Efficient and accurate road boundary detection is a fundamental building component of the
perception system for autonomous driving. Specially, the challenges for road boundary …

Few-shot learning (FSL) that aims to recognize novel classes with few labeled samples is
troubled by its data scarcity. Though recent works tackle FSL with data augmentation-based …

The weak classifier ensemble algorithms based on the decision tree model, mainly include
bagging (eg, fandom forest-RF) and boosting (eg, gradient boosting decision tree, eXtreme …

As a subset of machine learning, meta-learning, or learning to learn, aims at improving the
model's capabilities by employing prior knowledge and experience. A meta-learning …

Few-shot learning (FSL) is a challenging task in classifying new classes from few labelled
examples. Many existing models embed class structural knowledge as prior knowledge to …

Although few-shot learning aims to address data scarcity, it still requires large, annotated
datasets for training, which are often unavailable due to cost and privacy concerns. Previous …

When humans explain their reasoning, such as their classification decisions, they often
break down an image into parts and highlight the evidence from those parts to support the …

Few-shot classification aims to learn a classifier that categorizes objects of unseen classes
with limited samples. One general approach is to mine as much information as possible from …

Deep neural networks (DNNs) are often prone to learn the spurious correlations between
target classes and bias attributes, like gender and race, inherent in a major portion of …

Few-shot Learning (FSL) aims to recognize the novel classes from few novel samples.
Recently, lots of methods have been proposed to improve FSL performance by introducing …