CrossMoCo: Multi-Modal Momentum Contrastive Learning for Point Cloud
Bibliographic record
Abstract
The point cloud is a 3D geometric data that lacks a specific structure and is permutation-invariant. The applications of point clouds have gained significant attention recently in the field of vision tasks. However, most existing works on point clouds utilize supervised learning on large labelled data, which are costly and laborious to collect. To this end, unsupervised learning, for example, self-supervised learning, has shown promising performance in various tasks of 2D computer vision and holds the potential in 3D computer vision applications. In this study, we introduce a novel selfsupervised method called CrossMoCo, which learns the representations of unlabelled point cloud data in a multi-modal setup that also utilizes the 2D rendered images of the point clouds. CrossMoCo outperforms existing methods on multimodal self-supervised learning on point cloud by introducing two new concepts: momentum contrastive learning with more negative samples and multiple-view intra-modal contrastive learning. The first component learns from an online encoder and a momentum encoder with a large number of negative samples, which provides consistent learning signals. The second component enforces consistency between different views of the samples of the same modality, thereby improving multimodal representation. We conduct extensive studies on two popular benchmark datasets (ModelNet40 and ScanObjectNN) for linear classification and few-shot learning tasks. Our results demonstrate that CrossMoCo achieves superior performance over existing methods for both tasks on both datasets, achieving up to 4.36% improvement on linear classification and up to 9.2% on few-shot tasks. Our code is available at https://github.com/snehaputul/CrossMoCo.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".