SOTAVerified

Domain Generalization

The idea of Domain Generalization is to learn from one or multiple training domains, to extract a domain-agnostic model which can be applied to an unseen domain

Source: Diagram Image Retrieval using Sketch-Based Deep Learning and Transfer Learning

Papers

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Showing 101–150 of 1751 papers

TitleStatusHype
Towards Automated Cross-domain Exploratory Data Analysis through Large Language ModelsCode1
DATTA: Domain-Adversarial Test-Time Adaptation for Cross-Domain WiFi-Based Human Activity RecognitionCode1
Beyond Model Adaptation at Test Time: A SurveyCode1
Generalize or Detect? Towards Robust Semantic Segmentation Under Multiple Distribution ShiftsCode1
Point-PRC: A Prompt Learning Based Regulation Framework for Generalizable Point Cloud AnalysisCode1
START: A Generalized State Space Model with Saliency-Driven Token-Aware TransformationCode1
Adaptive High-Frequency Transformer for Diverse Wildlife Re-IdentificationCode1
QT-DoG: Quantization-aware Training for Domain GeneralizationCode1
Fine-Tuning CLIP's Last Visual Projector: A Few-Shot CornucopiaCode1
Parameter Competition Balancing for Model MergingCode1
Crafting Distribution Shifts for Validation and Training in Single Source Domain GeneralizationCode1
InterNet: Unsupervised Cross-modal Homography Estimation Based on Interleaved Modality Transfer and Self-supervised Homography PredictionCode1
PromptTA: Prompt-driven Text Adapter for Source-free Domain GeneralizationCode1
Prompting Segment Anything Model with Domain-Adaptive Prototype for Generalizable Medical Image SegmentationCode1
Do Vision Foundation Models Enhance Domain Generalization in Medical Image Segmentation?Code1
Structure-Aware Single-Source Generalization with Pixel-Level Disentanglement for Joint Optic Disc and Cup SegmentationCode1
Dual-stream Feature Augmentation for Domain GeneralizationCode1
Diffusion-Driven Data Replay: A Novel Approach to Combat Forgetting in Federated Class Continual LearningCode1
NuSegDG: Integration of Heterogeneous Space and Gaussian Kernel for Domain-Generalized Nuclei SegmentationCode1
Task-level Distributionally Robust Optimization for Large Language Model-based Dense RetrievalCode1
AMAES: Augmented Masked Autoencoder Pretraining on Public Brain MRI Data for 3D-Native SegmentationCode1
The Devil is in the Statistics: Mitigating and Exploiting Statistics Difference for Generalizable Semi-supervised Medical Image SegmentationCode1
Textual Query-Driven Mask Transformer for Domain Generalized SegmentationCode1
Disentangling Masked Autoencoders for Unsupervised Domain GeneralizationCode1
FDS: Feedback-guided Domain Synthesis with Multi-Source Conditional Diffusion Models for Domain GeneralizationCode1
Towards Multimodal Open-Set Domain Generalization and Adaptation through Self-supervisionCode1
ASPS: Augmented Segment Anything Model for Polyp SegmentationCode1
MedMNIST-C: Comprehensive benchmark and improved classifier robustness by simulating realistic image corruptionsCode1
Beyond Boundaries: Learning a Universal Entity Taxonomy across Datasets and Languages for Open Named Entity RecognitionCode1
Enhancing Fake News Detection in Social Media via Label Propagation on Cross-modal Tweet GraphCode1
Diffusion Features to Bridge Domain Gap for Semantic SegmentationCode1
Towards Generalizable Multi-Object TrackingCode1
Federated Unsupervised Domain Generalization using Global and Local Alignment of GradientsCode1
Improving Single Domain-Generalized Object Detection: A Focus on Diversification and AlignmentCode1
RobustMVS: Single Domain Generalized Deep Multi-view StereoCode1
Cross-Domain Feature Augmentation for Domain GeneralizationCode1
Adapting to Distribution Shift by Visual Domain Prompt GenerationCode1
RaffeSDG: Random Frequency Filtering enabled Single-source Domain Generalization for Medical Image SegmentationCode1
Soft Prompt Generation for Domain GeneralizationCode1
Parameter Efficient Fine-tuning of Self-supervised ViTs without Catastrophic ForgettingCode1
AAPL: Adding Attributes to Prompt Learning for Vision-Language ModelsCode1
DGMamba: Domain Generalization via Generalized State Space ModelCode1
Prompt Learning via Meta-RegularizationCode1
Language Guided Domain Generalized Medical Image SegmentationCode1
Unknown Prompt, the only Lacuna: Unveiling CLIP's Potential for Open Domain GeneralizationCode1
DPStyler: Dynamic PromptStyler for Source-Free Domain GeneralizationCode1
MatchSeg: Towards Better Segmentation via Reference Image MatchingCode1
DomainLab: A modular Python package for domain generalization in deep learningCode1
M-HOF-Opt: Multi-Objective Hierarchical Output Feedback Optimization via Multiplier Induced Loss Landscape SchedulingCode1
Enhancing Vision-Language Few-Shot Adaptation with Negative LearningCode1
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All datasetsPACSVizWiz-ClassificationImageNet-COffice-HomeImageNet-AImageNet-RDomainNetVLCSTerraIncognitaGTA-to-Avg(Cityscapes,BDD,Mapillary)ImageNet-SketchGTA5 to Cityscapes

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SIMPLE+Average Accuracy99—Unverified
2PromptStyler (CLIP, ViT-L/14)Average Accuracy98.6—Unverified
3GMDG (RegNetY-16GF, SWAD)Average Accuracy97.9—Unverified
4D-Triplet(RegNetY-16GF)Average Accuracy97.6—Unverified
5MoA (OpenCLIP, ViT-B/16)Average Accuracy97.4—Unverified
6GMDG (e RegNetY-16GF)Average Accuracy97.3—Unverified
7PromptStyler (CLIP, ViT-B/16)Average Accuracy97.2—Unverified
8SPG (CLIP, ViT-B/16)Average Accuracy97—Unverified
9CAR-FT (CLIP, ViT-B/16)Average Accuracy96.8—Unverified
10MIRO (RegNetY-16GF, SWAD)Average Accuracy96.8—Unverified
#ModelMetricClaimedVerifiedStatus
1ViT-8/B-224Accuracy - Clean Images450—Unverified
2VOLO-D5Accuracy - All Images57.2—Unverified
3ConvNeXt-BAccuracy - All Images53.5—Unverified
4ResNeXt-101 32x16dAccuracy - All Images51.7—Unverified
5EfficientNet-B8 (advprop+autoaug)Accuracy - All Images50.5—Unverified
6EfficientNet-B7 (advprop+autoaug)Accuracy - All Images49.7—Unverified
7EfficientNet-B6 (advprop+autoaug)Accuracy - All Images49.6—Unverified
8EfficientNet-B5 (advprop+autoaug)Accuracy - All Images49.1—Unverified
9ViT-16/L-224Accuracy - All Images49—Unverified
10ResNet-50 (gn)Accuracy - All Images48.9—Unverified