Search Results for author:
Found 7 papers, 2 papers with code
READ: Reciprocal Attention Discriminator for Image-to-Video Re-Identification
Person re-identification (re-ID) is the problem of visually identifying a person given a database of identities.
Exploring Temporally Dynamic Data Augmentation for Video Recognition
The magnitude of augmentation operations on each frame is changed by an effective mechanism, Fourier Sampling that parameterizes diverse, smooth, and realistic temporal variations.
Frequency Selective Augmentation for Video Representation Learning
FreqAug stochastically removes specific frequency components from the video so that learned representation captures essential features more from the remaining information for various downstream tasks.
VideoMix: Rethinking Data Augmentation for Video Classification
Recent data augmentation strategies have been reported to address the overfitting problems in static image classifiers.
Regularization on Spatio-Temporally Smoothed Feature for Action Recognition
We present that selective regularization on this locally smoothed feature makes a model handle the low-frequency and high-frequency component distinctively, resulting in performance improvement.
Predictive Coding-based Deep Dynamic Neural Network for Visuomotor Learning
This study presents a dynamic neural network model based on the predictive coding framework for perceiving and predicting the dynamic visuo-proprioceptive patterns.
Achieving Synergy in Cognitive Behavior of Humanoids via Deep Learning of Dynamic Visuo-Motor-Attentional Coordination
The current study examines how adequate coordination among different cognitive processes including visual recognition, attention switching, action preparation and generation can be developed via learning of robots by introducing a novel model, the Visuo-Motor Deep Dynamic Neural Network (VMDNN).
