Workshop Program

DiffCVML 2020 will be fully virtual. Please click on the link DiffCVML20 at CVPR2020 to participate. Attendees who have registered will have received instructions on how to participate virtually in the workshop.

All timgings are in the Pacific time zone (Seattle) .

8:45 am – 9:00 am Opening Remarks and Schedule Overview

9:00 am – 9:35 am Keynote Tamal Dey, Ohio State University
Computing Homology Cycles with Certified Geometry

9:40 am – 10:15 am Keynote Lek-Heng Lim, University of Chicago
Optimization on flag manifolds

10:20 am – 10:55 am Keynote Elizabeth Munch, Michigan State University
Featurization of Persistence Diagrams using Template Functions for Machine Learning Tasks

11:00 am – 12:10 pm Oral Presentations (20 minutes each)
  1. Representations, Metrics and Statistics for Shape Analysis of Elastic Graphs
    Xiaoyang Guo, Anuj Srivastava
  2. Smooth Summaries of Persistence Diagrams and Texture Classification
    Austin Lawson, Yu-Min Chung , Michael Hull
  3. Gromov-Wasserstein Averaging in a Riemannian Framework
    Samir Chowdhury, Tom Needham
12:10 pm – 1:10 pm Lunch Break

1:10 pm – 1:45 pm Keynote Guido Montufar, University of California Los Angeles
Wasserstein Information Geometry in Generative and Discriminative Learning with Image Data

1:50 pm – 2:25 pm Keynote Pavan Turaga, Arizona State University
Revisiting Invariants with Geometry and Deep-learning

2:30 pm – 3:40 pm Oral Presentations (20 minutes each)
  1. The Weighted Euler Curve Transform for Shape and Image Analysis
    Tom Needham, Sebastian Kurtek, Qitong Jiang
  2. A Geometric ConvNet on 3D Shape Manifold for Gait Recognition
    Nadia Hosni, Boulbaba Benamor
  3. Infinitesimal Drift Diffeomorphometry Models for Population Shape Analysis
    Brian Lee, Daniel Tward, Zhiyi Hu, Alain Trouve, Michael Miller

3:45 pm – 4:45 pm Poster Presentations (5 minutes each)
  1. PI-Net: A Deep Learning Approach to Extract Topological Persistence Images
    Anirudh Som, Hongjun Choi, Karthikeyan Natesan Ramamurthy, Matthew Buman, Pavan Turaga
  2. Hierarchical Image Classification using Entailment Cone Embeddings
    Ankit Dhall, Anastasia Makarova, Octavian Ganea, Dario Pavllo, Michael Greeff, Andreas Krause
  3. AMC-Loss: Angular Margin Contrastive Loss for Improved Explainability in Image Classification
    Hongjun Choi, Anirudh Som, Pavan Turaga
  4. An Interface between Grassmann manifolds and vector spaces
    Lincon Sales de Souza, Naoya Sogi, Bernardo Gatto, Takumi Kobayashi, Kazuhiro Fukui
  5. Simplifying Transformations for a Family of Elastic Metrics on the Space of Surfaces
    Zhe Su
  6. Metric Learning with A-based Scalar Product for Image-set Recognition
    Naoya Sogi, Lincon Sales de Souza, Bernardo Gatto, Kazuhiro Fukui
  7. A generic unfolding algorithm for manifolds estimated by local linear approximations
    Jonas Myhre, Matineh Shaker, Mustafa Devrim Kaba, Robert Jenssen, Deniz Erdogmus
  8. Persistent Homology-based Projection Pursuit
    Oleg Kachan
  9. Curvature: A signature for Action Recognition in Video Sequences
    He Chen, Gregory Chirikjian
  10. Coarse-to-Fine Hamiltonian Dynamics of Hierarchical Flows in Computational Anatomy
    Michael Miller, Daniel Tward, Alain Trouve
  11. Deep Low-Rank Subspace Clustering
    Mohsen Kheirandishfard, Fariba Zohrizadeh, Farhad Kamangar
  12. Deep Learning of Warping Functions for Shape Analysis
    Elvis Nunez, Shantanu Joshi

4:45 pm – 5:00 pm Closing Remarks