Few-shot learning (FSL) has emerged as an effective learning method and shows great
potential. Despite the recent creative works in tackling FSL tasks, learning valid information …

Machine learning has been highly successful in data-intensive applications but is often
hampered when the data set is small. Recently, Few-shot Learning (FSL) is proposed to …

Fine-grained image analysis (FGIA) is a longstanding and fundamental problem in computer
vision and pattern recognition, and underpins a diverse set of real-world applications. The …

Unsupervised domain adaptation (UDA) is to make predictions for unlabeled data on a
target domain, given labeled data on a source domain whose distribution shifts from the …

Distance metric learning (DML) has been successfully applied to object classification, both
in the standard regime of rich training data and in the few-shot scenario, where each …

To calculate the model accuracy on a computer vision task, eg, object recognition, we
usually require a test set composing of test samples and their ground truth labels. Whilst …

Given labeled instances on a source domain and unlabeled ones on a target domain,
unsupervised domain adaptation aims to learn a task classifier that can well classify target …

Fine-grained visual classification (FGVC) is much more challenging than traditional
classification tasks due to the inherently subtle intra-class object variations. Recent works …

Learning with auxiliary tasks can improve the ability of a primary task to generalise.
However, this comes at the cost of manually labelling auxiliary data. We propose a new …

Abstract ImageNet Large Scale Visual Recognition Challenge (ILSVRC) is one of the most
authoritative academic competitions in the field of Computer Vision (CV) in recent years. But …