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 5801–5825 of 10420 papers

TitleStatusHype
A Novel Multi-Attention Driven System For Multi-Label Remote Sensing Image Classification—0
A CNN-RNN Framework for Image Annotation from Visual Cues and Social Network Metadata—0
3DCNN-DQN-RNN: A Deep Reinforcement Learning Framework for Semantic Parsing of Large-scale 3D Point Clouds—0
Bilinear discriminant feature line analysis for image feature extraction—0
State Classification of Cooking Objects Using a VGG CNN—0
Understanding the efficacy, reliability and resiliency of computer vision techniques for malware detection and future research directions—0
Discriminative Transfer Learning with Tree-based Priors—0
Discriminative Robust Deep Dictionary Learning for Hyperspectral Image Classification—0
Discriminative Pattern Mining for Breast Cancer Histopathology Image Classification via Fully Convolutional Autoencoder—0
Boosting Few-Shot Text Classification via Distribution Estimation—0
Discriminative Nonlinear Analysis Operator Learning: When Cosparse Model Meets Image Classification—0
Discriminative models for robust image classification—0
Discriminative Localization in CNNs for Weakly-Supervised Segmentation of Pulmonary Nodules—0
Discriminative Learning of Sum-Product Networks—0
Boosting Few-Shot Learning with Disentangled Self-Supervised Learning and Meta-Learning for Medical Image Classification—0
Discriminative Label Consistent Domain Adaptation—0
Discriminative k-shot learning using probabilistic models—0
Boosting Discriminative Visual Representation Learning with Scenario-Agnostic Mixup—0
Discriminative Distillation to Reduce Class Confusion in Continual Learning—0
Improving Deep Hyperspectral Image Classification Performance with Spectral Unmixing—0
Discriminative and Geometry Aware Unsupervised Domain Adaptation—0
An Empirical Investigation into Benchmarking Model Multiplicity for Trustworthy Machine Learning: A Case Study on Image Classification—0
Addressing Uncertainty in Imbalanced Histopathology Image Classification of HER2 Breast Cancer: An interpretable Ensemble Approach with Threshold Filtered Single Instance Evaluation (SIE)—0
Discriminability-enforcing loss to improve representation learning—0
Discrete Wavelet Transform-Based Capsule Network for Hyperspectral 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
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90—Unverified