Research
Deep learning for computer vision applications
Deep learning for computer vision applications
High accurate object recognition (unconstrained face recognition)
ResNet 101 with center loss
Fast 3D face model approximation using a deep learning pipeline
Object detection and tracking
RCNN, Faster RCNN, MDNet
Mask RCNN
GANs to generate infra-red object images
Motion prediction from Mocap data
high confidence detection, tracking, and classification
computer graphics based data augmentation
image/video translation for generating training data
loss functions
multiple objects tracking and motion pattern analysis
3d inference
classical optimization framework
learning 3d representations
inferring 3d primitives from 3D data
deformable objects