3 accepted papers
Federated learning (FL) provides a decentralized machine learning paradigm where a server collaborates with a group of clients to learn a global model without accessing the clients' data. User heterogeneity is a significant challenge for FL, which together with the class-distribution imbalance furth…
Image classification for long-tailed scenarios has attracted more attention because its distribution is more similar to real-world image data. From the perspective of solving imbalance at the sample level, we propose a simple but effective method, named Sample-aware Knowledge Distillation (SAKD), wh…
Large-scale point cloud semantic segmentation has wide applications. Current popular researches mainly focus on fully supervised learning which demands expensive and tedious manual point-wise annotation. Weakly supervised learning is an alternative way to avoid this exhausting annotation. However, f…