… a new methodology using convolution neural networks (CNN) with varying … selection of the
optimal video representation and in the … An encoder learns advanced features based on an …

… of technical solutions include wearable sensors [1] and [2], … one set of spatio-temporal features
and employed the OFF CNN … video is compressed using a customized HEVC encoder that …

… video for human activity … using Encoder-Decoder Network (EDNet) which uses feature
enhancement and feature fusion to enable the information transfer between encoder and decoder…

… the spatio-temporal resolution trade-off, we use a compressive video sensing technique to
… coded exposure to CNN encoder and classification network as a task model. We trains this …

… an ensemble on trunk-branch where TBE-CNN shares distinct CNN’s lowand middle-level …
video codec is used to extract motion information. The motion vector, the magnitude of motion …

… learned features from the raw sensor data.Spatial and temporal … recognition problem, whose
encoder transforms a set of … of our video encoding as a spatio-temporal stream to a CNN to …

… classification methods to learn Spatio-temporal information of … CNN architectures are not
suitable for video processing. … idea of using LSTM blocks (decoder) after convolution blocks (…

… or spatio-temporal resolution so that jointly optimizing the video … (CNNs) composed of two
modules: a video encoder and a … : We use Kinetics400 [28] as the auxiliary video classification …

… CNN and transformer based model to learn spatio-temporal interconnections between
sensor … The encoder includes a full spatio-temporal attention module as well as a suggested …

… our detector’s performance both at frame- and video… uses a set prediction framework [3]
with a transformer head (plus 3 layers for each encoder- and decoder-transformer) on top a CNN …