Sign languages are visual languages which convey information by signers' handshape,
facial expression, body movement, and so forth. Due to the inherent restriction of …

Datasets often include noisy labels, but learning from them is difficult. Since mislabeled
examples usually have larger loss values in training, the small-loss trick is regarded as a …

Collecting full annotations to construct multi-label datasets is difficult and labor-consuming.
As an effective solution to relieve the annotation burden, single positive multi-label learning …

This paper investigates a challenging problem of zero-shot learning in the multi-label
scenario (MLZSL), wherein, the model is trained to recognize multiple unseen classes within …

Out-of-distribution (OOD) generalization has emerged as a critical challenge in graph
learning, as real-world graph data often exhibit diverse and shifting environments that …

Annotating data for multi-label classification is prohibitively expensive because every
category of interest must be confirmed to be present or absent. Recent work on single …

Multi-label zero-shot learning (MLZSL) aims to predict multiple unseen classes through
knowledge of seen classes in complex image scenarios. Different from traditional zero-shot …