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 8301–8325 of 10420 papers

TitleStatusHype
Explaining Image Classifiers by Counterfactual GenerationCode0
Provably Adversarially Robust Nearest Prototype ClassifiersCode0
Explaining Domain Shifts in Language: Concept erasing for Interpretable Image ClassificationCode0
From Seedling to Harvest: The GrowingSoy Dataset for Weed Detection in Soy Crops via Instance SegmentationCode0
From Trojan Horses to Castle Walls: Unveiling Bilateral Data Poisoning Effects in Diffusion ModelsCode0
On the Design of Black-box Adversarial Examples by Leveraging Gradient-free Optimization and Operator Splitting MethodCode0
From Volcano to Toyshop: Adaptive Discriminative Region Discovery for Scene RecognitionCode0
From Xception to NEXcepTion: New Design Decisions and Neural Architecture SearchCode0
Explaining Convolutional Neural Networks using Softmax Gradient Layer-wise Relevance PropagationCode0
Explaining Classifiers by Constructing Familiar ConceptsCode0
Coupling Adaptive Batch Sizes with Learning RatesCode0
Couplformer:Rethinking Vision Transformer with Coupling Attention MapCode0
On the design of convolutional neural networks for automatic detection of Alzheimer's diseaseCode0
Robustly Optimized Deep Feature Decoupling Network for Fatty Liver Diseases DetectionCode0
On the Effectiveness of Distillation in Mitigating Backdoors in Pre-trained EncoderCode0
Explainable Ensemble Machine Learning for Breast Cancer Diagnosis based on Ultrasound Image Texture FeaturesCode0
Explainability of Deep Neural Networks for Brain Tumor DetectionCode0
Data-Driven Neuron Allocation for Scale Aggregation NetworksCode0
FTA-FTL: A Fine-Tuned Aggregation Federated Transfer Learning Scheme for Lithology Microscopic Image ClassificationCode0
Provably Learning Diverse Features in Multi-View Data with Midpoint MixupCode0
Countering Adversarial Images using Input TransformationsCode0
Fully Convolutional Architectures for Multi-Class Segmentation in Chest RadiographsCode0
Provably Near-Optimal Federated Ensemble Distillation with Negligible OverheadCode0
On the Efficacy of Differentially Private Few-shot Image ClassificationCode0
Expert Kernel Generation Network Driven by Contextual Mapping for Hyperspectral Image ClassificationCode0
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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
10RevCol-HTop 1 Accuracy90—Unverified