Improving Optical Flow on a Pyramid Level

European Conf. on Computer Vision (ECCV) 2020 / August, 2020
By Markus Hofinger, Samuel Rota Bulò, Lorenzo Porzi, Arno Knapitsch, Thomas Pock, Peter Kontschieder

Abstract

In this work we review the coarse-to-fine spatial feature pyramid concept, which is used in state-of-the-art optical flow estimation networks to make exploration of the pixel flow search space computationally tractable and efficient. Within an individual pyramid level, we improve the cost volume construction process by departing from a warping- to a sampling-based strategy, which avoids ghosting and hence enables us to better preserve fine flow details. We further amplify the positive effects through a level-specific, loss max-pooling strategy that adaptively shifts the focus of the learning process on underperforming predictions. Our second contribution revises the gradient flow across pyramid levels. The typical operations performed at each pyramid level can lead to noisy, or even contradicting gradients across levels. We show and discuss how properly blocking some of these gradient components leads to improved convergence and ultimately better performance. Finally, we introduce a distillation concept to counteract the issue of catastrophic forgetting during finetuning and thus preserving knowledge over models sequentially trained on multiple datasets. Our findings are conceptually simple and easy to implement, yet result in compelling improvements on relevant error measures that we demonstrate via exhaustive ablations on datasets like Flying Chairs2, Flying Things, Sintel and KITTI. We establish new state-of-the-art results on the challenging Sintel and KITTI 2012 test datasets, and even show the portability of our findings to different optical flow and depth from stereo approaches.

Publications

CrowdDriven: A New Challenging Dataset for Outdoor Visual Localization

By Ara Jafarzadeh, Manuel López Antequera, Pau Gargallo, Yubin Kuang, Carl Toft, Fredrik Kahl, Torsten Sattler
Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021

Improving Panoptic Segmentation at All Scales

By Lorenzo Porzi, Samuel Rota Bulò, Peter Kontschieder
Conf. on Computer Vision and Pattern Recognition (CVPR) 2021

Mapillary Planet-Scale Depth Dataset

By Manuel López-Antequera, Pau Gargallo, Markus Hofinger, Samuel Rota Bulò, Yubin Kuang, Peter Kontschieder
European Conf. on Computer Vision (ECCV) 2020

Improving Optical Flow on a Pyramid Level

By Markus Hofinger, Samuel Rota Bulò, Lorenzo Porzi, Arno Knapitsch, Thomas Pock, Peter Kontschieder
European Conf. on Computer Vision (ECCV) 2020

Towards Generalization Across Depth for Monocular 3D Object Detection

By Andrea Simonelli, Samuel Rota Bulò, Lorenzo Porzi, Elisa Ricci, Peter Kontschieder
European Conf. on Computer Vision (ECCV) 2020

The Mapillary Traffic Sign Dataset for Detection and Classification on a Global Scale

By Christian Ertler, Jerneja Mislej, Tobias Ollmann, Lorenzo Porzi, Gerhard Neuhold, Yubin Kuang
European Conf. on Computer Vision (ECCV) 2020

Modeling the Background for Incremental Learning in Semantic Segmentation

By Fabio Cermelli, Massimiliano Mancini, Samuel Rota Bulò, Elisa Ricci, Barbara Caputo
Conf. on Computer Vision and Pattern Recognition (CVPR) 2020

Mapillary Street-Level Sequences: A Dataset for Lifelong Place Recognition

By Frederik Warburg, Soren Hauberg, Manuel López-Antequera, Pau Gargallo, Yubin Kuang, Javier Civera
Conf. on Computer Vision and Pattern Recognition (CVPR) 2020

Learning Multi-Object Tracking and Segmentation from Automatic Annotations

By Lorenzo Porzi, Markus Hofinger, Idoia Ruiz, Joan Serrat, Samuel Rota Bulò, Peter Kontschieder
International Conf. on Computer Vision (ICCV) 2019

Disentangling Monocular 3D Object Detection

By Andrea Simonelli, Samuel Rota Bulò, Lorenzo Porzi, Manuel López-Antequera, Peter Kontschieder
Conf. on Computer Vision and Pattern Recognition (CVPR) 2019

Seamless Scene Segmentation

By Lorenzo Porzi, Samuel Rota Bulò, Aleksander Colovic, Peter Kontschieder
Conf. on Computer Vision and Pattern Recognition (CVPR) 2019

AdaGraph: Unifying Predictive and Continuous Domain Adaptation through Graphs

By Massimiliano Mancini, Samuel Rota Bulò, Barbara Caputo, Elisa Ricci
Conf. on Computer Vision and Pattern Recognition (CVPR) 2019

Unsupervised Domain Adaptation using Feature-Whitening and Consensus Loss

By Subhankar Roy, Aliaksandr Siarohin, Enver Sangineto, Samuel Rota Bulò, Nicu Sebe, Elisa Ricci
Conf. on Computer Vision and Pattern Recognition (CVPR) 2019

Deep Single Image Camera Calibration with Radial Distortion

By Manuel López-Antequera, Roger Marı́, Pau Gargallo, Yubin Kuang, Javier Gonzalez-Jimenez, Gloria Haro
Conf. on Computer Vision and Pattern Recognition (CVPR) 2019

In-Place Activated BatchNorm for Memory-Optimized Training of DNNs

By Samuel Rota Bulò, Lorenzo Porzi, Peter Kontschieder
Conf. on Computer Vision and Pattern Recognition (CVPR) 2018

Boosting Domain Adaptation by Discovering Latent Domains

By Massimilano Mancini, Lorenzo Porzi, Samuel Rota Bulò, Barbara Caputo, Elisa Ricci
Conf. on Computer Vision and Pattern Recognition (CVPR) 2018

Geometry-Aware Network for Non-Rigid Shape Prediction from a Single View

By Albert Pumarola, Antonio Agudo, Lorenzo Porzi, Alberto Sanfeliu, Vincent Lepetit, Francesc Moreno-Noguer
Conf. on Computer Vision and Pattern Recognition (CVPR) 2018

The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes

By Gerhard Neuhold, Tobias Ollmann, Samuel Rota Bulò, Peter Kontschieder
International Conf. on Computer Vision (ICCV) 2017

AutoDIAL: Automatic DomaIn Alignment Layers

By Fabio Maria Carlucci, Lorenzo Porzi, Barbara Caputo, Elisa Ricci, Samuel Rota Bulò
International Conf. on Computer Vision (ICCV) 2017

Loss Max-Pooling for Semantic Image Segmentation

By Samuel Rota Bulò, Gerhard Neuhold, Peter Kontschieder
Conf. on Computer Vision and Pattern Recognition (CVPR) 2017

Online Learning with Bayesian Classification Trees

By Samuel Rota Bulò, Peter Kontschieder
Conf. on Computer Vision and Pattern Recognition (CVPR) 2016

Dropout Distillation

By Samuel Rota Bulò, Lorenzo Porzi, Peter Kontschieder
Intern. Conf. on Machine Learning (ICML) 2016