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Found 8 papers, 5 papers with code
PDF: A Probability-Driven Framework for Open World 3D Point Cloud Semantic Segmentation
Existing point cloud semantic segmentation networks cannot identify unknown classes and update their knowledge, due to a closed-set and static perspective of the real world, which would induce the intelligent agent to make bad decisions.
MENTOR: Multi-level Self-supervised Learning for Multimodal Recommendation
It utilizes multimodal information to alleviate the data sparsity problem in recommendation systems, thus improving recommendation accuracy.
Dual-Reference Source-Free Active Domain Adaptation for Nasopharyngeal Carcinoma Tumor Segmentation across Multiple Hospitals
We collect a large-scale clinical dataset comprising 1057 NPC patients from five hospitals to validate our approach.
Point-LGMask: Local and Global Contexts Embedding for Point Cloud Pre-training with Multi-Ratio Masking
In our work, we present Point-LGMask, a novel method to embed both local and global contexts with multi-ratio masking, which is quite effective for self-supervised feature learning of point clouds but is unfortunately ignored by existing pre-training works.
Ranked #3 on
Few-Shot 3D Point Cloud Classification
on ModelNet40 5-way (10-shot)
(using extra training data)
CasFusionNet: A Cascaded Network for Point Cloud Semantic Scene Completion by Dense Feature Fusion
In our work, we present CasFusionNet, a novel cascaded network for point cloud semantic scene completion by dense feature fusion.
Improving Feature Extraction from Histopathological Images Through A Fine-tuning ImageNet Model
The extracted features from the finetuned FTX2048 exhibited significantly higher accuracy for predicting tisue types of CRC compared to the off the shelf feature directly from Xception based on ImageNet database.
Learning Latent Features with Pairwise Penalties in Low-Rank Matrix Completion
Low-rank matrix completion has achieved great success in many real-world data applications.
On Scalable Inference with Stochastic Gradient Descent
In many applications involving large dataset or online updating, stochastic gradient descent (SGD) provides a scalable way to compute parameter estimates and has gained increasing popularity due to its numerical convenience and memory efficiency.
