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 1676–1700 of 10420 papers

TitleStatusHype
Uncertainty-Aware SAR ATR: Defending Against Adversarial Attacks via Bayesian Neural Networks—0
Mini-Gemini: Mining the Potential of Multi-modality Vision Language ModelsCode7
Targeted Visualization of the Backbone of Encoder LLMsCode1
The Need for Speed: Pruning Transformers with One RecipeCode1
Boosting Few-Shot Learning with Disentangled Self-Supervised Learning and Meta-Learning for Medical Image Classification—0
Tiny Models are the Computational Saver for Large ModelsCode0
PlainMamba: Improving Non-Hierarchical Mamba in Visual RecognitionCode3
Task2Box: Box Embeddings for Modeling Asymmetric Task RelationshipsCode0
Histogram Layers for Neural Engineered FeaturesCode0
Enhancing Neural Network Representations with Prior Knowledge-Based NormalizationCode0
DeepGleason: a System for Automated Gleason Grading of Prostate Cancer using Deep Neural NetworksCode1
Assessing the Performance of Deep Learning for Automated Gleason Grading in Prostate Cancer—0
Leveraging Deep Learning and Xception Architecture for High-Accuracy MRI Classification in Alzheimer Diagnosis—0
CBGT-Net: A Neuromimetic Architecture for Robust Classification of Streaming DataCode0
On machine learning analysis of atomic force microscopy images for image classification, sample surface recognition—0
Multi-Task Learning with Multi-Task Optimization—0
A Deep Learning Architectures for Kidney Disease Classification—0
iDAT: inverse Distillation Adapter-TuningCode1
VLM-CPL: Consensus Pseudo Labels from Vision-Language Models for Human Annotation-Free Pathological Image ClassificationCode1
Do not trust what you trust: Miscalibration in Semi-supervised LearningCode0
ParFormer: A Vision Transformer with Parallel Mixer and Sparse Channel Attention Patch Embedding—0
Extracting Human Attention through Crowdsourced Patch Labeling—0
Your Image is My Video: Reshaping the Receptive Field via Image-To-Video Differentiable AutoAugmentation and Fusion—0
Clean-image Backdoor Attacks—0
Image Classification with Rotation-Invariant Variational Quantum Circuits—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