Human Action Recognition (HAR) aims to understand human behavior and assign a label to
each action. It has a wide range of applications, and therefore has been attracting increasing …

Deep neural models, in recent years, have been successful in almost every field, even
solving the most complex problem statements. However, these models are huge in size with …

In recent years, deep neural networks have been successful in both industry and academia,
especially for computer vision tasks. The great success of deep learning is mainly due to its …

Federated learning (FL) has been proposed to protect data privacy and virtually assemble
the isolated data silos by cooperatively training models among organizations without …

Semi-supervised video action recognition tends to enable deep neural networks to achieve
remarkable performance even with very limited labeled data. However, existing methods are …

We propose a novel knowledge distillation approach to facilitate the transfer of dark
knowledge from a teacher to a student. Contrary to most of the existing methods that rely on …

Event cameras sense per-pixel intensity changes and produce asynchronous event streams
with high dynamic range and less motion blur, showing advantages over the conventional …

Visual modality is one of the most dominant modalities for current continuous emotion
recognition methods. Compared to which the EEG modality is relatively less sound due to its …

In this paper we make the first attempt at achieving the cross-modal (ie image-to-events)
adaptation for event-based object recognition without accessing any labeled source image …

Cost-effective depth and infrared sensors as alternatives to usual RGB sensors are now a
reality, and have some advantages over RGB in domains like autonomous navigation and …