Pinned Repositories
basel-face-pipeline
Bayesian-Amodal
[CVPR '22] Amodal Segmentation through Out-of-Task andOut-of-Distribution Generalization with a Bayesian Model
CompositionalNets
Official implementation of CVPR2020 paper: "Compositional Convolutional Neural Networks: A Deep Architecture with Innate Robustness to Partial Occlusion" https://arxiv.org/abs/2003.04490
iccv21-adv-workshop.github.io
models
Models and examples built with TensorFlow
Occlusion-Robust-MoFA
RingNet
Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision
NeMo
The official implementation of NeMo: Neural Mesh Models of Contrastive Features for Robust 3D Pose Estimation [ICLR-2021]. https://arxiv.org/pdf/2101.12378.pdf
OccludedPASCAL3D
The OccludedPASCAL3D+ is a dataset generated via superimposing occluder to PASCAL3D+ dataset for multiple computer vision tasks.
parametric-face-image-generator
Generate fully parametric face images from the Basel Face Model 2017
AdamKortylewski's Repositories
AdamKortylewski/CompositionalNets
Official implementation of CVPR2020 paper: "Compositional Convolutional Neural Networks: A Deep Architecture with Innate Robustness to Partial Occlusion" https://arxiv.org/abs/2003.04490
AdamKortylewski/RingNet
Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision
AdamKortylewski/Bayesian-Amodal
[CVPR '22] Amodal Segmentation through Out-of-Task andOut-of-Distribution Generalization with a Bayesian Model
AdamKortylewski/iccv21-adv-workshop.github.io
AdamKortylewski/models
Models and examples built with TensorFlow
AdamKortylewski/Occlusion-Robust-MoFA
AdamKortylewski/basel-face-pipeline
AdamKortylewski/BNN_Informed_MCMC
AdamKortylewski/generative-vision.github.io
AdamKortylewski/parametric-face-image-generator
Generate fully parametric face images from the Basel Face Model 2017
AdamKortylewski/scala-tf-renderer
Renderer in scala and tensorflow.
AdamKortylewski/tree_regularized_cnn
AdamKortylewski/UncertaintyNN
Implementation and evaluation of different approaches to get uncertainty in neural networks