Machine learning systems generally assume that the training and testing distributions are
the same. To this end, a key requirement is to develop models that can generalize to unseen …

Facial recognition is one of the most academically studied and industrially developed areas
within computer vision where we readily find associated applications deployed globally. This …

Tracking objects of interest in a video is one of the most popular and widely applicable
problems in computer vision. However, with the years, a Cambrian explosion of use cases …

Visual modality is one of the most dominant modalities for current continuous emotion
recognition methods. Compared to which the EEG modality is relatively less sound due to its …

In the real world, humans have the ability to accumulate new knowledge in any conditions.
However, deeplearning suffers from the phenomenon so-called catastrophic forgetting of the …

Recent years witnessed the breakthrough of face recognition with deep convolutional neural
networks. Dozens of papers in the field of FR are published every year. Some of them were …

Owing to security implications of adversarial vulnerability, adversarial robustness of deep
metric learning models has to be improved. In order to avoid model collapse due to …

Due to the lack of high-quality labeled data and poor generalization ability of supervised
models in remote sensing scene classification, cross-domain scene classification is …

Few-shot-based facial recognition systems have gained increasing attention due to their
scalability and ability to work with a few face images during the model deployment phase …

Abstract Convolutional Neural Networks have achieved remarkable success in face
recognition, in part due to the abundant availability of data. However, the data used for …