This paper reviews the state of the art in visual privacy protection techniques, with particular
attention paid to techniques applicable to the field of Active and Assisted Living (AAL). A …

We wish to automatically predict the" speediness" of moving objects in videos-whether they
move faster, at, or slower than their" natural" speed. The core component in our approach is …

In this paper, we propose self-supervised training for video transformers using unlabeled
video data. From a given video, we create local and global spatiotemporal views with …

When people observe events, they are able to abstract key information and build concise
summaries of what is happening. These summaries include contextual and semantic …

Action recognition has improved dramatically with massive-scale video datasets. Yet, these
datasets are accompanied with issues related to curation cost, privacy, ethics, bias, and …

Recently, there has been a lot of interest in building compact models for video classification
which have a small memory footprint (< 1 GB). While these models are compact, they …

Pre-training on large scale unlabelled datasets has shown impressive performance
improvements in the fields of computer vision and natural language processing. Given the …

Most methods tackle zero-shot video classification by aligning visual-semantic
representations within seen classes, which limits generalization to unseen classes. To …

How can we generalize to a new prediction task at test time when it also uses a new
modality as input? More importantly, how can we do this with as little annotated data as …

Scaling up weakly-supervised datasets has shown to be highly effective in the image-text
domain and has contributed to most of the recent state-of-the-art computer vision and …