In the early days, content-based image retrieval (CBIR) was studied with global features.
Since 2003, image retrieval based on local descriptors (de facto SIFT) has been extensively …

In recent years a vast amount of visual content has been generated and shared from many
fields, such as social media platforms, medical imaging, and robotics. This abundance of …

Transfer learning is a widely-used paradigm in deep learning, where models pre-trained on
standard datasets can be efficiently adapted to downstream tasks. Typically, better pre …

We uncover an ever-overlooked deficiency in the prevailing Few-Shot Learning (FSL)
methods: the pre-trained knowledge is indeed a confounder that limits the performance. This …

Transfer learning is a cornerstone of computer vision, yet little work has been done to
evaluate the relationship between architecture and transfer. An implicit hypothesis in …

Object detection, one of the most fundamental and challenging problems in computer vision,
seeks to locate object instances from a large number of predefined categories in natural …

Convolutional Neural Networks (CNNs) have reigned for a decade as the de facto approach
to automated medical image diagnosis. Recently, vision transformers (ViTs) have appeared …

Transfer learning, which allows a source task to affect the inductive bias of the target task, is
widely used in computer vision. The typical way of conducting transfer learning with deep …

Computer vision has become increasingly important and effective in recent years due to its
wide-ranging applications in areas as diverse as smart surveillance and monitoring, health …

Transfer learning is a standard technique to transfer knowledge from one domain to another.
For applications in medical imaging, transfer from ImageNet has become the de-facto …