FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation

Judy Hoffman, Dequan Wang, Fisher Yu, Trevor Darrell
arXiv 2016

FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation

Abstract

Fully convolutional models for dense prediction have proven successful for a wide range of visual tasks. Such models perform well in a supervised setting, but performance can be surprisingly poor under domain shifts that appear mild to a human observer. For example, training on one city and testing on another in a different geographic region and/or weather condition may result in significantly degraded performance due to pixel-level distribution shift. In this paper, we introduce the first domain adaptive semantic segmentation method, proposing an unsupervised adversarial approach to pixel prediction problems. Our method consists of both global and category specific adaptation techniques. Global domain alignment is performed using a novel semantic segmentation network with fully convolutional domain adversarial learning. This initially adapted space then enables category specific adaptation through a generalization of constrained weak learning, with explicit transfer of the spatial layout from the source to the target domains. Our approach outperforms baselines across different settings on multiple large-scale datasets, including adapting across various real city environments, different synthetic sub-domains, from simulated to real environments, and on a novel large-scale dash-cam dataset.

Paper

Citation

@misc{hoffman2016fcns,
      title={FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation}, 
      author={Judy Hoffman and Dequan Wang and Fisher Yu and Trevor Darrell},
      year={2016},
      eprint={1612.02649},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

Related


Prototypical Cross-Attention Networks for Multiple Object Tracking and Segmentation

Prototypical Cross-Attention Networks for Multiple Object Tracking and Segmentation

NeurIPS 2021 Spotlight We propose Prototypical Cross-Attention Network (PCAN), capable of leveraging rich spatio-temporal information for online multiple object tracking and segmentation.


Exploring Cross-Image Pixel Contrast for Semantic Segmentation

Exploring Cross-Image Pixel Contrast for Semantic Segmentation

ICCV 2021 Oral We propose a pixel-wise contrastive algorithm for semantic segmentation in the fully supervised setting.


Dense Prediction with Attentive Feature Aggregation

Dense Prediction with Attentive Feature Aggregation

arXiv 2021 We propose Attentive Feature Aggregation (AFA) to exploit both spatial and channel information for semantic segmentation and boundary detection.


BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning

BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning

CVPR 2020 Oral The largest driving video dataset for heterogeneous multitask learning.


Learning Saliency Propagation for Semi-Supervised Instance Segmentation

Learning Saliency Propagation for Semi-Supervised Instance Segmentation

CVPR 2020 We propose a ShapeProp module to propagate information between object detection and segmentation supervisions for Semi-Supervised Instance Segmentation.


Characterizing Adversarial Examples Based on Spatial Consistency Information for Semantic Segmentation

Characterizing Adversarial Examples Based on Spatial Consistency Information for Semantic Segmentation

ECCV 2018 We aim to characterize adversarial examples based on spatial context information in semantic segmentation.


Deep Layer Aggregation

Deep Layer Aggregation

CVPR 2018 Oral We augment standard architectures with deeper aggregation to better fuse information across layers.


Dilated Residual Networks

Dilated Residual Networks

CVPR 2017 We show that dilated residual networks (DRNs) outperform their non-dilated counterparts in image classification without increasing the model’s depth or complexity.


Multi-Scale Context Aggregation by Dilated Convolutions

Multi-Scale Context Aggregation by Dilated Convolutions

ICLR 2016 We study dilated convolution in depth. It has become a foundamental network operation.