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 14011425 of 1751 papers

TitleStatusHype
When and How Does CLIP Enable Domain and Compositional Generalization?0
When Domain Generalization meets Generalized Category Discovery: An Adaptive Task-Arithmetic Driven Approach0
When is Task Vector Provably Effective for Model Editing? A Generalization Analysis of Nonlinear Transformers0
Why Domain Generalization Fail? A View of Necessity and Sufficiency0
WIDIn: Wording Image for Domain-Invariant Representation in Single-Source Domain Generalization0
WQT and DG-YOLO: towards domain generalization in underwater object detection0
Zero-shot 3D Segmentation of Abdominal Organs in CT Scans Using Segment Anything Model 2: Adapting Video Tracking Capabilities for 3D Medical Imaging0
Zero-shot Domain Generalization of Foundational Models for 3D Medical Image Segmentation: An Experimental Study0
Zero-Shot Estimation of Base Models' Weights in Ensemble of Machine Reading Comprehension Systems for Robust Generalization0
MedVLM-R1: Incentivizing Medical Reasoning Capability of Vision-Language Models (VLMs) via Reinforcement Learning0
MegaCOIN: Enhancing Medium-Grained Color Perception for Vision-Language Models0
Medical Image Segmentation via Single-Source Domain Generalization with Random Amplitude Spectrum SynthesisCode0
Color-Quality Invariance for Robust Medical Image SegmentationCode0
When Vision Transformers Outperform ResNets without Pre-training or Strong Data AugmentationsCode0
Understanding Domain Generalization: A Noise Robustness PerspectiveCode0
PLACE dropout: A Progressive Layer-wise and Channel-wise Dropout for Domain GeneralizationCode0
Magnification Generalization for Histopathology Image EmbeddingCode0
Robust White Matter Hyperintensity Segmentation on Unseen DomainCode0
Generalizing to unseen domains via distribution matchingCode0
Understanding the Limits of Unsupervised Domain Adaptation via Data PoisoningCode0
Adversarial Style Augmentation for Domain Generalized Urban-Scene SegmentationCode0
Meta Distribution Alignment for Generalizable Person Re-IdentificationCode0
LFME: A Simple Framework for Learning from Multiple Experts in Domain GeneralizationCode0
Rotation-Constrained Cross-View Feature Fusion for Multi-View Appearance-based Gaze EstimationCode0
Leveraging Vision-Language Models for Visual Grounding and Analysis of Automotive UICode0
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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