Large deep learning models are impressive, but they struggle when real-time data is not
available. Few-shot class-incremental learning (FSCIL) poses a significant challenge for …

Real-world systems often encounter new data over time which leads to experiencing target
domain shifts. Existing Test-Time Adaptation (TTA) methods tend to apply computationally …

Few-shot class-incremental learning was introduced to solve the model adaptation problem
for new incremental classes with only a few examples while still remaining effective for old …

In the field of intelligent multimedia analysis, ultra-fine-grained visual categorization (Ultra-
FGVC) plays a vital role in distinguishing intricate subcategories within broader categories …

Few-shot Class-Incremental Learning (FSCIL) presents a unique challenge in machine
learning, as it necessitates the continuous learning of new classes from sparse labeled …

In pattern recognition, ultra-fine-grained visual classification (ultra-FGVC) has emerged as a
paramount challenge, focusing on sub-category distinction within fine-grained objects. The …

Ultra-fine-grained visual categorization (Ultra-FGVC) aims at distinguishing highly similar
sub-categories within fine-grained objects such as different soybean cultivars. Compared to …

Few-shot class-incremental learning has recently received significant research focus in
remote sensing scene classification (FSCIL-RSSC). The success of FSCIL-RSSC relies on …

Few-shot class-incremental learning (FSCIL) has recently attracted extensive attention in
various areas. Existing FSCIL methods highly depend on the robustness of the feature …

Few-shot class-incremental learning (FSCIL) aims to learn sequential classes with limited
samples in a few-shot fashion. Inherited from the classical class-incremental learning …