Temporal-wise attention spiking neural networks for event streams classification
How to effectively and efficiently deal with spatio-temporal event streams, where the events
are generally sparse and non-uniform and have the us temporal resolution, is of great value …
are generally sparse and non-uniform and have the us temporal resolution, is of great value …
Skeletonmae: graph-based masked autoencoder for skeleton sequence pre-training
Skeleton sequence representation learning has shown great advantages for action
recognition due to its promising ability to model human joints and topology. However, the …
recognition due to its promising ability to model human joints and topology. However, the …
Transfer learning and its extensive appositeness in human activity recognition: A survey
A Ray, MH Kolekar - Expert Systems with Applications, 2023 - Elsevier
In this competitive world, the supervision and monitoring of human resources are primary
and necessary tasks to drive context-aware applications. Advancement in sensor and …
and necessary tasks to drive context-aware applications. Advancement in sensor and …
Gate-shift networks for video action recognition
S Sudhakaran, S Escalera… - Proceedings of the IEEE …, 2020 - openaccess.thecvf.com
Deep 3D CNNs for video action recognition are designed to learn powerful representations
in the joint spatio-temporal feature space. In practice however, because of the large number …
in the joint spatio-temporal feature space. In practice however, because of the large number …
A survey on video action recognition in sports: Datasets, methods and applications
To understand human behaviors, action recognition based on videos is a common
approach. Compared with image-based action recognition, videos provide much more …
approach. Compared with image-based action recognition, videos provide much more …
Temporal query networks for fine-grained video understanding
Our objective in this work is fine-grained classification of actions in untrimmed videos, where
the actions may be temporally extended or may span only a few frames of the video. We cast …
the actions may be temporally extended or may span only a few frames of the video. We cast …
Removing the background by adding the background: Towards background robust self-supervised video representation learning
Self-supervised learning has shown great potentials in improving the video representation
ability of deep neural networks by getting supervision from the data itself. However, some of …
ability of deep neural networks by getting supervision from the data itself. However, some of …
Video modeling with correlation networks
Motion is a salient cue to recognize actions in video. Modern action recognition models
leverage motion information either explicitly by using optical flow as input or implicitly by …
leverage motion information either explicitly by using optical flow as input or implicitly by …
Sportscap: Monocular 3d human motion capture and fine-grained understanding in challenging sports videos
Markerless motion capture and understanding of professional non-daily human movements
is an important yet unsolved task, which suffers from complex motion patterns and severe …
is an important yet unsolved task, which suffers from complex motion patterns and severe …
Depthwise spatio-temporal STFT convolutional neural networks for human action recognition
Conventional 3D convolutional neural networks (CNNs) are computationally expensive,
memory intensive, prone to overfitting, and most importantly, there is a need to improve their …
memory intensive, prone to overfitting, and most importantly, there is a need to improve their …