3 accepted papers
Long-tailed image classification remains a long-standing challenge, as real-world data typically follow highly imbalanced distributions where a few head classes dominate and many tail classes contain only limited samples. This imbalance biases feature learning toward head categories and leads to sig
With the increasing demand of privacy protection, Machine Unlearning (MU) appears to remove private data from an already trained model without retraining from scratch. Most current works suffer from overly unlearning (low fidelity) or incomplete unlearning (low effectiveness). To identify the issues
Incremental few-shot semantic segmentation (IFSS) expands segmentation capacity of the trained model to segment new-class images with few samples. However, semantic meanings may shift from background to object class or vice versa during incremental learning. Moreover, new-class samples often lack re…