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

Showing 25512600 of 10419 papers

TitleStatusHype
Intelligent Multi-View Test Time AugmentationCode0
Class2Str: End to End Latent Hierarchy LearningCode0
CLAD: A Contrastive Learning based Approach for Background DebiasingCode0
Instance-based Label Smoothing For Better Calibrated Classification NetworksCode0
Instance Enhancement Batch Normalization: an Adaptive Regulator of Batch NoiseCode0
CKD: Contrastive Knowledge Distillation from A Sample-wise PerspectiveCode0
Input-gradient space particle inference for neural network ensemblesCode0
Circuit Design and Efficient Simulation of Quantum Inner Product and Empirical Studies of Its Effect on Near-Term Hybrid Quantum-Classic Machine LearningCode0
CINIC-10 is not ImageNet or CIFAR-10Code0
IBCL: Zero-shot Model Generation for Task Trade-offs in Continual LearningCode0
Input Invex Neural NetworkCode0
An Intelligent Remote Sensing Image Quality Inspection SystemCode0
A Group-Theoretic Framework for Data AugmentationCode0
CIFAR-10 Image Classification Using Feature EnsemblesCode0
Inference via Sparse Coding in a Hierarchical Vision ModelCode0
A Comprehensive Overhaul of Feature DistillationCode0
Influence of Image Classification Accuracy on Saliency Map EstimationCode0
Information Competing Process for Learning Diversified RepresentationsCode0
Volley Revolver: A Novel Matrix-Encoding Method for Privacy-Preserving Neural Networks (Inference)Code0
ChronoNet: A Deep Recurrent Neural Network for Abnormal EEG IdentificationCode0
InDL: A New Dataset and Benchmark for In-Diagram Logic Interpretation based on Visual IllusionCode0
Chordal Sparsity for Lipschitz Constant Estimation of Deep Neural NetworksCode0
Adversarial Attacks on Data AttributionCode0
In-domain representation learning for remote sensingCode0
CHIP: Channel-wise Disentangled Interpretation of Deep Convolutional Neural NetworksCode0
Revisiting 16-bit Neural Network Training: A Practical Approach for Resource-Limited LearningCode0
Adversarial Attacks on Black Box Video Classifiers: Leveraging the Power of Geometric TransformationsCode0
Understanding Intrinsic Robustness Using Label UncertaintyCode0
Increasing-Margin Adversarial (IMA) Training to Improve Adversarial Robustness of Neural NetworksCode0
In-distribution Public Data Synthesis with Diffusion Models for Differentially Private Image ClassificationCode0
Initialization Matters for Adversarial Transfer LearningCode0
Chest X-Ray Images Classification with CNNCode0
A Novel Explainable Out-of-Distribution Detection Approach for Spiking Neural NetworksCode0
ChessMix: Spatial Context Data Augmentation for Remote Sensing Semantic SegmentationCode0
Inception-inspired LSTM for Next-frame Video PredictionCode0
Chemception: A Deep Neural Network with Minimal Chemistry Knowledge Matches the Performance of Expert-developed QSAR/QSPR ModelsCode0
Improvising the Learning of Neural Networks on Hyperspherical ManifoldCode0
CHEF: A Cheap and Fast Pipeline for Iteratively Cleaning Label Uncertainties (Technical Report)Code0
Characterizing Bias in Classifiers using Generative ModelsCode0
Adversarial Attack and Defense on Graph Data: A SurveyCode0
Improving Transferability of Adversarial Examples with Input DiversityCode0
A Comprehensive guide to Bayesian Convolutional Neural Network with Variational InferenceCode0
GPCA: A Probabilistic Framework for Gaussian Process Embedded Channel AttentionCode0
Chair Segments: A Compact Benchmark for the Study of Object SegmentationCode0
Improving the repeatability of deep learning models with Monte Carlo dropoutCode0
Improving the trustworthiness of image classification models by utilizing bounding-box annotationsCode0
In-Place Activated BatchNorm for Memory-Optimized Training of DNNsCode0
Invariant backpropagation: how to train a transformation-invariant neural networkCode0
CGRclust: Chaos Game Representation for Twin Contrastive Clustering of Unlabelled DNA SequencesCode0
Cervical Optical Coherence Tomography Image Classification Based on Contrastive Self-Supervised Texture LearningCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CoCa (finetuned)Top 1 Accuracy91Unverified
2Model soups (BASIC-L)Top 1 Accuracy90.98Unverified
3Model soups (ViT-G/14)Top 1 Accuracy90.94Unverified
4DaViT-GTop 1 Accuracy90.4Unverified
5Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2Unverified
6DaViT-HTop 1 Accuracy90.2Unverified
7SwinV2-GTop 1 Accuracy90.17Unverified
8MAWS (ViT-6.5B)Top 1 Accuracy90.1Unverified
9Florence-CoSwin-HTop 1 Accuracy90.05Unverified
10RevCol-HTop 1 Accuracy90Unverified