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 5051–5075 of 10420 papers

TitleStatusHype
Evolutionary Neural Architecture Search for Image Restoration—0
CLOG-CD: Curriculum Learning based on Oscillating Granularity of Class Decomposed Medical Image Classification—0
Evolutionary Multi-objective Optimisation in Neurotrajectory Prediction—0
Evolutionary Cell Aided Design for Neural Network Architectures—0
EVM-Fusion: An Explainable Vision Mamba Architecture with Neural Algorithmic Fusion—0
Applying adversarial networks to increase the data efficiency and reliability of Self-Driving Cars—0
Adversarial Feature Alignment: Avoid Catastrophic Forgetting in Incremental Task Lifelong Learning—0
Evidential Federated Learning for Skin Lesion Image Classification—0
Evidence-empowered Transfer Learning for Alzheimer's Disease—0
CLIP-Q: Deep Network Compression Learning by In-Parallel Pruning-Quantization—0
Applications of the Streaming Networks—0
Event-Based Control for Online Training of Neural Networks—0
Evaluation, Tuning and Interpretation of Neural Networks for Meteorological Applications—0
Evaluation of Transfer Learning for Classification of: (1) Diabetic Retinopathy by Digital Fundus Photography and (2) Diabetic Macular Edema, Choroidal Neovascularization and Drusen by Optical Coherence Tomography—0
CLIP-QDA: An Explainable Concept Bottleneck Model—0
Applications of Sequential Learning for Medical Image Classification—0
Adversarial Examples Versus Cloud-based Detectors: A Black-box Empirical Study—0
A Comprehensive Survey on Hardware-Aware Neural Architecture Search—0
A 3-stage Spectral-spatial Method for Hyperspectral Image Classification—0
PreMix: Addressing Label Scarcity in Whole Slide Image Classification with Pre-trained Multiple Instance Learning Aggregators—0
Fidelity of Interpretability Methods and Perturbation Artifacts in Neural Networks—0
Applications and Effect Evaluation of Generative Adversarial Networks in Semi-Supervised Learning—0
Evaluation of Deep Learning on an Abstract Image Classification Dataset—0
Evaluation of Deep Convolutional Nets for Document Image Classification and Retrieval—0
CLIP meets Model Zoo Experts: Pseudo-Supervision for Visual Enhancement—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
5Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2—Unverified
6DaViT-HTop 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
10RevCol-HTop 1 Accuracy90—Unverified