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 51–75 of 10419 papers

TitleStatusHype
Can We Infer Confidential Properties of Training Data from LLMs?—0
DeepTraverse: A Depth-First Search Inspired Network for Algorithmic Visual Understanding—0
Detecção da Psoríase Utilizando Visão Computacional: Uma Abordagem Comparativa Entre CNNs e Vision Transformers—0
ScalableHD: Scalable and High-Throughput Hyperdimensional Computing Inference on Multi-Core CPUs—0
Hyperspectral Image Classification via Transformer-based Spectral-Spatial Attention Decoupling and Adaptive GatingCode0
InceptionMamba: An Efficient Hybrid Network with Large Band Convolution and Bottleneck MambaCode1
An Adaptive Method Stabilizing Activations for Enhanced GeneralizationCode0
Biologically Inspired Deep Learning Approaches for Fetal Ultrasound Image Classification—0
Normalized Radon Cumulative Distribution Transforms for Invariance and Robustness in Optimal Transport Based Image ClassificationCode0
Hyperbolic Dual Feature Augmentation for Open-Environment—0
Mind the Gap: Removing the Discretization Gap in Differentiable Logic Gate Networks—0
Improving Memory Efficiency for Training KANs via Meta LearningCode0
Mobility-Aware Asynchronous Federated Learning with Dynamic Sparsification—0
pFedSOP : Accelerating Training Of Personalized Federated Learning Using Second-Order Optimization—0
SAFE: Finding Sparse and Flat Minima to Improve PruningCode1
Rewriting the Budget: A General Framework for Black-Box Attacks Under Cost AsymmetryCode0
FPDANet: A Multi-Section Classification Model for Intelligent Screening of Fetal Ultrasound—0
Eigenspectrum Analysis of Neural Networks without Aspect Ratio BiasCode1
Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts—0
Recent Advances in Medical Image Classification—0
KOALA++: Efficient Kalman-Based Optimization of Neural Networks with Gradient-Covariance Products—0
Enhancing Interpretable Image Classification Through LLM Agents and Conditional Concept Bottleneck Models—0
Quantifying task-relevant representational similarity using decision variable correlation—0
Structured Pruning and Quantization for Learned Image CompressionCode0
OD3: Optimization-free Dataset Distillation for Object DetectionCode1
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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