all publications

2022

  1. arXiv’22
    SlotFormer: Unsupervised Visual Dynamics Simulation with Object-Centric Models
    Ziyi Wu, Nikita Dvornik, Klaus Greff, Thomas Kipf, and Animesh Garg
    arXiv preprint arXiv:2210.05861 2022
    arXiv HTML
  2. BMVC’22
    SAGE: Saliency-Guided Mixup with Optimal Rearrangements
    Avery Ma, Nikita Dvornik, Ran Zhang, Leila Pishdad, Konstantinos G Derpanis, and Afsaneh Fazly
    In British Machine Vision Conference (BMVC) 2022
    arXiv
  3. CRL’22
    SlotFormer: Long-Term Dynamic Modeling in Object-Centric Models
    Ziyi Wu, Nikita Dvornik, Klaus Greff, Xi Jiaqi, Thomas Kipf, and Animesh Garg
    CRL@UAI workshop 2022
    HTML Website
  4. ECCV’22
    Oral
    Flow graph to Video Grounding for Weakly-supervised Multi-Step Localization
    Nikita Dvornik, Isma Hadji, Hai Pham, Dhaivat Bhatt, Brais Martinez, Afsaneh Fazly, and Allan D Jepson
    In European Conference on Computer Vision (ECCV) 2022
    arXiv Code
  5. CVPR’22
    Oral
    P3IV: Probabilistic Procedure Planning from Instructional Videos with Weak Supervision
    He Zhao, Isma Hadji, Nikita Dvornik, Konstantinos G Derpanis, Richard P Wildes, and Allan D Jepson
    In Conference on Computer Vision and Pattern Recognition (CVPR) 2022
    arXiv Code

2021

  1. NeurIPS’21
    Drop-DTW: Aligning Common Signal Between Sequences While Dropping Outliers
    Nikita Dvornik, Isma Hadji, Konstantinos G Derpanis, Animesh Garg, and Allan D Jepson
    In Advances in Neural Information Processing Systems (NeurIPS) 2021
    arXiv Code

2020

  1. ECCV’20
    Selecting relevant features from a multi-domain representation for few-shot classification
    Nikita Dvornik, Cordelia Schmid, and Julien Mairal
    In European Conference on Computer Vision (ECCV) 2020
    arXiv Code

2019

  1. ICCV’19
    Diversity with cooperation: Ensemble methods for few-shot classification
    Nikita Dvornik, Cordelia Schmid, and Julien Mairal
    In International Conference on Computer Vision (ICCV) 2019
    arXiv Code
  2. TPAMI’19
    On the importance of visual context for data augmentation in scene understanding
    Nikita Dvornik, Julien Mairal, and Cordelia Schmid
    IEEE transactions on Pattern Analysis and Machine Intelligence (PAMI) 2019
    arXiv Code

2018

  1. ECCV’18
    Modeling visual context is key to augmenting object detection datasets
    Nikita Dvornik, Julien Mairal, and Cordelia Schmid
    In European Conference on Computer Vision (ECCV) 2018
    arXiv Code

2017

  1. ICCV’17
    Blitznet: A real-time deep network for scene understanding
    Nikita Dvornik, Konstantin Shmelkov, Julien Mairal, and Cordelia Schmid
    In International Conference on Computer Vision (ICCV) 2017
    arXiv Code