Self-supervised learning can extract representations of good quality from solely unlabeled
data, which is appealing for point cloud videos due to their high labelling cost. In this paper …

Standard multi-modal models assume the use of the same modalities in training and
inference stages. However, in practice, the environment in which multi-modal models …

Due to the variation in spatial resolution and the diversity of object scales, the interpretation
of optical remote sensing images is extremely challenging. Deep learning has become the …

We propose and address a new generalisation problem: can a model trained for action
recognition successfully classify actions when they are performed within a previously …

Human perception and understanding is a major domain of computer vision which like many
other vision subdomains recently stands to gain from the use of large models pre-trained on …

Recently, the community has made tremendous progress in developing effective methods
for point cloud video understanding that learn from massive amounts of labeled data …

This paper presents SimVTP: a Simple Video-Text Pretraining framework via masked
autoencoders. We randomly mask out the spatial-temporal tubes of input video and the word …

Video-language pre-training is crucial for learning powerful multi-modal representation.
However, it typically requires a massive amount of computation. In this paper, we develop …

Learning computer vision models from (and for) movies has a long-standing history. While
great progress has been attained, there is still a need for a pretrained multimodal model that …

Abstract Audio-Visual Emotion Recognition (AVER) has garnered increasing attention in
recent years for its critical role in creating emotion-aware intelligent machines. Previous …