Deep learning methods haverevolutionized speech recognition, image recognition, and
natural language processing since 2010. Each of these tasks involves a single modality in …

Zero-shot remote sensing scene classification aims to solve the scene classification problem
on unseen categories and has attracted numerous research attention in the remote sensing …

With the rise of powerful pre-trained vision-language models like CLIP, it becomes essential
to investigate ways to adapt these models to downstream datasets. A recently proposed …

Large pre-trained vision-language models like CLIP have shown great potential in learning
representations that are transferable across a wide range of downstream tasks. Different …

State-of-the-art computer vision systems are trained to predict a fixed set of predetermined
object categories. This restricted form of supervision limits their generality and usability since …

An integral part of video analysis and surveillance is temporal activity detection, which
means to simultaneously recognize and localize activities in long untrimmed videos …

Most machine-learning methods focus on classifying instances whose classes have already
been seen in training. In practice, many applications require classifying instances whose …

When labeled training data is scarce, a promising data augmentation approach is to
generate visual features of unknown classes using their attributes. To learn the class …

We present a conceptually simple, flexible, and general framework for few-shot learning,
where a classifier must learn to recognise new classes given only few examples from each …

In this work we study the problem of using referential language to identify common objects in
real-world 3D scenes. We focus on a challenging setup where the referred object belongs to …