Abstract The field of Continual Learning investigates the ability to learn consecutive tasks
without losing performance on those previously learned. The efforts of researchers have …

Object detection (OD) is a crucial computer vision task that has seen the development of
many algorithms and models over the years. While the performance of current OD models …

Nowadays, scene text recognition has attracted more and more attention due to its various
applications. Most state-of-the-art methods adopt an encoder-decoder framework with …

This paper focuses on the prevalent stage interference and stage performance imbalance of
incremental learning. To avoid obvious stage learning bottlenecks, we propose a new …

Self-supervised representation learning for visual pre-training has achieved remarkable
success with sample (instance or pixel) discrimination and semantics discovery of instance …

Text-based visual question answering (TextVQA) requires analyzing both the visual contents
and texts in an image to answer a question, which is more practical than general visual …

Modern object detectors are ill-equipped to incrementally learn new emerging object
classes over time due to the well-known phenomenon of catastrophic forgetting. Due to data …

Due to the large success in object detection and instance segmentation, Mask R-CNN
attracts great attention and is widely adopted as a strong baseline for arbitrary-shaped …

Atrial fibrillation detection (AFD) has attracted much attention in the field of embedded
systems. In this study, we propose a sorting fuzzy min-max (SFMM) model, and then develop …

The dynamic expansion architecture is becoming popular in class incremental learning,
mainly due to its advantages in alleviating catastrophic forgetting. However, task confu-sion …