Video transformers: A survey
Transformer models have shown great success handling long-range interactions, making
them a promising tool for modeling video. However, they lack inductive biases and scale …
them a promising tool for modeling video. However, they lack inductive biases and scale …
Spatio-temporal attention networks for action recognition and detection
Recently, 3D Convolutional Neural Network (3D CNN) models have been widely studied for
video sequences and achieved satisfying performance in action recognition and detection …
video sequences and achieved satisfying performance in action recognition and detection …
Improving action segmentation via graph-based temporal reasoning
Temporal relations among multiple action segments play an important role in action
segmentation especially when observations are limited (eg, actions are occluded by other …
segmentation especially when observations are limited (eg, actions are occluded by other …
Holistic interaction transformer network for action detection
Actions are about how we interact with the environment, including other people, objects, and
ourselves. In this paper, we propose a novel multi-modal Holistic Interaction Transformer …
ourselves. In this paper, we propose a novel multi-modal Holistic Interaction Transformer …
A survey on deep learning-based spatio-temporal action detection
Spatio-temporal action detection (STAD) aims to classify the actions present in a video and
localize them in space and time. It has become a particularly active area of research in …
localize them in space and time. It has become a particularly active area of research in …
A two-stage attentive network for single image super-resolution
Recently, deep convolutional neural networks (CNNs) have been widely explored in single
image super-resolution (SISR) and contribute remarkable progress. However, most of the …
image super-resolution (SISR) and contribute remarkable progress. However, most of the …
Empowering relational network by self-attention augmented conditional random fields for group activity recognition
This paper presents a novel relational network for group activity recognition. The core of our
network is to augment the conditional random fields (CRF), amenable to learning inter …
network is to augment the conditional random fields (CRF), amenable to learning inter …
Mmpoint-gnn: Graph neural network with dynamic edges for human activity recognition through a millimeter-wave radar
P Gong, C Wang, L Zhang - 2021 International Joint …, 2021 - ieeexplore.ieee.org
Human activity recognition has a wide range of application prospects and research
significance in intelligent monitoring, assisted driving and human-computer interaction, such …
significance in intelligent monitoring, assisted driving and human-computer interaction, such …
Wavelet-based self-attention GAN with collaborative feature fusion for image inpainting
L Shen, J Yan, X Sun, B Li, Z Pan - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Image inpainting is a significant task in the applications of computer vision, that aims to fill in
damaged regions with visually realistic contents. With the development of deep learning …
damaged regions with visually realistic contents. With the development of deep learning …
A*: Atrous spatial temporal action recognition for real time applications
Deep learning has become a popular tool across various fields and is increasingly being
integrated into real-world applications such as autonomous driving cars and surveillance …
integrated into real-world applications such as autonomous driving cars and surveillance …