Video-based human action recognition is currently one of the most active research areas in
computer vision. Various research studies indicate that the performance of action …

Human activity recognition (HAR) is an important research problem in computer vision. This
problem is widely applied to building applications in human–machine interactions …

Although augmentations (eg, perturbation of graph edges, image crops) boost the efficiency
of Contrastive Learning (CL), feature level augmentation is another plausible …

Abstract We design a Kernelized Few-shot Object Detector by leveraging kernelized
matrices computed over multiple proposal regions, which yield expressive non-linear …

Many skeletal action recognition models use GCNs to represent the human body by 3D
body joints connected body parts. GCNs aggregate one-or few-hop graph neighbourhoods …

Many few-shot learning models focus on recognising images. In contrast, we tackle a
challenging task of few-shot action recognition from videos. We build on a C3D encoder for …

Abstract Dynamic Time Warping (DTW) is used for matching pairs of sequences and
celebrated in applications such as forecasting the evolution of time series, clustering time …

Skeleton sequences are lightweight and compact and thus are ideal candidates for action
recognition on edge devices. Recent skeleton-based action recognition methods extract …

Video deblurring models exploit consecutive frames to remove blurs from camera shakes
and object motions. In order to utilize neighboring sharp patches, typical methods rely …

We propose a Few-shot Learning pipeline for 3D skeleton-based action recognition by Joint
Temporal and Camera Viewpoint Alignment. To factor out misalignment between query and …