The rapid advances in Vision Transformer (ViT) refresh the state-of-the-art performances in
various vision tasks, overshadowing the conventional CNN-based models. This ignites a few …

Video domain generalization aims to learn generalizable video classification models for
unseen target domains by training in a source domain. A critical challenge of video domain …

Spatial convolutions are extensively used in numerous deep video models. It fundamentally
assumes spatio-temporal invariance, ie, using shared weights for every location in different …

We address a novel cross-domain few-shot learning task (CD-FSL) with multimodal input
and unlabeled target data for egocentric action recognition. This paper simultaneously …

Human action recognition systems enhance public safety by detecting abnormal behavior
autonomously. RGB sensors commonly used in such systems capture personal information …

In the field of sign language recognition, effective interpretation of semantic information,
which is primarily conveyed through facial and hand gestures, poses significant challenges …

Action recognition, whose goal is identifying and extracting spatio-temporal features from
video content, is a foundation of work in video understanding. However, current methods for …

Considering the increasing concerns about data copyright and sensitivity issues, we present
a novel Privacy-Enhanced Zero-Shot Learning (PE-ZSL) paradigm. The key innovation is to …

Designing a high-performance neural network is a difficult task. Neural architecture search
(NAS) methods aim to solve this process. However, the construction of a high-quality …

Current architectures for video understanding mainly build upon 3D convolutional blocks or
2D convolutions with additional operations for temporal modeling. However, these methods …