SOTAVerified

Image Classification

Image Classification is a fundamental task in vision recognition that aims to understand and categorize an image as a whole under a specific label. Unlike object detection, which involves classification and location of multiple objects within an image, image classification typically pertains to single-object images. When the classification becomes highly detailed or reaches instance-level, it is often referred to as image retrieval, which also involves finding similar images in a large database.

Source: Metamorphic Testing for Object Detection Systems

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 376–400 of 10419 papers

TitleStatusHype
A Transformer-in-Transformer Network Utilizing Knowledge Distillation for Image Recognition—0
A Priori Generalizability Estimate for a CNN—0
Make LoRA Great Again: Boosting LoRA with Adaptive Singular Values and Mixture-of-Experts Optimization AlignmentCode2
Disentangling Visual Transformers: Patch-level Interpretability for Image Classification—0
AUKT: Adaptive Uncertainty-Guided Knowledge Transfer with Conformal Prediction—0
MOB-GCN: A Novel Multiscale Object-Based Graph Neural Network for Hyperspectral Image ClassificationCode0
A Multi-Scale Isolation Forest Approach for Real-Time Detection and Filtering of FGSM Adversarial Attacks in Video Streams of Autonomous Vehicles—0
TransMamba: Fast Universal Architecture Adaption from Transformers to Mamba—0
Directional Gradient Projection for Robust Fine-Tuning of Foundation Models—0
Steganographic Embeddings as an Effective Data AugmentationCode0
Quantum autoencoders for image classification—0
Reinforcement Learning for Ultrasound Image Analysis A Comprehensive Review of Advances and Applications—0
Stochastic Resonance Improves the Detection of Low Contrast Images in Deep Learning Models—0
Reliable Explainability of Deep Learning Spatial-Spectral Classifiers for Improved Semantic Segmentation in Autonomous Driving—0
Medical Image Classification with KAN-Integrated Transformers and Dilated Neighborhood AttentionCode2
MaxSup: Overcoming Representation Collapse in Label SmoothingCode1
Likelihood-Ratio Regularized Quantile Regression: Adapting Conformal Prediction to High-Dimensional Covariate Shifts—0
DAMamba: Vision State Space Model with Dynamic Adaptive ScanCode2
When Segmentation Meets Hyperspectral Image: New Paradigm for Hyperspectral Image ClassificationCode0
RingFormer: Rethinking Recurrent Transformer with Adaptive Level Signals—0
Benchmarking MedMNIST dataset on real quantum hardware—0
OCT Data is All You Need: How Vision Transformers with and without Pre-training Benefit Imaging—0
Leveraging Conditional Mutual Information to Improve Large Language Model Fine-Tuning For Classification—0
Simulations of Common Unsupervised Domain Adaptation Algorithms for Image ClassificationCode0
On Space Folds of ReLU Neural Networks—0
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All datasetsImageNetCIFAR-10CIFAR-100STL-10ObjectNetMNISTSVHNiNaturalist 2018ImageNet ReaLFlowers-102Clothing1Mmini WebVision 1.0

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CoCa (finetuned)Top 1 Accuracy91—Unverified
2Model soups (BASIC-L)Top 1 Accuracy90.98—Unverified
3Model soups (ViT-G/14)Top 1 Accuracy90.94—Unverified
4DaViT-GTop 1 Accuracy90.4—Unverified
5DaViT-HTop 1 Accuracy90.2—Unverified
6Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2—Unverified
7SwinV2-GTop 1 Accuracy90.17—Unverified
8MAWS (ViT-6.5B)Top 1 Accuracy90.1—Unverified
9Florence-CoSwin-HTop 1 Accuracy90.05—Unverified
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90—Unverified