Real-world recognition system often encounters the challenge of unseen labels. To identify
such unseen labels, multi-label zero-shot learning (ML-ZSL) focuses on transferring …

Existing knowledge distillation methods typically work by imparting the knowledge of output
logits or intermediate feature maps from the teacher network to the student network, which is …

Recently, zero-shot multi-label classification has garnered considerable attention owing to
its capacity to predict unseen labels without human annotations. Nevertheless, prevailing …

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 …

Multi-modal Emotion Recognition (MER) aims to identify various human emotions from
heterogeneous modalities. With the development of emotional theories, there are more and …

Multi-task learning (MTL) permits joint task learning based on a shared deep learning
architecture and multiple loss functions. Despite the recent advances in MTL, one loss often …

We identify a critical bias in contemporary CLIP-based models, which we denote as\textit
{single tag bias}. This bias manifests as a disproportionate focus on a singular tag (word) …

Existing multi-label knowledge distillation methods simply use regression or single-label
classification methods without fully exploiting the essence of multi-label classification …

Accurate discriminative region proposal has an important effect for fine-grained image
recognition. The vision transformer (ViT) brings about a striking effect in computer vision due …

In multi-label classification, each training instance is associated with multiple class labels
simultaneously. Unfortunately, collecting the fully precise class labels for each training …