the underlying semantic and structural information of point clouds is the spatial layout of the points. Moreover, the properties of challenging in-context and background noise pose more challenges to point cloud analysis. One assumption is that the poor performance of the classification model can be attributed to the indistinguishable embedding feature that impedes the search for the optimal classifier. This work offers a new strategy for learning powerful representations via a contrastive learning approach that can be embedded into any point cloud classification network. First, we propose a supervised contrastive classification method to implement embedding feature distribution refinement by improving the intra-class compactness and inter-class separability. Second, to solve the confusion problem caused by small inter-class compactness and inter-class separability. Second, to solve the confusion problem caused by small inter-class variations between some similar-looking categories, we propose a confusion-prone class mining strategy to alleviate the confusion effect. Finally, considering that outliers of the sample clusters in the embedding space may cause performance degradation, we design an entropy-aware attention module with information entropy theory to identify the outlier cases and the unstable samples by measuring the uncertainty of predicted probability. The results of extensive experiments demonstrate that our method outperforms the state-of-the-art approaches by achieving 82.9% accuracy on the real-world ScanObjectNN dataset and substantial performance gains up to 2.9% in DCGNN, 3.1% in PointNet++, and 2.4% in GBNet.

Related papers

• BiEquiFormer: Bi-Equivariant Representations for Global Point Cloud Registration [28.75341781515012]
  The goal of this paper is to address the problem of global point cloud registration (PCR) i.e., finding the optimal alignment between point clouds. We show that state-of-the-art deep learning methods suffer from huge performance degradation when the point clouds are arbitrarily placed in space.
  arXiv  Detail & Related papers  (2024-07-11T17:58:10Z)
• Class-Imbalanced Semi-Supervised Learning for Large-Scale Point Cloud Semantic Segmentation via Decoupling Optimization [64.36097398869774]
  Semi-supervised learning (SSL) has been an active research topic for large-scale 3D scene understanding. The existing SSL-based methods suffer from severe training bias due to class imbalance and long-tail distributions of the point cloud data. We introduce a new decoupling optimization framework, which disentangles feature representation learning and classifier in an alternative optimization manner to shift the bias decision boundary effectively.
  arXiv  Detail & Related papers  (2024-01-13T04:16:40Z)
• Bidirectional Knowledge Reconfiguration for Lightweight Point Cloud Analysis [74.00441177577295]
  Point cloud analysis faces computational system overhead, limiting its application on mobile or edge devices. This paper explores feature distillation for lightweight point cloud models. We propose bidirectional knowledge reconfiguration to distill informative contextual knowledge from the teacher to the student.
  arXiv  Detail & Related papers  (2023-10-08T11:32:50Z)
• Clustering based Point Cloud Representation Learning for 3D Analysis [80.88995099442374]
  We propose a clustering based supervised learning scheme for point cloud analysis. Unlike current de-facto, scene-wise training paradigm, our algorithm conducts within-class clustering on the point embedding space. Our algorithm shows notable improvements on famous point cloud segmentation datasets.
  arXiv  Detail & Related papers  (2023-07-27T03:42:12Z)
• Data Augmentation-free Unsupervised Learning for 3D Point Cloud Understanding [61.30276576646909]
  We propose an augmentation-free unsupervised approach for point clouds to learn transferable point-level features via soft clustering, named SoftClu. We exploit the affiliation of points to their clusters as a proxy to enable self-training through a pseudo-label prediction task.
  arXiv  Detail & Related papers  (2022-10-06T10:18:16Z)
• Unsupervised Learning on 3D Point Clouds by Clustering and Contrasting [11.64827192421785]
  unsupervised representation learning is a promising direction to auto-extract features without human intervention. This paper proposes a general unsupervised approach, named textbfConClu, to perform the learning of point-wise and global features.
  arXiv  Detail & Related papers  (2022-02-05T12:54:17Z)
• Guided Point Contrastive Learning for Semi-supervised Point Cloud Semantic Segmentation [90.2445084743881]
  We present a method for semi-supervised point cloud semantic segmentation to adopt unlabeled point clouds in training to boost the model performance. Inspired by the recent contrastive loss in self-supervised tasks, we propose the guided point contrastive loss to enhance the feature representation and model generalization ability.
  arXiv  Detail & Related papers  (2021-10-15T16:38:54Z)
• Learning Robust Representation for Clustering through Locality Preserving Variational Discriminative Network [16.259673823482665]
  Variational Deep Embedding achieves great success in various clustering tasks. VaDE suffers from two problems: 1) it is fragile to the input noise; 2) it ignores the locality information between the neighboring data points. We propose a joint learning framework that improves VaDE with a robust embedding discriminator and a local structure constraint.
  arXiv  Detail & Related papers  (2020-12-25T02:31:55Z)
• PointManifold: Using Manifold Learning for Point Cloud Classification [5.705680763604835]
  We propose a point cloud classification method based on graph neural network and manifold learning. This paper uses manifold learning algorithms to embed point cloud features for better considering continuity on the surface. Experiments show that the proposed point cloud classification methods obtain the mean class accuracy (mA) of 90.2% and the overall accuracy (oA) of 93.2%.
  arXiv  Detail & Related papers  (2020-10-14T16:28:19Z)
• ReMarNet: Conjoint Relation and Margin Learning for Small-Sample Image Classification [49.87503122462432]
  We introduce a novel neural network termed Relation-and-Margin learning Network (ReMarNet) Our method assembles two networks of different backbones so as to learn the features that can perform excellently in both of the aforementioned two classification mechanisms. Experiments on four image datasets demonstrate that our approach is effective in learning discriminative features from a small set of labeled samples.
  arXiv  Detail & Related papers  (2020-06-27T13:50:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.