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 2251–2275 of 10420 papers

TitleStatusHype
Deep Fried ConvnetsCode1
Explaining and Harnessing Adversarial ExamplesCode1
Unsupervised Domain Adaptation by BackpropagationCode1
Going Deeper with ConvolutionsCode1
Very Deep Convolutional Networks for Large-Scale Image RecognitionCode1
ImageNet Large Scale Visual Recognition ChallengeCode1
Recurrent Models of Visual AttentionCode1
OverFeat: Integrated Recognition, Localization and Detection using Convolutional NetworksCode1
Improving neural networks by preventing co-adaptation of feature detectorsCode1
Automatic Classification and Segmentation of Tunnel Cracks Based on Deep Learning and Visual Explanations—0
Adversarial attacks to image classification systems using evolutionary algorithms—0
MUPAX: Multidimensional Problem Agnostic eXplainable AI—0
Efficient Adaptation of Pre-trained Vision Transformer underpinned by Approximately Orthogonal Fine-Tuning Strategy—0
Federated Learning for Commercial Image Sources—0
Hashed Watermark as a Filter: Defeating Forging and Overwriting Attacks in Weight-based Neural Network WatermarkingCode0
Transferring Styles for Reduced Texture Bias and Improved Robustness in Semantic Segmentation Networks—0
FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise—0
ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark EvaluationCode0
Admissibility of Stein Shrinkage for Batch Normalization in the Presence of Adversarial Attacks—0
GNN-ViTCap: GNN-Enhanced Multiple Instance Learning with Vision Transformers for Whole Slide Image Classification and Captioning—0
Model-free Optical Processors using In Situ Reinforcement Learning with Proximal Policy Optimization—0
SoftReMish: A Novel Activation Function for Enhanced Convolutional Neural Networks for Visual Recognition Performance—0
MVNet: Hyperspectral Remote Sensing Image Classification Based on Hybrid Mamba-Transformer Vision Backbone ArchitectureCode0
Transferring Visual Explainability of Self-Explaining Models through Task Arithmetic—0
Beyond Accuracy: Metrics that Uncover What Makes a 'Good' Visual DescriptorCode0
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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