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 6001–6050 of 10420 papers

TitleStatusHype
GraphViz2Vec: A Structure-aware Feature Generation Model to Improve Classification in GNNs—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
A Hybrid Architecture for On-Device Compressive Machine Learning—0
Graph Structural Aggregation for Explainable Learning—0
Locally Asynchronous Stochastic Gradient Descent for Decentralised Deep Learning—0
Graphs for deep learning representations—0
Locally Differentially Private Gradient Tracking for Distributed Online Learning over Directed Graphs—0
MGiaD: Multigrid in all dimensions. Efficiency and robustness by coarsening in resolution and channel dimensions—0
Graph Semi-Supervised Learning for Point Classification on Data Manifolds—0
Graph-RISE: Graph-Regularized Image Semantic Embedding—0
Counterfactual Thinking for Long-tailed Information Extraction—0
Graph-propagation based Correlation Learning for Weakly Supervised Fine-grained Image Classification—0
Graph Neural Networks for UnsupervisedDomain Adaptation of Histopathological ImageAnalytics—0
A Hybrid Approach with Optimization and Metric-based Meta-Learner for Few-Shot Learning—0
LoCa: Logit Calibration for Knowledge Distillation—0
Graph Neural Networks for Image Classification and Reinforcement Learning using Graph representations—0
Counterfactual Influence as a Distributional Quantity—0
A Transformer-in-Transformer Network Utilizing Knowledge Distillation for Image Recognition—0
An Effective Gram Matrix Characterizes Generalization in Deep Networks—0
Local-to-Global Self-Supervised Representation Learning for Diabetic Retinopathy Grading—0
Active Transfer Learning Network: A Unified Deep Joint Spectral-Spatial Feature Learning Model For Hyperspectral Image Classification—0
MetricOpt: Learning to Optimize Black-Box Evaluation Metrics—0
MgNet: A Unified Framework of Multigrid and Convolutional Neural Network—0
LocTex: Learning Data-Efficient Visual Representations from Localized Textual Supervision—0
Logarithmic Lenses: Exploring Log RGB Data for Image Classification—0
An Effective Label Noise Model for DNN Text Classification—0
Log-Hilbert-Schmidt metric between positive definite operators on Hilbert spaces—0
MiDaS v3.1 -- A Model Zoo for Robust Monocular Relative Depth Estimation—0
Logit Attenuating Weight Normalization—0
GFCN: A New Graph Convolutional Network Based on Parallel Flows—0
LoGra-Med: Long Context Multi-Graph Alignment for Medical Vision-Language Model—0
Graph Convolutional Networks based on Manifold Learning for Semi-Supervised Image Classification—0
LoKi: Low-dimensional KAN for Efficient Fine-tuning Image Models—0
DIME-FM : DIstilling Multimodal and Efficient Foundation Models—0
Causality-aware counterfactual confounding adjustment for feature representations learned by deep models—0
Graph Clustering With Missing Data: Convex Algorithms and Analysis—0
DINO-CXR: A self supervised method based on vision transformer for chest X-ray classification—0
Graph Classification with Geometric Scattering—0
Graph-based Representation for Image based on Granular-ball—0
Graph based Label Enhancement for Multi-instance Multi-label learning—0
Co-Training 2L Submodels for Visual Recognition—0
DiRaC-I: Identifying Diverse and Rare Training Classes for Zero-Shot Learning—0
Looking Beyond Single Images for Contrastive Semantic Segmentation Learning—0
A High-Performance Adaptive Quantization Approach for Edge CNN Applications—0
Look One and More: Distilling Hybrid Order Relational Knowledge for Cross-Resolution Image Recognition—0
Metastatic Cancer Image Classification Based On Deep Learning Method—0
Co-training 2^L Submodels for Visual Recognition—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