Publications

Peer-reviewed conferences and journals:

  • Conditional Generation Using Polynomial Expansions.

    Grigorios Chrysos, Markos Georgopoulos, Yannis Panagakis
    Conference on Neural Information Processing Systems (NeurIPS), 2021.
     PDF
    We propose a polynomial expansion with respect to two (or more) variables, which is applied to conditional image generation.

  • Poly-NL: Linear Complexity Non-local Layers with Polynomials.

    Francesca Babiloni, Ioannis Marras, Filippos Kokkinos, Jiankang Deng, Grigorios Chrysos, Stefanos Zafeiriou
    International Conference on Computer Vision (ICCV), 2021.
     PDF
    We cast non-local blocks as special cases of third degree polynomial functions. In addition, we propose a new non-local block that builds on this polynomial perspective but has more efficient operations, i.e., we aim to retain the expressivity of non-local layers while maintaining a linear complexity.

  • Tensor Methods in Computer Vision and Deep Learning.

    Yannis Panagakis*, Jean Kossaifi*, Grigorios Chrysos, James Oldfield, Mihalis A. Nicolaou, Anima Anandkumar, Stefanos Zafeiriou
    Proceedings of the IEEE, 2021.
     Paper  Code
    We provide an in-depth review of tensors and tensor methods in the context of representation learning and deep learning, with a particular focus on computer vision applications. We also provide jupyter notebooks with accompanying code.

  • Unsupervised Controllable Generation with Self-Training.

    Grigorios Chrysos, Jean Kossaifi, Zhiding Yu, Anima Anandkumar
    International Joint Conference on Neural Networks (IJCNN), 2021.
     PDFOral.
    We modify the GAN architecture to achieve interpretable generation without using any supervision.

  • Deep Polynomial Neural Networks.

    Grigorios Chrysos, Stylianos Moschoglou, Giorgos Bouritsas, Jiankang Deng, Yannis Panagakis, Stefanos Zafeiriou
    IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), 2021. (impact factor 2019: 17.861)
     Paper Paper (open access)  Code  Blog post  1-minute video
    We propose a new class of architectures that use polynomial expansions to approximate the target functions. We validate the proposed polynomial expansions (i.e. Π-nets) in diverse experiments: data generation, data classifcation, face recognition and non-euclidean representation learning.

  • Non-adversarial polynomial synthesis.

    Grigorios Chrysos, Yannis Panagakis
    Pattern Recognition Letters, 2020.
     Paper
    We propose a decoder-only generator that uses a polynomial expansion to synthesize new images.

  • Reconstructing the Noise Manifold for Image Denoising.

    Ioannis Marras, Grigorios Chrysos, Ioannis Alexiou, Gregory Slabaugh, Stefanos Zafeiriou
    European Conference on Computer Vision (ECCV), 2020.
     PDF
    We propose learning the noise variance manifold along with typical image-to-image translation to obtain improved denoising.

  • Multilinear Latent Conditioning for Generating Unseen Attribute Combinations.

    Markos Georgopoulos, Grigorios Chrysos, Maja Pantic, Yannis Panagakis
    International Conference on Machine Learning (ICML), 2020.
     PDF
    We extend conditional VAE to capture multiplicative interactions of the (annotated) attributes in the latent space. This enables generating images with unseen attribute combinations during training.

  • RoCGAN: Robust Conditional GAN.

    Grigorios Chrysos, Jean Kossaifi, Stefanos Zafeiriou
    International Journal of Computer Vision (IJCV), 2020. (impact factor 2019: 11.042)
     Paper (open access)  Code
    We leverage structure in the output domain of a conditional data generation task (e.g., super-resolution) to improve the synthesized image. We experimentally validate that this results in synthesized images more robust to noise. Extension of the conference paper.

  • Π-nets: Deep Polynomial Neural Networks.

    Grigorios Chrysos, Stylianos Moschoglou, Giorgos Bouritsas, Yannis Panagakis, Jiankang Deng, Stefanos Zafeiriou
    Computer Vision and Pattern Recognition Conference (CVPR), 2020.
     PDF  Code  Blog post  1-minute video  Poster
    We use a high-order polynomial expansion as a function approximation method. The unknown parameters of the polynomial (i.e., high-order tensors) are estimated using a collective tensor factorization.

  • Motion Deblurring of Faces.

    Grigorios Chrysos, Paolo Favaro, Stefanos Zafeiriou
    International Journal of Computer Vision (IJCV), 2019. (impact factor 2019: 11.042)
     Paper (open access)
    We introduce a framework for tackling motion blur of faces. Our method simulates motion blur using averaging of video frames, while we collect a dataset that contains millions of such frames.

  • The Menpo Benchmark for Multi-pose 2D and 3D Facial Landmark Localisation and Tracking.

