Recent success of deep learning is largely attributed to the sheer amount of data used for
training deep neural networks. Despite the unprecedented success, the massive data …

Influenced by the great success of deep learning via cloud computing and the rapid
development of edge chips, research in artificial intelligence (AI) has shifted to both of the …

In recent years, deep neural networks have been successful in both industry and academia,
especially for computer vision tasks. The great success of deep learning is mainly due to its …

Abstract Graph Neural Networks (GNNs) have shown exceptional performance in the task of
link prediction. Despite their effectiveness, the high latency brought by non-trivial …

Abstract Domain generalization studies the problem of training a model with samples from
several domains (or distributions) and then testing the model with samples from a new …

Abstract Knowledge distillation is an effective model compression method to improve the
performance of a lightweight student model by transferring the knowledge of a well …

Achieving backward compatibility when rolling out new models can highly reduce costs or
even bypass feature re-encoding of existing gallery images for in-production visual retrieval …

Transformer-based architectures have become the de-facto standard models for diverse
vision tasks owing to their superior performance. As the size of these transformer-based …

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 …

Over the past decade, deep learning models have exhibited considerable advancements,
reaching or even exceeding human-level performance in a range of visual perception tasks …