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 3476–3500 of 10420 papers

TitleStatusHype
Evolved Part Masking for Self-Supervised Learning—0
Rate Gradient Approximation Attack Threats Deep Spiking Neural NetworksCode1
DropKey for Vision Transformer—0
Boundary Unlearning: Rapid Forgetting of Deep Networks via Shifting the Decision Boundary—0
MDL-NAS: A Joint Multi-Domain Learning Framework for Vision Transformer—0
DISC: Learning From Noisy Labels via Dynamic Instance-Specific Selection and CorrectionCode1
iCLIP: Bridging Image Classification and Contrastive Language-Image Pre-Training for Visual RecognitionCode0
Quantum-Inspired Spectral-Spatial Pyramid Network for Hyperspectral Image Classification—0
ViewNet: A Novel Projection-Based Backbone With View Pooling for Few-Shot Point Cloud ClassificationCode1
AdaptiveMix: Improving GAN Training via Feature Space ShrinkageCode1
PEFAT: Boosting Semi-Supervised Medical Image Classification via Pseudo-Loss Estimation and Feature Adversarial TrainingCode0
Initialization Noise in Image Gradients and Saliency Maps—0
Co-Training 2L Submodels for Visual Recognition—0
ProD: Prompting-To-Disentangle Domain Knowledge for Cross-Domain Few-Shot Image Classification—0
A General Regret Bound of Preconditioned Gradient Method for DNN TrainingCode1
Efficient On-device Training via Gradient FilteringCode1
GoogLe2Net: Going Transverse with Convolutions—0
Neural Collapse in Deep Linear Networks: From Balanced to Imbalanced DataCode1
Chest X-Ray Images Classification with CNNCode0
TransIFC: Invariant Cues-aware Feature Concentration Learning for Efficient Fine-grained Bird Image Classification—0
DiRaC-I: Identifying Diverse and Rare Training Classes for Zero-Shot Learning—0
Machine Learning and Thermography Applied to the Detection and Classification of Cracks in Building—0
Learning Multimodal Data Augmentation in Feature SpaceCode1
On Learning the Structure of Clusters in Graphs—0
Thermal Heating in ReRAM Crossbar Arrays: Challenges and Solutions—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