Workshop Program

DiffCVML 2021 will be fully virtual and will be held live on Friday June 25 2021. Attendees who have registered will have received instructions on how to participate virtually in the workshop.

All timgings are in the Pacific time zone (San Francisco) .

7:55 am – 8:00 am Opening Remarks and Schedule Overview

8:00 am – 8:25 am Keynote Aasa Feragen, Technical University of Denmark
Predicting Graphs

8:30 am – 8:55 am Keynote Justin Solomon, Massachusetts Institute of Technology
Learning from Irregularly-Structured Geometric Data

9:00 am – 9:25 am Keynote Soren Hauberg, Technical University of Denmark
Identifiability in Latent Variable Models

9:30 am – 9:55 am Keynote Miaomiao Zhang, University of Virginia Charlottesville
Deep Networks for Predictive Diffeomorphic Image Registration and Statistical Shape Analysis

10:00 pm – 10:30 pm Poster Session (offline/hybrid)

  1. Uniting Stereo and Depth-from-Defocus: A Thin Lens-based Variational Framework for Multiview Reconstruction
    Robert D Friedlander, Huizong Yang, Anthony Yezzi

  2. Geometric empirical Bayesian model for classification of functional data under diverse sampling regimes
    James A Matuk, Karthik Bharath, Oksana Chkrebtii, Sebastian Kurtek

  3. Deep Spherical Manifold Gaussian Kernel for Unsupervised Domain Adaptation
    Youshan Zhang, Brian D. Davison

  4. GILDA++: Grassmann Incremental Linear Discriminant Analysis
    Navya Nagananda, Andreas Savakis

  5. Shape My Face: Registering 3D Face Scans by Surface-to-Surface Translation
    Mehdi Bahri, Michael Bronstein, Stefanos Zafeiriou

  6. SrvfRegNet: Elastic Function Registration Using Deep Networks
    Chao Chen, Anuj Srivastava

  7. Multi scale diffeomorphic metric mapping of spatial transcriptomics datasets
    Michael Miller, Jean Fan, Daniel Tward

  8. SrvfNet: A Generative Network for Unsupervised Multiple Diffeomorphic Functional Alignment
    Elvis Nunez, Andrew Lizarraga, Shantanu H Joshi

  9. Towards Diffeomorphism Invariant Convolution Neural Networks
    Pengyu Kan, Rudrasis Chakraborty, Vishnu Suresh Lokhande, Vikas Singh

  10. Topological Loss and Its Application in Biomedical Image Analysis
    Chao Chen

10:30 am – 10:55 am Keynote Jose Perea, Michigan State University
Approximate Vector Bundles

11:00 am – 12:00 pm Oral Presentations (20 minutes each)

  1. A Sheaf and Topology Approach to Detecting Local Merging Relations in Digital Image
    Chuan-Shen Peter Hu, Yu-Min Chung

  2. Learning low bending and low distortion manifold embeddings
    Juliane Braunsmann, Marko Rajkovic, Martin Rumpf, Benedikt Wirth

  3. Supervised deep learning of elastic SRV distances on the shape space of curves
    Emmanuel L Hartman, Yashil Sukurdeep, Nicolas Charon, Eric Klassen, Martin Bauer