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 2650 of 1751 papers

TitleStatusHype
GLOBEM Dataset: Multi-Year Datasets for Longitudinal Human Behavior Modeling GeneralizationCode2
Generative Medical SegmentationCode2
Learning to Prompt for Vision-Language ModelsCode2
HPT++: Hierarchically Prompting Vision-Language Models with Multi-Granularity Knowledge Generation and Improved Structure ModelingCode2
Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference timeCode2
Enhance Then Search: An Augmentation-Search Strategy with Foundation Models for Cross-Domain Few-Shot Object DetectionCode2
eCeLLM: Generalizing Large Language Models for E-commerce from Large-scale, High-quality Instruction DataCode2
FAMNet: Frequency-aware Matching Network for Cross-domain Few-shot Medical Image SegmentationCode2
DiffusionFake: Enhancing Generalization in Deepfake Detection via Guided Stable DiffusionCode2
Diff9D: Diffusion-Based Domain-Generalized Category-Level 9-DoF Object Pose EstimationCode2
Domain Adaptive and Generalizable Network Architectures and Training Strategies for Semantic Image SegmentationCode2
Feed-Forward SceneDINO for Unsupervised Semantic Scene CompletionCode2
DatasetDM: Synthesizing Data with Perception Annotations Using Diffusion ModelsCode2
Depth Field Networks for Generalizable Multi-view Scene RepresentationCode2
Description and Discussion on DCASE 2024 Challenge Task 2: First-Shot Unsupervised Anomalous Sound Detection for Machine Condition MonitoringCode2
CrossEarth: Geospatial Vision Foundation Model for Domain Generalizable Remote Sensing Semantic SegmentationCode2
DeepPerception: Advancing R1-like Cognitive Visual Perception in MLLMs for Knowledge-Intensive Visual GroundingCode2
Earth-Adapter: Bridge the Geospatial Domain Gaps with Mixture of Frequency AdaptationCode2
EasyPortrait -- Face Parsing and Portrait Segmentation DatasetCode2
Continuous Temporal Domain GeneralizationCode2
Building Computationally Efficient and Well-Generalizing Person Re-Identification Models with Metric LearningCode2
Benchmarking Neural Network Robustness to Common Corruptions and PerturbationsCode2
Adversarial Supervision Makes Layout-to-Image Diffusion Models ThriveCode2
CLIP-Powered Domain Generalization and Domain Adaptation: A Comprehensive SurveyCode2
Avoiding Shortcuts: Enhancing Channel-Robust Specific Emitter Identification via Single-Source Domain GeneralizationCode2
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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