Search Results for author:
Found 11 papers, 1 papers with code
Clusformer: A Transformer Based Clustering Approach to Unsupervised Large-Scale Face and Visual Landmark Recognition
This work therefore presents the Clusformer, a simple but new perspective of Transformer based approach, to automatic visual clustering via its unsupervised attention mechanism.
Improving Text to Image Generation using Mode-seeking Function
Generative Adversarial Networks (GANs) have long been used to understand the semantic relationship between the text and image.
LIAAD: Lightweight Attentive Angular Distillation for Large-scale Age-Invariant Face Recognition
This work presents a novel Lightweight Attentive Angular Distillation (LIAAD) approach to Large-scale Lightweight AiFR that overcomes these limitations.
Toponym Identification in Epidemiology Articles - A Deep Learning Approach
These results underline the importance of domain specific embedding as well as specific linguistic features in toponym detection in medical journals.
Automatic Face Aging in Videos via Deep Reinforcement Learning
This paper presents a novel approach to synthesize automatically age-progressed facial images in video sequences using Deep Reinforcement Learning.
Longitudinal Face Aging in the Wild - Recent Deep Learning Approaches
Face Aging has raised considerable attentions and interest from the computer vision community in recent years.
Learning from Longitudinal Face Demonstration - Where Tractable Deep Modeling Meets Inverse Reinforcement Learning
The proposed model is experimented in both tasks of face aging synthesis and cross-age face verification.
Deep Appearance Models: A Deep Boltzmann Machine Approach for Face Modeling
This paper presents a novel Deep Appearance Models (DAMs) approach, an efficient replacement for AAMs, to accurately capture both shape and texture of face images under large variations.
Robust Deep Appearance Models
In this approach, two crucial components of face images, i. e. shape and texture, are represented by Deep Boltzmann Machines and Robust Deep Boltzmann Machines (RDBM), respectively.
Longitudinal Face Modeling via Temporal Deep Restricted Boltzmann Machines
The Temporal Deep Restricted Boltzmann Machines based age progression model together with the prototype faces are then constructed to learn the aging transformation between faces in the sequence.
Beyond Principal Components: Deep Boltzmann Machines for Face Modeling
The "interpretation through synthesis", i. e.
