CVPR 2022
Domain Adaptation

01 Background

Annotations are particularly
costly as every pixel has to belabeled

01 What is UDA?
UDA: Unsupervised Domain Adaptation

Image:

test

Label:

Domain Adaptation
Undoing the Damage of Label Shift for Cross-domain Semantic Segmentation

01 Undoing the Damage of Label Shift for Cross-domain Semantic Segmentation• CVPR 2022
• Motivation
Ignore the label shift problem, which commonly exists in CDSS tasks, since the label distribution is often different acrossdomains.

01

Undoing the Damage of Label Shift for Cross-domain Semantic Segmentation• Contribution••

propose to address the label shift issue for CDSS tasks in a more realistic scenario (i.e., the conditional
distributions are different across domains) and reveal that the classifier bias is the critical factor leading topoor generalization on the target domain
propose two simple yet effective methods to rectify the classifier bias from source to target by remolding
the classifier predictions after explicitly aligning the conditional distribution

01

Undoing the Damage of Label Shift for Cross-domain Semantic Segmentation• Motivation• 𝑮=𝑪∘𝑭•𝑮 𝒙 =𝑪 𝑭 𝒙= 𝒑(𝒀|𝑭 𝒙 )• Bayes’ theorem :𝑪 𝑭 𝒙=𝑷 𝒀𝑭 𝒙• 𝑪' (𝑭 𝒙 ) ∝ 𝑷𝒔 (𝑭𝑺(𝒙)|𝒀)𝑷𝒔 (𝒀)• 𝑪𝒕 (𝑭 𝒙 ) ∝ 𝑷𝒕 (𝑭𝒕 (𝒙)|𝒀)𝑷𝒕 (𝒀)𝑪𝒔 (𝑭(𝒙))𝑷𝒕 (𝒀)• 𝑪𝒕 (𝑭 𝒙 ) ∝𝑷𝒔 (𝒀)=𝑷 𝑭 𝒙𝒀 𝑷(𝒀)𝑷(𝑭 𝒙 )

01

Undoing the Damage of Label Shift for Cross-domain Semantic Segmentation• Conditional Distribution Alignment

01

Undoing the Damage of Label Shift for Cross-domain Semantic Segmentation• Classifier Rectification(CR)01

Undoing the Damage of Label Shift for Cross-domain Semantic Segmentation• Inference Adjustment (IA)

01

Undoing the Damage of Label Shift for Cross-domain Semantic Segmentation• Label Distribution Estimation• we denote the count of the image-level label of i-th source image as𝑰𝒊𝒔(𝒌)01 Undoing the Damage of Label Shift for Cross-domain Semantic Segmentation• Experimental Results• GTA5:

01 Undoing the Damage of Label Shift for Cross-domain Semantic Segmentation• Experimental Results• SYNTHIA :01 Undoing the Damage of Label Shift for Cross-domain Semantic Segmentation• Experimental Results

Domain Adaptation
Class-Balanced Pixel-Level Self-Labeling for
Domain Adaptive Semantic Segmentation

01Class-Balanced Pixel-Level Self-Labeling for Domain Adaptive Semantic
Segmentation• CVPR 2022
• Motivation
pixel-wise cluster assignments could reveal the intrinsic distributions of pixels in target domain, and provide usefulsupervision for model training

01Class-Balanced Pixel-Level Self-Labeling for Domain Adaptive Semantic
Segmentation• Contribution••

A pixel-level self-labeling module is developed for domain adaptive semantic segmentation. We cluster
pixels in an online fashion and simultaneously rectify pseudo labels based on the resulting cluster
assignments.
propose two simple yet effective methods to rectify the classifier bias from source to target by remolding
the classifier predictions after explicitly aligning the conditional distribution

01Class-Balanced Pixel-Level Self-Labeling for Domain Adaptive Semantic
Segmentation• Overview of the Proposed Model01Class-Balanced Pixel-Level Self-Labeling for Domain Adaptive Semantic
Segmentation• Online Pixel-Level Self-Labeling

Weight Initialization

01Class-Balanced Pixel-Level Self-Labeling for Domain Adaptive Semantic
Segmentation• Class-Balanced Sampling.

