Scale is the primary factor for building a powerful foundation model that could well
generalize to a variety of downstream tasks. However, it is still challenging to train video …

Masked autoencoding has shown excellent performance on self-supervised video
representation learning. Temporal redundancy has led to a high masking ratio and …

In this paper we tackle the problem of self-supervised video alignment and activity progress
prediction using in-the-wild videos. Our proposed self-supervised representation learning …

As the deep learning revolution marches on, self-supervised learning has garnered
increasing attention in recent years thanks to its remarkable representation learning ability …

Dynamic facial expression recognition (DFER) in the wild is still hindered by data limitations,
eg, insufficient quantity and diversity of pose, occlusion and illumination, as well as the …

How to learn discriminative video representation from unlabeled videos is challenging but
crucial for video analysis. The latest attempts seek to learn a representation model by …

Masked autoencoders (MAE) is a deep learning method based on Transformer. Originally
used for images, it has now been extended to video, audio, and some other temporal …

Infrared (IR) technology has emerged as a solution for monitoring dark environments. It
offers resilience to shifting illumination, appearance changes, and shadows, with …

Infrared (IR) human action recognition (AR) exhibits resilience against shifting illumination
conditions, changes in appearance, and shadows. It has valuable applications in numerous …

Effective spatio-temporal modeling as a core of video representation learning is challenged
by complex scale variations in spatio-temporal cues in videos, especially different visual …