Search Results for author:
Found 7 papers, 4 papers with code
ActNetFormer: Transformer-ResNet Hybrid Method for Semi-Supervised Action Recognition in Videos
Our framework leverages both labeled and unlabelled data to robustly learn action representations in videos, combining pseudo-labeling with contrastive learning for effective learning from both types of samples.
Action Recognition In Videos
Activity Recognition In Videos
+4
Transferable Class-Modelling for Decentralized Source Attribution of GAN-Generated Images
GAN-generated deepfakes as a genre of digital images are gaining ground as both catalysts of artistic expression and malicious forms of deception, therefore demanding systems to enforce and accredit their ethical use.
Graph Autoencoders for Embedding Learning in Brain Networks and Major Depressive Disorder Identification
Our new framework demonstrates feasibility of learning graph embeddings on brain networks to provide discriminative information for diagnosis of brain disorders.
Dual-stream shallow networks for facial micro-expression recognition
Micro-expressions are spontaneous, brief and subtle facial muscle movements that exposes underlying emotions.
Enriched Long-term Recurrent Convolutional Network for Facial Micro-Expression Recognition
Facial micro-expression (ME) recognition has posed a huge challenge to researchers for its subtlety in motion and limited databases.
Less is More: Micro-expression Recognition from Video using Apex Frame
The apex frame of a video contains the highest intensity of expression changes among all frames, while the onset is the perfect choice of a reference frame with neutral expression.
Micro Expression Recognition
Micro-Expression Recognition
+1
Spontaneous expression classification in the encrypted domain
To date, most facial expression analysis have been based on posed image databases and is carried out without being able to protect the identity of the subjects whose expressions are being recognised.
