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 2726–2750 of 10420 papers

TitleStatusHype
Review helps learn better: Temporal Supervised Knowledge Distillation—0
FedDefender: Backdoor Attack Defense in Federated LearningCode1
The Forward-Forward Algorithm as a feature extractor for skin lesion classification: A preliminary study—0
Query-Efficient Decision-based Black-Box Patch Attack—0
SysNoise: Exploring and Benchmarking Training-Deployment System Inconsistency—0
Forward-Forward Algorithm for Hyperspectral Image Classification: A Preliminary Study—0
MobileViG: Graph-Based Sparse Attention for Mobile Vision ApplicationsCode1
Single-Stage Heavy-Tailed Food Classification—0
Filter Pruning for Efficient CNNs via Knowledge-driven Differential Filter SamplerCode0
More for Less: Compact Convolutional Transformers Enable Robust Medical Image Classification with Limited Data—0
Sphere2Vec: A General-Purpose Location Representation Learning over a Spherical Surface for Large-Scale Geospatial PredictionsCode1
Vision Through the Veil: Differential Privacy in Federated Learning for Medical Image Classification—0
Designing Stable Neural Networks using Convex Analysis and ODEsCode0
Post-train Black-box Defense via Bayesian Boundary Correction—0
CLIPAG: Towards Generator-Free Text-to-Image Generation—0
Boosting the Generalization Ability for Hyperspectral Image Classification using Spectral-spatial Axial Aggregation Transformer—0
PCDAL: A Perturbation Consistency-Driven Active Learning Approach for Medical Image Segmentation and ClassificationCode0
BinaryViT: Pushing Binary Vision Transformers Towards Convolutional ModelsCode1
Cross-Inferential Networks for Source-free Unsupervised Domain Adaptation—0
A Structurally Regularized CNN Architecture via Adaptive Subband Decomposition—0
Rapid-INR: Storage Efficient CPU-free DNN Training Using Implicit Neural RepresentationCode0
Does Saliency-Based Training bring Robustness for Deep Neural Networks in Image Classification?—0
Pseudo-Bag Mixup Augmentation for Multiple Instance Learning-Based Whole Slide Image Classification—0
Approximated Prompt Tuning for Vision-Language Pre-trained Models—0
Predictive Coding beyond Correlations—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