4 papers
Beyond Random Masking: A Dual-Stream Approach for Rotation-Invariant Point Cloud Masked Autoencoders
Xuanhua Yin, Dingxin Zhang, Yu Feng +3
Existing rotation-invariant point cloud masked autoencoders (MAE) rely on random masking strategies that overlook geometric structure and semantic coherence. Random masking treats…
HFBRI-MAE: Handcrafted Feature Based Rotation-Invariant Masked Autoencoder for 3D Point Cloud Analysis
Xuanhua Yin, Dingxin Zhang, Jianhui Yu +1
Self-supervised learning (SSL) has demonstrated remarkable success in 3D point cloud analysis, particularly through masked autoencoders (MAEs). However, existing MAE-based methods…
CA-W3D: Leveraging Context-Aware Knowledge for Weakly Supervised Monocular 3D Detection
Chupeng Liu, Runkai Zhao, Weidong Cai
Weakly supervised monocular 3D detection, while less annotation-intensive, often struggles to capture the global context required for reliable 3D reasoning. Conventional label-effi…
MIRROR: Multi-Modal Pathological Self-Supervised Representation Learning via Modality Alignment and Retention
Tianyi Wang, Jianan Fan, Dingxin Zhang +4
Histopathology and transcriptomics are fundamental modalities in oncology, encapsulating the morphological and molecular aspects of the disease. Multi-modal self-supervised learnin…