Multi-label image recognition with partial labels (MLR-PL), in which some labels are known
while others are unknown for each image, may greatly reduce the cost of annotation and …

Multilabel image classification aims to assign images to multiple possible labels. In this task,
each image may be associated with multiple labels, making it more challenging than the …

Recently many multi-label image recognition (MLR) works have made significant progress
by introducing pre-trained object detection models to generate lots of proposals or utilizing …

Low-light image enhancement is a long-term low-level vision problem, which aims to
improve the visual quality of images captured in low illumination environment. Convolutional …

Multi-label image classification (MLIC) is a fundamental and highly challenging task in the
field of computer vision. Most methods usually only focus on the inter-label association or …

Modern visual recognition models often display overconfidence due to their reliance on
complex deep neural networks and one-hot target supervision, resulting in unreliable …

The coexistence of multiple defect categories as well as the substantial class imbalance
problem significantly impair the detection of sewer pipeline defects. To solve this problem, a …

Multi-abel Learning (MLL) often involves the assignment of multiple relevant labels to each
instance, which can lead to the leakage of sensitive information (such as smoking, diseases …

With the increasing amount of textual information in the Internet, smart semantic
comprehension is a practical demand. Among, automatic annotation for semantic roles …