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 9051–9075 of 10420 papers

TitleStatusHype
Identification and Recognition of Rice Diseases and Pests Using Convolutional Neural Networks—0
Protection Against Reconstruction and Its Applications in Private Federated Learning—0
Deep Hierarchical Machine: a Flexible Divide-and-Conquer Architecture—0
MetaGAN: An Adversarial Approach to Few-Shot Learning—0
Modern Neural Networks Generalize on Small Data Sets—0
Classifying a specific image region using convolutional nets with an ROI mask as inputCode0
GLoMo: Unsupervised Learning of Transferable Relational Graphs—0
Learning to Specialize with Knowledge Distillation for Visual Question Answering—0
Stochastic Training of Residual Networks: a Differential Equation Viewpoint—0
Symbolic Graph Reasoning Meets ConvolutionsCode0
Structure-Aware Convolutional Neural NetworksCode0
Snapshot Distillation: Teacher-Student Optimization in One Generation—0
Thwarting Adversarial Examples: An L_0-Robust Sparse Fourier Transform—0
Modeling natural language emergence with integral transform theory and reinforcement learningCode0
On the Computational Inefficiency of Large Batch Sizes for Stochastic Gradient Descent—0
Graph-Based Global Reasoning NetworksCode0
Effective, Fast, and Memory-Efficient Compressed Multi-function Convolutional Neural Networks for More Accurate Medical Image Classification—0
Generalized Coarse-to-Fine Visual Recognition with Progressive Training—0
TEA-DNN: the Quest for Time-Energy-Accuracy Co-optimized Deep Neural Networks—0
Unsupervised Meta-Learning For Few-Shot Image Classification—0
ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural NetworkCode0
Adversarial Attacks for Optical Flow-Based Action Recognition Classifiers—0
Sequentially Aggregated Convolutional NetworksCode0
Efficient non-uniform quantizer for quantized neural network targeting reconfigurable hardware—0
Stochastic Gradient Push for Distributed Deep LearningCode0
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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