Thanks to the large pre-trained vision-language models (VLMs) like CLIP, we can craft a
zero-shot classifier by discrete prompt design, eg, the confidence score of an image …

Existing long-tailed classification (LT) methods only focus on tackling the class-wise
imbalance that head classes have more samples than tail classes, but overlook the attribute …

While long-tailed semi-supervised learning (LTSSL) has received tremendous attention in
many real-world classification problems, existing LTSSL algorithms typically assume that the …

Generalized few-shot object detection aims to achieve precise detection on both base
classes with abundant annotations and novel classes with limited training data. Existing …

Long-tailed distributions frequently emerge in real-world data, where a large number of
minority categories contain a limited number of samples. Such imbalance issue …

Long-tailed visual recognition has received increasing attention in recent years. Due to the
extremely imbalanced data distribution in long-tailed learning, the learning process shows …

Long-tailed learning aims to tackle the crucial challenge that head classes dominate the
training procedure under severe class imbalance in real-world scenarios. However, little …

Real-world data usually present long-tailed distributions. Training on imbalanced data tends
to render neural networks perform well on head classes while much worse on tail classes …

Mainstream few-shot segmentation methods meet performance bottleneck due to the data
scarcity of novel classes with insufficient intra-class variations, which results in a biased …

Weeds are a major threat to crop production. Automated innovations for reducing herbicides
and labor needed for weeding have become a high priority for sustainable weed …