Adobe at NeurIPS 2022 

December 1, 2022

Tags: Conferences

This Adobe co-authored NeurIPS paper presents a dataset that contains over one million fractured objects, which can be used for various machine learning applications.

Adobe researchers have co-authored twelve main conference papers, including four accepted as oral presentations, and six workshop papers for this year’s Neural Information Processing Systems conference (NeurIPS), held from November 28 to December 9, 2022.  

NeurIPS is a multi-track interdisciplinary annual meeting that includes invited talks, demonstrations, symposia, and oral and poster presentations of refereed papers. Alongside the conference is a professional exposition focusing on machine learning in practice, a series of tutorials, and topical workshops that provide a less formal setting for the exchange of ideas.  

Nearly all of Adobe’s papers are the result of student internships or other collaborations with university students and faculty. Please check out the Adobe Research careers page to learn more about internships and full-time career opportunities.

Main conference: Adobe co-authored papers 

Oral papers  

Breaking Bad: A Dataset for Geometric Fracture and Reassembly 
Silvia Sellán, Yun-Chun Chen, Ziyi Wu, Animesh Garg, Alec Jacobson 

CyCLIP: Cyclic Contrastive Language-Image Pretraining 
Shashank Goel, Hritik Bansal, Sumit Bhatia, Ryan Rossi, Vishwa Vinay, Aditya Grover 

NeuForm: Adaptive Overfitting for Neural Shape Editing 
Connor Lin, Niloy Mitra, Gordon Wetzstein, Leonidas Guibas, Paul Guerrero 

NeMF: Neural Motion Fields for Kinematic Animation 
Chengan He, Jun Saito, James Zachary, Holly Rushmeier, Yi Zhou 

Poster papers  

Delving into OOD Detection with Vision-Language Representations 
Yifei Ming, Ziyang Cai, Jiuxiang Gu, Yiyou Sun, Wei Li, Yixuan Li 

Dynamic Tensor Product Regression 
Aravind Reddy, Zhao Song, Lichen Zhang 

Fast Distance Oracles for Any Symmetric Norm 
Yichuan Deng, Zhao Song, Omri Weinstein, Ruizhe Zhang 

Monocular Dynamic View Synthesis: A Reality Check 
Hang Gao, Ruilong Li, Shubham Tulsiani, Bryan Russell, Angjoo Kanazawa 

Root Cause Analysis of Failures in Microservices through Causal Discovery 
Azam Ikram, Sarthak Chakraborty, Subrata Mitra, Shiv Saini, Saurabh Bagchi, Murat Kocaoglu 

Sample Constrained Treatment Effect Estimation 
Raghavendra Addanki, David Arbour, Tung Mai, Cameron Musco, Anup Rao 

VectorAdam for Rotation Equivariant Geometry Optimization 
Selena Zihan Ling, Nicholas Sharp, Alec Jacobson 

VITA: Video Instance Segmentation via Object Token Association 
Miran Heo, Sukjun Hwang, Seoung Wug Oh, Joon-Young Lee, Seon Joo Kim

Workshop: Adobe co-authored papers 

Contrastive Learning on Synthetic Videos for GAN Latent Disentangling 
Kevin Duarte, Wei-An Lin, Ratheesh Kalarot, Jingwan Lu, Eli Shechtman, Shabnam Ghadar, Mubarak Shah 
Presented at the workshop Synthetic Data for Empowering ML Research 

Improving cross-modal attention via object detection 
Yongil Kim, Yerin Hwang, Seunghyun Yoon, Hyeongu Yun, Kyomin Jung 
Presented at the workshop All Things Attention: Bridging Different Perspectives on Attention 

Trajectory-based Explainability Framework for Offline RL 
Shripad Deshmukh, Arpan Dasgupta, Chirag Agarwal, Nan Jiang, Balaji Krishnamurthy, Georgios Theocharous, Jayakumar Subramanian 
Presented at the workshop Offline Reinforcement Learning Workshop: Offline RL as a “Launchpad” 

Using Informative Data Subsets for Efficient Training of Large Language Models: An Initial Study 
H S V N S Kowndinya Renduchintala, Krishnateja Killamsetty, Sumit Bhatia, Milan Aggarwal, Ganesh Ramakrishnan, Rishabh Iyer 
Presented at the workshop Efficient Natural Language and Speech Processing 

Videogenic: Video Highlights via Photogenic Moments  
David Chuan-En Lin, Fabian Caba, Joon-Young Lee, Oliver Wang, Nikolas Martelaro                             
Presented at the workshop Machine Learning for Creativity and Design 

VideoMap: Video Editing in Latent Space 
David Chuan-En Lin, Fabian Caba Heilbron, Joon-Young Lee, Oliver Wang, Nikolas Martelaro           
Presented at the workshop Machine Learning for Creativity and Design 

Area Chairs 

Georgios Theocharous 

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