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 …

Nano-optic imagers that modulate light at sub-wavelength scales could enable new
applications in diverse domains ranging from robotics to medicine. Although metasurface …

State-of-the-art visual perception models for a wide range of tasks rely on supervised
pretraining. ImageNet classification is the de facto pretraining task for these models. Yet …

Image retrieval is the problem of searching an image database for items that are similar to a
query image. To address this task, two main types of image representations have been …

Establishing correspondences between two images requires both local and global spatial
context. Given putative correspondences of feature points in two views, in this paper, we …

While image retrieval and instance recognition techniques are progressing rapidly, there is a
need for challenging datasets to accurately measure their performance--while posing novel …

Image Retrieval is a fundamental task of obtaining images similar to the query one from a
database. A common image retrieval practice is to firstly retrieve candidate images via …

Image descriptors based on activations of Convolutional Neural Networks (CNNs) have
become dominant in image retrieval due to their discriminative power, compactness of …

We consider the problem of producing compact architectures for text classification, such that
the full model fits in a limited amount of memory. After considering different solutions …