Human activity recognition in artificial intelligence framework: a narrative review
Human activity recognition (HAR) has multifaceted applications due to its worldly usage of
acquisition devices such as smartphones, video cameras, and its ability to capture human …
acquisition devices such as smartphones, video cameras, and its ability to capture human …
A review of multimodal human activity recognition with special emphasis on classification, applications, challenges and future directions
Human activity recognition (HAR) is one of the most important and challenging problems in
the computer vision. It has critical application in wide variety of tasks including gaming …
the computer vision. It has critical application in wide variety of tasks including gaming …
Mvitv2: Improved multiscale vision transformers for classification and detection
In this paper, we study Multiscale Vision Transformers (MViTv2) as a unified architecture for
image and video classification, as well as object detection. We present an improved version …
image and video classification, as well as object detection. We present an improved version …
St-adapter: Parameter-efficient image-to-video transfer learning
Capitalizing on large pre-trained models for various downstream tasks of interest have
recently emerged with promising performance. Due to the ever-growing model size, the …
recently emerged with promising performance. Due to the ever-growing model size, the …
Uniformer: Unifying convolution and self-attention for visual recognition
It is a challenging task to learn discriminative representation from images and videos, due to
large local redundancy and complex global dependency in these visual data. Convolution …
large local redundancy and complex global dependency in these visual data. Convolution …
Attention bottlenecks for multimodal fusion
Humans perceive the world by concurrently processing and fusing high-dimensional inputs
from multiple modalities such as vision and audio. Machine perception models, in stark …
from multiple modalities such as vision and audio. Machine perception models, in stark …
Multiscale vision transformers
Abstract We present Multiscale Vision Transformers (MViT) for video and image recognition,
by connecting the seminal idea of multiscale feature hierarchies with transformer models …
by connecting the seminal idea of multiscale feature hierarchies with transformer models …
Vivit: A video vision transformer
We present pure-transformer based models for video classification, drawing upon the recent
success of such models in image classification. Our model extracts spatio-temporal tokens …
success of such models in image classification. Our model extracts spatio-temporal tokens …
[PDF][PDF] Is space-time attention all you need for video understanding?
Training. We train our model for 15 epochs with an initial learning rate of 0.005, which is
divided by 10 at epochs 11, and 14. During training, we first resize the shorter side of the …
divided by 10 at epochs 11, and 14. During training, we first resize the shorter side of the …
Actionclip: A new paradigm for video action recognition
The canonical approach to video action recognition dictates a neural model to do a classic
and standard 1-of-N majority vote task. They are trained to predict a fixed set of predefined …
and standard 1-of-N majority vote task. They are trained to predict a fixed set of predefined …