Group Equivariant Video Action Recognition: Making action-recognition networks equivariant to temporal direction and discrete spatial rotations

This work applies the theory of group equivariance to the domain of video action recognition replacing standard 3Dconvolutions with group convolutions which are equivariant to temporal direction, and multiples of 90-degree spatial rotations. We propose a temporal direction symmetry group T2 and extend the standard planar rotations group to three...

Contrastive Learning of Visual Representations from Unlabeled Videos

This thesis presents a novel self-supervised approach of learning visual representations from videos containing human actions. Our approach tackles the complex problem of learning without the need of labeled data by exploring to what extent the ideas successfully used for images can be transferred, adapted and extended to videos for action...

Learning Phase-Based Descriptions for Action Recognition

Action recognition continues to receive significant attention from the research community, with new neural network architectures being developed continuously. Optical flow is by far the most popular input motion representation to these architectures, leaving a lot of undiscovered potential for other types of motion representations. Eulerian...