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

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Showing 3451–3500 of 10419 papers

TitleStatusHype
SynerMix: Synergistic Mixup Solution for Enhanced Intra-Class Cohesion and Inter-Class Separability in Image ClassificationCode0
Improving Intervention Efficacy via Concept Realignment in Concept Bottleneck ModelsCode0
Deep Learning applied to NLPCode0
Accurate Dictionary Learning with Direct Sparsity ControlCode0
Deep Learning: An Introduction for Applied MathematiciansCode0
Improving Generalization and Convergence by Enhancing Implicit RegularizationCode0
Improving Generalization of Batch Whitening by Convolutional Unit OptimizationCode0
Improving robustness to corruptions with multiplicative weight perturbationsCode0
GPCA: A Probabilistic Framework for Gaussian Process Embedded Channel AttentionCode0
Orchid2024: A cultivar-level dataset and methodology for fine-grained classification of Chinese Cymbidium OrchidsCode0
A Comprehensive guide to Bayesian Convolutional Neural Network with Variational InferenceCode0
A Method of Moments Embedding Constraint and its Application to Semi-Supervised LearningCode0
Improving Ensemble Distillation With Weight Averaging and Diversifying PerturbationCode0
Attacking by Aligning: Clean-Label Backdoor Attacks on Object DetectionCode0
Improving Deep Neural Network Random Initialization Through Neuronal RewiringCode0
Adversarial Attack and Defense on Graph Data: A SurveyCode0
Improving Fairness in Image Classification via SketchingCode0
Improving Classification Neural Networks by using Absolute activation function (MNIST/LeNET-5 example)Code0
Characterizing Bias in Classifiers using Generative ModelsCode0
Improving Confident-Classifiers For Out-of-distribution DetectionCode0
Deep Layer AggregationCode0
Improving Calibration by Relating Focal Loss, Temperature Scaling, and PropernessCode0
Deep Intrinsic Decomposition with Adversarial Learning for Hyperspectral Image ClassificationCode0
Towards Difficulty-Agnostic Efficient Transfer Learning for Vision-Language ModelsCode0
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency MapsCode0
S4L: Self-Supervised Semi-Supervised LearningCode0
Deep Hybrid Architecture for Very Low-Resolution Image Classification Using Capsule AttentionCode0
BR-NPA: A Non-Parametric High-Resolution Attention Model to improve the Interpretability of AttentionCode0
Improving (α, f)-Byzantine Resilience in Federated Learning via layerwise aggregation and cosine distanceCode0
Improving Generalizability of Kolmogorov-Arnold Networks via Error-Correcting Output CodesCode0
Input Invex Neural NetworkCode0
DeepGraviLens: a Multi-Modal Architecture for Classifying Gravitational Lensing DataCode0
Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed TrainingCode0
eGAN: Unsupervised approach to class imbalance using transfer learningCode0
EG-Booster: Explanation-Guided Booster of ML Evasion AttacksCode0
Deep Gradient Compression Reduce the Communication Bandwidth For distributed TraningCode0
Avoiding The Double Descent Phenomenon of Random Feature Models Using Hybrid RegularizationCode0
Improved Gradient based Adversarial Attacks for Quantized NetworksCode0
Deep Generative Classifiers for Thoracic Disease Diagnosis with Chest X-ray ImagesCode0
Local Relation Networks for Image RecognitionCode0
DeepFool: a simple and accurate method to fool deep neural networksCode0
Improved efficient capsule network for Kuzushiji-MNIST benchmark dataset classificationCode0
Importance of Disjoint Sampling in Conventional and Transformer Models for Hyperspectral Image ClassificationCode0
A Vision-Language Foundation Model for Leaf Disease IdentificationCode0
Improved Activation Clipping for Universal Backdoor Mitigation and Test-Time DetectionCode0
Soft ascent-descent as a stable and flexible alternative to floodingCode0
A Mathematical Framework, a Taxonomy of Modeling Paradigms, and a Suite of Learning Techniques for Neural-Symbolic SystemsCode0
Elucidating Meta-Structures of Noisy Labels in Semantic Segmentation by Deep Neural NetworksCode0
Improved robustness of reinforcement learning policies upon conversion to spiking neuronal network platforms applied to ATARI gamesCode0
Deep Feature Response Discriminative CalibrationCode0
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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