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 5351–5375 of 10420 papers

TitleStatusHype
Hermitry Ratio: Evaluating the validity of perturbation methods for explainable deep learning—0
GUIDED MCMC FOR SPARSE BAYESIAN MODELS TO DETECT RARE EVENTS IN IMAGES SANS LABELED DATA—0
Fundamental Limits of Transfer Learning in Binary Classifications—0
Function-Space Variational Inference for Deep Bayesian Classification—0
Task Conditioned Stochastic Subsampling—0
Assessing two novel distance-based loss functions for few-shot image classification—0
SVMnet: Non-parametric image classification based on convolutional SVM ensembles for small training sets—0
FLOAT: FAST LEARNABLE ONCE-FOR-ALL ADVERSARIAL TRAINING FOR TUNABLE TRADE-OFF BETWEEN ACCURACY AND ROBUSTNESS—0
Learning to Schedule Learning rate with Graph Neural Networks—0
Compositional Training for End-to-End Deep AUC Maximization—0
FedBABU: Toward Enhanced Representation for Federated Image Classification—0
Feature Kernel Distillation—0
Neural Extensions: Training Neural Networks with Set Functions—0
Pseudo Knowledge Distillation: Towards Learning Optimal Instance-specific Label Smoothing Regularization—0
Noise-Contrastive Variational Information Bottleneck Networks—0
Sequence Approximation using Feedforward Spiking Neural Network for Spatiotemporal Learning: Theory and Optimization Methods—0
Towards Efficient On-Chip Training of Quantum Neural Networks—0
DM-CT: Consistency Training with Data and Model Perturbation—0
Diurnal or Nocturnal? Federated Learning of Multi-branch Networks from Periodically Shifting Distributions—0
Tessellated 2D Convolution Networks: A Robust Defence against Adversarial Attacks—0
When in Doubt, Summon the Titans: A Framework for Efficient Inference with Large Models—0
On the Convergence of Nonconvex Continual Learning with Adaptive Learning Rate—0
To Smooth or not to Smooth? On Compatibility between Label Smoothing and Knowledge Distillation—0
Measuring the Effectiveness of Self-Supervised Learning using Calibrated Learning Curves—0
Towards Generic Interface for Human-Neural Network Knowledge Exchange—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