With the recent renaissance of deep convolutional neural networks (CNNs), encouraging
breakthroughs have been achieved on the supervised recognition tasks, where each class …

Contrastive language-image pretraining has shown great success in learning visual-textual
joint representation from web-scale data, demonstrating remarkable “zero-shot” …

This article takes a problem-oriented perspective and presents a comprehensive review of
transfer-learning methods, both shallow and deep, for cross-dataset visual recognition …

Large-scale multi-modal training with image-text pairs imparts strong generalization to CLIP
model. Since training on a similar scale for videos is infeasible, recent approaches focus on …

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

Adopting contrastive image-text pretrained models like CLIP towards video classification has
gained attention due to its cost-effectiveness and competitive performance. However, recent …

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

Video action recognition is one of the representative tasks for video understanding. Over the
last decade, we have witnessed great advancements in video action recognition thanks to …

Zero-shot learning (ZSL) aims to classify images from unseen categories, by merely utilizing
seen class images as the training data. Existing works on ZSL mainly leverage the global …

The growing number of action classes has posed a new challenge for video understanding,
making Zero-Shot Action Recognition (ZSAR) a thriving direction. The ZSAR task aims to …