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

Showing 101125 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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SIMPLE+Average Accuracy99Unverified
2PromptStyler (CLIP, ViT-L/14)Average Accuracy98.6Unverified
3GMDG (RegNetY-16GF, SWAD)Average Accuracy97.9Unverified
4D-Triplet(RegNetY-16GF)Average Accuracy97.6Unverified
5MoA (OpenCLIP, ViT-B/16)Average Accuracy97.4Unverified
6GMDG (e RegNetY-16GF)Average Accuracy97.3Unverified
7PromptStyler (CLIP, ViT-B/16)Average Accuracy97.2Unverified
8SPG (CLIP, ViT-B/16)Average Accuracy97Unverified
9CAR-FT (CLIP, ViT-B/16)Average Accuracy96.8Unverified
10MIRO (RegNetY-16GF, SWAD)Average Accuracy96.8Unverified
#ModelMetricClaimedVerifiedStatus
1ViT-8/B-224Accuracy - Clean Images450Unverified
2VOLO-D5Accuracy - All Images57.2Unverified
3ConvNeXt-BAccuracy - All Images53.5Unverified
4ResNeXt-101 32x16dAccuracy - All Images51.7Unverified
5EfficientNet-B8 (advprop+autoaug)Accuracy - All Images50.5Unverified
6EfficientNet-B7 (advprop+autoaug)Accuracy - All Images49.7Unverified
7EfficientNet-B6 (advprop+autoaug)Accuracy - All Images49.6Unverified
8EfficientNet-B5 (advprop+autoaug)Accuracy - All Images49.1Unverified
9ViT-16/L-224Accuracy - All Images49Unverified
10ResNet-50 (gn)Accuracy - All Images48.9Unverified