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 10301–10350 of 10420 papers

TitleStatusHype
Local Color Contrastive Descriptor for Image Classification—0
Local descriptor-based multi-prototype network for few-shot Learning—0
Local Descriptors Weighted Adaptive Threshold Filtering For Few-Shot Learning—0
Local-Global Shifting Vision Transformers—0
Locality and Structure Regularized Low Rank Representation for Hyperspectral Image Classification—0
Locality Constrained Analysis Dictionary Learning via K-SVD Algorithm—0
Locality Guided Neural Networks for Explainable Artificial Intelligence—0
Localization-Aware Active Learning for Object Detection—0
Localized Adversarial Training for Increased Accuracy and Robustness in Image Classification—0
Localizing Interpretable Multi-scale informative Patches Derived from Media Classification Task—0
Locally Asynchronous Stochastic Gradient Descent for Decentralised Deep Learning—0
Locally Differentially Private Distributed Online Learning with Guaranteed Optimality—0
Locally Differentially Private Gradient Tracking for Distributed Online Learning over Directed Graphs—0
Locally optimal detection of stochastic targeted universal adversarial perturbations—0
Locally-Supervised Deep Hybrid Model for Scene Recognition—0
Locally-Transferred Fisher Vectors for Texture Classification—0
Local Magnification for Data and Feature Augmentation—0
LocalNorm: Robust Image Classification through Dynamically Regularized Normalization—0
LoCa: Logit Calibration for Knowledge Distillation—0
Local-to-Global Self-Supervised Representation Learning for Diabetic Retinopathy Grading—0
Location-Aware Self-Supervised Transformers for Semantic Segmentation—0
LocTex: Learning Data-Efficient Visual Representations from Localized Textual Supervision—0
Logarithmic Lenses: Exploring Log RGB Data for Image Classification—0
Log-Hilbert-Schmidt metric between positive definite operators on Hilbert spaces—0
Logical Activation Functions: Logit-space equivalents of Probabilistic Boolean Operators—0
Logit Attenuating Weight Normalization—0
Logit-Based Ensemble Distribution Distillation for Robust Autoregressive Sequence Uncertainties—0
LoGra-Med: Long Context Multi-Graph Alignment for Medical Vision-Language Model—0
LoKi: Low-dimensional KAN for Efficient Fine-tuning Image Models—0
Looking at the posterior: accuracy and uncertainty of neural-network predictions—0
Looking Beyond Single Images for Contrastive Semantic Segmentation Learning—0
Look One and More: Distilling Hybrid Order Relational Knowledge for Cross-Resolution Image Recognition—0
LookupViT: Compressing visual information to a limited number of tokens—0
Loom: Exploiting Weight and Activation Precisions to Accelerate Convolutional Neural Networks—0
Losing momentum in continuous-time stochastic optimisation—0
Supervision Interpolation via LossMix: Generalizing Mixup for Object Detection and Beyond—0
Lost in Translation: Modern Neural Networks Still Struggle With Small Realistic Image Transformations—0
LoTLIP: Improving Language-Image Pre-training for Long Text Understanding—0
Lottery Hypothesis based Unsupervised Pre-training for Model Compression in Federated Learning—0
Low-Light Enhancement Effect on Classification and Detection: An Empirical Study—0
Low-Memory Neural Network Training: A Technical Report—0
Low-Rank Discriminative Least Squares Regression for Image Classification—0
Low-rank features based double transformation matrices learning for image classification—0
Low-Rank Pairwise Alignment Bilinear Network For Few-Shot Fine-Grained Image Classification—0
Zero-shot and Few-shot Learning with Knowledge Graphs: A Comprehensive Survey—0
Low Saturation Confidence Distribution-based Test-Time Adaptation for Cross-Domain Remote Sensing Image Classification—0
Low Tensor-Rank Adaptation of Kolmogorov--Arnold Networks—0
LPRNet: Lightweight Deep Network by Low-rank Pointwise Residual Convolution—0
LQF: Linear Quadratic Fine-Tuning—0
Lung Nodule Classification Using Biomarkers, Volumetric Radiomics and 3D CNNs—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