Advances in Human Action Recognition Using Deep Learning

Dear Colleagues,

The topic of human action recognition in video sequences is a problem of immense applicability in a variety of real-world situations, including but not limited to (i) video surveillance, such as monitoring for fatalities in elderly homes or ensuring safety in public places, (ii) autonomous driving, when a driving agent needs to predict the next action a pedestrian standing at an intersection may consider, (iii) learning from instruction videos, when one needs to quickly find out the next step to cook a dish, (iv) medical diagnosis, such as in early diagnosis of autism-spectrum disorders in children, or (v) even as leisurely as to summarize a long movie; all these tasks will need human action recognition as a fundamental ingredient in their solutions.

Embarking on developments in deep neural networks, the problem of human action recognition has seen significant research strides recently. Starting from the success of two-stream neural architectures, deep-learning-based advancements in action recognition have moved into the design of sophisticated neural networks based on contrastive learning, transformers, and 3D-CNNs and is growing fast towards matching human performance.

Nonetheless, the performances of action recognition system are still quite far from the helm that object recognizers adorn today. This performance gap is perhaps due to the unique challenges the video modality poses from computational and algorithmic perspectives. For example, the temporal evolution of objects in videos brings in a challenging dimension to recognition that would potentially need larger training sets for learning to span the space of actions, while also demanding neural models of larger capacity. This problem is further exacerbated due to the fact that real-world video sequences often contain camera motions, varied camera poses, object/actor occlusions, and video-specific non-stationary noise, such as motion blur, among many others. Thus, a general-purpose deep-learning-based action recognition system needs to cater to these varied challenges for its success in the real world.

To this end, in this Special Issue, we envisage a unique venue to publish high-quality research that orbits around the aforementioned issues in human action recognition using deep learning techniques. We look forward to submissions that address all aspects of action recognition and aim to publish research that is unique in its approach, generalizes across varied data conditions, and demonstrates good empirical performances.

Dr. Anoop Cherian
Dr. Basura Fernando
Guest Editors

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• deep learning architectures for human action recognition
• multimodal video representation learning for action recognition including skeletons, depth etc.
• self-supervised/contrastive approaches to action recognition
• generative/adversarial/variational approaches to action recognition
• graph neural networks/transformers and variants for action recognition
• geometric approaches to action recognition
• novel tasks/applications and datasets based on action recognition
• few-shot/weakly-supervised methods for action recognition
• efficient deep learning for action recognition