EquiVision

Exploiting symmetry in structured data is a powerful way to improve the generalization ability, data efficiency, and robustness of AI systems, which leads to the research direction of equivariant deep learning. Showing its effectiveness, it has been widely adopted in a large variety of subareas of computer vision, from 2D image analysis to 3D perception, as well as further applications such as medical imaging and robotics.

Our topics include but are not limited to:

Theoretical foundations of equivariant deep learning with symmetry and group theory.
Equivariance by design: Neural network architectures and mathematical guarantees.
Equivariance from data: Learning equivariant and invariant features.
Applications in 2D and 3D computer vision and robotics.
Applications in broader science: computational biology, medicine, natural science, etc.
Equivariance in the large-model era and potential future directions.

Keynote Speakers

Leonidas Guibas
Stanford

Haggai Maron
Technion & NVIDIA

Carlos Esteves
Google

Erik Bekkers
UvA

Nina Miolane
UCSB

Tutorial

Stefanos Pertigkiozoglou
UPenn

Evangelos Chatzipantazis
UPenn

Schedule

Session 1: Equivariance Theory and Network Design	08:30-11:45
Opening Remarks and Welcome	08:30-08:45
Keynote Talk: Haggai Maron Exploiting Symmetries for Learning in Deep Weight Spaces	08:45-09:30
Keynote Talk: Leonidas Guibas Learning and Enforcing Equivariance	09:30-10:15
Coffee Break	10:15-10:30
Keynote Talk: Erik Bekkers Neural Ideograms and Geometry-Grounded Representation Learning	10:30-11:15
Spotlight Talk LeaF: Learning Frames for 4D Point Cloud Sequence Understanding	11:15-11:30
Spotlight Talk EquiAdapt: Equivariant Adaptation of Large Pretrained Models	11:30-11:45
Lunch Break	11:45-12:30
Accepted Paper Poster Session	12:30-14:00
Session 2: Applications in Computer Vision and Beyond	14:00-17:30
Keynote Talk: Carlos Esteves Geometric Deep Learning for Weather	14:00-14:45
Keynote Talk: Nina Miolane Hierarchical G-Equivariance in Vision	14:45-15:30
Coffee Break	15:30-15:45
Spotlight Talk Diffusion-EDFs: Bi-equivariant Denoising Generative Modeling on SE(3) for Visual Robotic Manipulation	15:45-16:00
Spotlight Talk DiffAssemble: A Unified Graph-Diffusion Model for 2D and 3D Reassembly	16:00-16:15
Tutorial: Getting Started with Equivariant Networks	16:15-17:15
Conclusion	17:15-17:30

Accepted Papers

(Spotlight) LeaF: Learning Frames for 4D Point Cloud Sequence Understanding Yunze Liu, Junyu Chen, Zekai Zhang, Jingwei Huang, Li Yi
SERNet-Former: Semantic Segmentation by Efficient Residual Network with Attention-Boosting Gates and Attention-Fusion Networks Serdar Erisen	[Poster]
Living Scenes: Multi-object Relocalization and Reconstruction in Changing 3D Environments Liyuan Zhu, Shengyu Huang, Konrad Schindler, Iro Armeni
Making Vision Transformers Truly Shift-Equivariant Renan A. Rojas-Gomez, Teck-Yian Lim, Minh N. Do, Raymond A. Yeh	[Poster]
Stability Analysis of Equivariant Convolutional Representations Through The Lens of Equivariant Multi-layered CKNs Soutrik Roy Chowdhury
TetraSphere: A Neural Descriptor for O(3)-Invariant Point Cloud Analysis Pavlo Melnyk, Andreas Robinson, Michael Felsberg, Mårten Wadenbäck	[Poster]
SurfelReloc: Surfel-based 3D Registration with Equivariant Features Xueyang Kang, Hang Zhao, Zhaoliang Luan, Patrick Vandewalle, Kourosh Khoshelham	[Poster]
Improving Equivariance in State-of-the-Art Supervised Depth and Normal Predictors Yuanyi Zhong, Anand Bhattad, Yu-Xiong Wang, David Forsyth
(Spotlight) Diffusion-EDFs: Bi-equivariant Denoising Generative Modeling on SE(3) for Visual Robotic Manipulation Hyunwoo Ryu, Jiwoo Kim, Hyunseok An, Junwoo Chang, Joohwan Seo, Taehan Kim, Yubin Kim, Chaewon Hwang, Jongeun Choi, Roberto Horowitz	[Poster]
Learning SO(3)-Invariant Semantic Correspondence via Local Shape Transform Chunghyun Park, Seungwook Kim, Jaesik Park, Minsu Cho	[Poster]
Neural Processing of Tri-Plane Hybrid Neural Fields Adriano Cardace, Pierluigi Zama Ramirez, Francesco Ballerini, Allan Zhou, Samuele Salti, Luigi di Stefano	[Poster]
(Spotlight) EquiAdapt: Equivariant Adaptation of Large Pretrained Models Arnab Kumar Mondal, Siba Smarak Panigrahi, Sékou-Oumar Kaba, Sai Rajeswar, Siamak Ravanbakhsh	[Poster]
RiEMann: Near Real-Time SE(3)-Equivariant Robot Manipulation without Point Cloud Segmentation Chongkai Gao, Zhengrong Xue, Shuying Deng, Tianhai Liang, Siqi Yang, Lin Shao, Huazhe Xu	[Poster]
Equivariance versus Augmentation for Spherical Images Jan E Gerken, Oscar Carlsson, Hampus Linander, Fredrik Ohlsson, Christoffer Petersson, Daniel Persson	[Poster]
Leveraging SE(3) Equivariance for Learning 3D Geometric Shape Assembly Ruihai Wu, Chenrui Tie, Yushi Du, Yan Shen, Hao Dong	[Poster]
EqvAfford: SE(3) Equivariance for Point-Level Affordance Learning Yue Chen, Chenrui Tie, Ruihai Wu, Hao Dong	[Poster]
Color Equivariant Network Felix O'Mahony, Yulong Yang, Christine Allen-Blanchette	[Poster]
(Spotlight) DiffAssemble: A Unified Graph-Diffusion Model for 2D and 3D Reassembly Stefano Fiorini, Gianluca Scarpellini, Francesco Giuliari, Pietro Morerio, Alessio Del Bue	[Poster]
Steerers: A framework for rotation equivariant keypoint descriptors Georg Bökman, Johan Edstedt, Michael Felsberg, Fredrik Kahl	[Poster]
Improved Canonicalization for Model Agnostic Equivariance Siba Smarak Panigrahi, Arnab Kumar Mondal