    Jiankang Deng, Anastasios Roussos, Grigorios Chrysos, Evangelos Ververas, Irene Kotsia, Jie Shen, Stefanos Zafeiriou
    International Journal of Computer Vision (IJCV), 2019. (impact factor 2019: 11.042)
     Paper (open access)
    A semi-automatic framework is proposed for annotating challenging deformable images and videos.

  • Robust Conditional Generative Adversarial Networks.

    Grigorios Chrysos, Jean Kossaifi, Stefanos Zafeiriou
    International Conference on Learning Representations (ICLR), 2019.
     PDF  Code  Poster

    The topic of conditional data generation task (e.g., super-resolution) is the focus of this work. We introduce a new pathway in the encoder-decoder generator to improve the synthesized image.

  • A Comprehensive Performance Evaluation of Deformable Face Tracking ''In-the-Wild''.

    Grigorios Chrysos, Epameinondas Antonakos, Patrick Snape, A. Asthana, Stefanos Zafeiriou
    International Journal of Computer Vision (IJCV), 2018. (impact factor 2019: 11.042)
     Paper (open access)  Code
    We conduct a large-scale study of deformable face tracking `in-the-wild', i.e., with videos captured in unrestricted conditions.

  • IPST: Incremental Pictorial Structures for model-free Tracking of deformable objects.

    Grigorios Chrysos, Epameinondas Antonakos, Stefanos Zafeiriou
    IEEE Transactions on Image Processing (TIP), 2018. (impact factor 2019: 9.34)
     Paper
    We introduce incremental pictorial structures for tracking deformable (part-based) objects, e.g., human body parts or fiducial points in the face.

  • PD2T: Person-specific Detection, Deformable Tracking.

    Grigorios Chrysos, Stefanos Zafeiriou
    IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), 2017. (impact factor 2019: 17.861)
     Paper
    We propose a framework for extracting object-specific statistics for tracking a (deformable) object.

  • Surface Based Object Detection in RGBD Images.

    Siddhartha Chandra, Grigorios Chrysos, Iasonas Kokkinos
    British Machine Vision Conference (BMVC), 2015.
     PDF Oral, acceptance rate: 7%.
    We extend standard object detection pipelines by leveraging depth information and introducing viewpoint based mixture components.

Workshop papers:

  • Self-Supervised Neural Architecture Search for Imbalanced Datasets.

    Aleksandr Timofeev, Grigorios Chrysos, Volkan Cevher
    International Conference on Machine Learning Workshops (ICMLW), 2021.
     PDF
    We propose a neural architecture search (NAS) framework for real world tasks: (a) in the absence of labels, (b) in the presence of imbalanced datasets, (c) on a constrained computational budget.

  • Unsupervised Controllable Generation with Self-Training.

    Grigorios Chrysos, Jean Kossaifi, Zhiding Yu, Anima Anandkumar
    International Conference on Machine Learning Workshops (ICMLW), 2020.
     PDF
    We modify the GAN architecture to achieve interpretable generation without using any supervision.

  • The 3D Menpo Facial Landmark Tracking Challenge.

    Stefanos Zafeiriou*, Grigorios Chrysos*, Anastasios Roussos*, Evangelos Ververas, J. Deng, George Trigeorgis
    International Conference on Computer Vision Workshops (ICCVW), 2017.
     PDF
    The first large-scale dataset with 3D annotations of facial landmarkrs is introduced.

  • Deep Face Deblurring.

    Grigorios Chrysos, Stefanos Zafeiriou
    Computer Vision and Pattern Recognition Conference Workshops (CVPRW), 2017.
     PDF
    A method for face deblurring is proposed. The method utilizes weak supervision to guide the learning of the deep neural network.

  • The Menpo Facial Landmark Localisation Challenge.

    Stefanos Zafeiriou, George Trigeorgis, Grigorios Chrysos, J. Deng, Jie Shen
    Computer Vision and Pattern Recognition Conference Workshops (CVPRW), 2017.
     PDF
    The first large-scale dataset with annotations of facial landmarkrs in both (semi-)frontal and profile poses is introduced.

  • The First Facial Landmark Tracking in-the-Wild Challenge: Benchmark and Results.

    Jie Shen, Stefanos Zafeiriou, Grigorios Chrysos, Jean Kossaifi, Georgios Tzimiropoulos, Maja Pantic
    International Conference on Computer Vision Workshops (ICCVW), 2015.
     PDF
    The first large-scale dataset for facial landmark tracking is introduced.

  • Offline Deformable Face Tracking in Arbitrary Videos.

    Grigorios Chrysos, Epameinondas Antonakos, Stefanos Zafeiriou, Patrick Snape
    International Conference on Computer Vision Workshops (ICCVW), 2015.
     PDF
    We propose a framework that can extract object-specific statistics and can be used for tracking long sequences of videos.