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 2851–2900 of 10419 papers

TitleStatusHype
FairDD: Fair Dataset Distillation via Synchronized Matching—0
Sparse Attention Vectors: Generative Multimodal Model Features Are Discriminative Vision-Language Classifiers—0
ANDHRA Bandersnatch: Training Neural Networks to Predict Parallel RealitiesCode0
MVFormer: Diversifying Feature Normalization and Token Mixing for Efficient Vision Transformers—0
Controlling Participation in Federated Learning with FeedbackCode0
Leveraging Semi-Supervised Learning to Enhance Data Mining for Image Classification under Limited Labeled Data—0
Pruning Deep Convolutional Neural Network Using Conditional Mutual Information—0
KANs for Computer Vision: An Experimental Study—0
Mixture of Experts in Image Classification: What's the Sweet Spot?—0
Optimized Tradeoffs for Private Prediction with Majority Ensembling—0
Fall Leaf Adversarial Attack on Traffic Sign Classification—0
CoA: Chain-of-Action for Generative Semantic LabelsCode0
An In-depth Investigation of Sparse Rate Reduction in Transformer-like Models—0
SpikeAtConv: An Integrated Spiking-Convolutional Attention Architecture for Energy-Efficient Neuromorphic Vision Processing—0
BadScan: An Architectural Backdoor Attack on Visual State Space Models—0
Debiasing Classifiers by Amplifying Bias with Latent Diffusion and Large Language Models—0
Creating Scalable AGI: the Open General Intelligence Framework—0
Twin Trigger Generative Networks for Backdoor Attacks against Object Detection—0
MUNBa: Machine Unlearning via Nash BargainingCode0
PaRCE: Probabilistic and Reconstruction-based Competency Estimation for CNN-based Image ClassificationCode0
Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual AttacksCode0
Uni-Mlip: Unified Self-supervision for Medical Vision Language Pre-training—0
LPLgrad: Optimizing Active Learning Through Gradient Norm Sample Selection and Auxiliary Model TrainingCode0
MEGL: Multimodal Explanation-Guided Learning—0
Problem-dependent convergence bounds for randomized linear gradient compression—0
Invariant Shape Representation Learning For Image ClassificationCode0
Self-Supervised Learning in Deep Networks: A Pathway to Robust Few-Shot Classification—0
Exploring Emerging Trends and Research Opportunities in Visual Place Recognition—0
Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging—0
Just Leaf It: Accelerating Diffusion Classifiers with Hierarchical Class Pruning—0
Diagnostic Text-guided Representation Learning in Hierarchical Classification for Pathological Whole Slide Image—0
Deep Feature Response Discriminative CalibrationCode0
Multi-perspective Contrastive Logit Distillation—0
Hysteresis Activation Function for Efficient InferenceCode0
Evidential Federated Learning for Skin Lesion Image Classification—0
Embedding Byzantine Fault Tolerance into Federated Learning via Virtual Data-Driven Consistency Scoring PluginCode0
Adapting the Biological SSVEP Response to Artificial Neural Networks—0
On the Cost of Model-Serving Frameworks: An Experimental Evaluation—0
Outliers resistant image classification by anomaly detection—0
ResidualDroppath: Enhancing Feature Reuse over Residual Connections—0
RenderBender: A Survey on Adversarial Attacks Using Differentiable Rendering—0
SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision TransformersCode0
Heuristical Comparison of Vision Transformers Against Convolutional Neural Networks for Semantic Segmentation on Remote Sensing ImageryCode0
Efficient Whole Slide Image Classification through Fisher Vector Representation—0
ScaleNet: Scale Invariance Learning in Directed GraphsCode0
Computed tomography using meta-optics—0
Semantic segmentation on multi-resolution optical and microwave data using deep learning—0
Can KAN Work? Exploring the Potential of Kolmogorov-Arnold Networks in Computer Vision—0
Deep Active Learning in the Open World—0
Exploring Structural Nonlinearity in Binary Polariton-Based Neuromorphic Architectures—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
5DaViT-HTop 1 Accuracy90.2—Unverified
6Meta Pseudo Labels (EfficientNet-L2)Top 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
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90—Unverified