Distribution Alignment: simultaneouslyoptimizing Q and 𝑃# in Eq. 5 may lead todegenerated results that all data points are
trivially assigned to a single cluster.

01Segmentation

Class-Balanced Pixel-Level Self-Labeling for Domain Adaptive Semantic• Experimental Results• GTA5:

01Segmentation

Class-Balanced Pixel-Level Self-Labeling for Domain Adaptive Semantic• Experimental Results• SYNTHIA:

01Segmentation

Class-Balanced Pixel-Level Self-Labeling for Domain Adaptive Semantic• Experimental Results

Domain Adaptation
Towards Fewer Annotations: Active Learning via Region Impurity andPrediction Uncertainty for Domain Adaptive Semantic Segmentation

01Prediction Uncertainty for Domain Adaptive Semantic Segmentation

Towards Fewer Annotations: Active Learning via Region Impurity and• CVPR 2022
• Motivation

01Towards Fewer Annotations: Active Learning via Region Impurity andPrediction Uncertainty for Domain Adaptive Semantic Segmentation• Contribution••benchmark the performance of prior methods for active domain adaptation regarding semantic
segmentation and uncover that methods using image-based or point-based selection strategies are noteffective
propose a region-based acquisition strategy for domain adaptive semantic segmentation, termed RIPU,
that utilizes region impurity and prediction uncertainty to identify image regions that are both diverse inspatial adjacency and uncertain in prediction output

01 Active Learning via Region Impurity and Prediction Uncertainty• Overview of the Proposed Model• Region Generation: k-square-neighbors• Region Impurity:• Prediction Uncertainty:

01 Active Learning via Region Impurity and Prediction Uncertainty• Overview of the Proposed Model

01 Active Learning via Region Impurity and Prediction Uncertainty• Experimental Results：GTAV----Cityscapes01 Active Learning via Region Impurity and Prediction Uncertainty• Experimental Results：SYNTHIA----Cityscapes01 Active Learning via Region Impurity and Prediction Uncertainty• Experimental Result

Domain Adaptation
HRDA: Context-Aware High-Resolution
Domain-Adaptive Semantic Segmentation

01 DAformer• Previous Work：DAformer

01 HRDA: Context-Aware High-Resolution Domain-Adaptive SemanticSegmentation• arXiv• Motivation
UDA methods are usually more GPU memory intensive than regular supervised training as images from multiple
domains, additional networks (e.g teacher model or domain discriminator), and additional losses are required for
UDA training
predictions from low-resolution (LR) inputs often fail to recognize small objects such as distant traffic lights and topreserve fine segmentation details such as limbs of distant pedestrians

01 HRDA: Context-Aware High-Resolution Domain-Adaptive SemanticSegmentation• Contribution••••

studying the influence of resolution and crop size
exploiting HR inputs for adapting small objects and fine segmentation details
applying multi-resolution training with a learned scale attention for object-scale-dependent adaptation
proposing a nested context and detail crop for memory-efficient training

01 HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic
Segmentation• Overview of the Proposed Model

01 HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic
Segmentation• Context and Detail CropLR Context:
HR Detail:01 HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic
Segmentation• Multi-Resolution Fusion

scale attention
𝑎! = 𝜎(𝑓 " 𝑓 # 𝑥! )attention-weighted sum:

01 HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic
Segmentation• Multi-Resolution Fusion

Source:Target:01 HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic
Segmentation• Pseudo-Label Generation with Overlapping Sliding Window&The underlying HRDA prediction𝑦,!,%is fused from the LR prediction 𝑦,!& and HR&prediction 𝑦,!,'(using the full scale attention𝑎!&01 HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic
Segmentation• Experimental Results• GTA5:

01 ProCST: Boosting Semantic Segmentation using Progressive Cyclic StyleTransfer• Experimental Results• SYNTHIA :01 ProCST: Boosting Semantic Segmentation using Progressive Cyclic StyleTransfer• Experimental Results

01 ProCST: Boosting Semantic Segmentation using Progressive Cyclic StyleTransfer• Experimental Results

01 ProCST: Boosting Semantic Segmentation using Progressive Cyclic StyleTransfer• Experimental Results