5 accepted papers
Fine-Grained Domain Generalization (FGDG) presents greater challenges than conventional domain generalization due to the subtle inter-class differences and relatively pronounced intra-class variations inherent in fine-grained recognition tasks. Under domain shifts, the model becomes overly sensitive
Class Incremental Learning (CIL) aims to enable models to continually learn new classes while retaining previously learned knowledge. The principal challenge in CIL is catastrophic forgetting, which prior approaches typically address by distilling knowledge from previous model. However, such way is
Few-Shot Class-Incremental Learning (FSCIL) aims to continuously learn new classes from a limited set of training samples without forgetting knowledge of previously learned classes. Conventional FSCIL methods typically build a robust feature extractor during the base training session with abundant t…
In this paper, we focus on the issue of Couple Noisy Labels (CNL) in Visible-Infrared Person Re-identification. CNL which refers to the Noisy Annotations and the Noisy Correspondences. Existing methods have a drawback of wasting samples, as only clean samples selected based on confidence are conside…
DNNs inevitably over-fits to label noisy sample, resulting in poor generalization. To mitigate the influence of label noise, Co-teaching based methods tend to select potentially clean data as the training samples based on small-loss criterion. However, the small-loss criterion cannot guarantee the p…