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 1–10 of 1751 papers

TitleStatusHype
MoTM: Towards a Foundation Model for Time Series Imputation based on Continuous Modeling—0
GLAD: Generalizable Tuning for Vision-Language Models—0
Simulate, Refocus and Ensemble: An Attention-Refocusing Scheme for Domain GeneralizationCode0
InstructFLIP: Exploring Unified Vision-Language Model for Face Anti-spoofingCode1
From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion—0
Integrated Structural Prompt Learning for Vision-Language Models—0
Feed-Forward SceneDINO for Unsupervised Semantic Scene CompletionCode2
Prompt-Free Conditional Diffusion for Multi-object Image AugmentationCode1
Bridging Domain Generalization to Multimodal Domain Generalization via Unified Representations—0
Prompt Disentanglement via Language Guidance and Representation Alignment for Domain Generalization—0
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All datasetsPACSVizWiz-ClassificationImageNet-COffice-HomeImageNet-AImageNet-RDomainNetVLCSTerraIncognitaGTA-to-Avg(Cityscapes,BDD,Mapillary)ImageNet-SketchGTA5 to Cityscapes

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1tqdm (EVA02-CLIP-L)mIoU68.88—Unverified
2ADSImIoU67.75—Unverified
3ReinmIoU66.4—Unverified
4VLTSeg (EVA02-CLIP-L)mIoU65.6—Unverified
5DIFFmIoU58.01—Unverified
6CMFormermIoU55.31—Unverified
7Self-adaptation (ResNet - 101)mIoU46.99—Unverified
8GtA-SFDA Source-Only (DeepLabv2-ResNet101)mIoU43.5—Unverified