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 6001–6025 of 10420 papers

TitleStatusHype
Meta-Gating Framework for Fast and Continuous Resource Optimization in Dynamic Wireless Environments—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
Meta-Learner with Linear Nulling—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
Locally Differentially Private Gradient Tracking for Distributed Online Learning over Directed Graphs—0
Graph-propagation based Correlation Learning for Weakly Supervised Fine-grained Image Classification—0
Locally optimal detection of stochastic targeted universal adversarial perturbations—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
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
LoCa: Logit Calibration for Knowledge Distillation—0
Active Transfer Learning Network: A Unified Deep Joint Spectral-Spatial Feature Learning Model For Hyperspectral Image Classification—0
MetaGAN: An Adversarial Approach to Few-Shot Learning—0
Meta-Learning of NAS for Few-shot Learning in Medical Image Applications—0
An Effective Gram Matrix Characterizes Generalization in Deep Networks—0
GFCN: A New Graph Convolutional Network Based on Parallel Flows—0
Meta-DRN: Meta-Learning for 1-Shot Image Segmentation—0
Graph Convolutional Networks based on Manifold Learning for Semi-Supervised Image Classification—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