Promotional videos are rapidly becoming a popular medium for persuading people to
change their behaviours in many settings (eg, online shop**, social enterprise initiatives) …

Abstract Machine learning is transforming the video editing industry. Recent advances in
computer vision have leveled-up video editing tasks such as intelligent reframing …

This paper presents TCE: Temporally Coherent Embeddings for self-supervised video
representation learning. The proposed method exploits inherent structure of unlabeled video …

Transformers have gained increasing popularity in a wide range of applications, including
Natural Language Processing (NLP), Computer Vision and Speech Recognition, because of …

We propose to learn to distinguish reversible from irreversible actions for better informed
decision-making in Reinforcement Learning (RL). From theoretical considerations, we show …

The goal of this paper is to self-train a 3D convolutional neural network on an unlabeled
video collection for deployment on small-scale video collections. As smaller video datasets …

This paper proposes a novel pretext task for self-supervised video representation learning
by exploiting spatiotemporal continuity in videos. It is motivated by the fact that videos are …

Self-supervised learning has made unsupervised pretraining relevant again for difficult
computer vision tasks. The most effective self-supervised methods involve prediction tasks …

Large labeled datasets are crucial for video understanding progress. However, the labeling
process is time-consuming, expensive, and tiresome. To overcome this impediment, various …

The success of deep learning based models for computer vision applications requires large
scale human annotated data which are often expensive to generate. Self-supervised …