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 7951–8000 of 10420 papers

TitleStatusHype
Improving Few-Shot Image Classification Using Machine- and User-Generated Natural Language Descriptions—0
Deep Collaborative Learning for Visual Recognition—0
Improving Explainability of Image Classification in Scenarios with Class Overlap: Application to COVID-19 and Pneumonia—0
Improving End-to-End Memory Networks with Unified Weight Tying—0
Deep CNNs for Peripheral Blood Cell Classification—0
Automating Defense Against Adversarial Attacks: Discovery of Vulnerabilities and Application of Multi-INT Imagery to Protect Deployed Models—0
A Low-cost and Ultra-lightweight Binary Neural Network for Traffic Signal Recognition—0
Adaptive Class Preserving Representation for Image Classification—0
Accumulator-Aware Post-Training Quantization—0
How Good is your Explanation? Algorithmic Stability Measures to Assess the Quality of Explanations for Deep Neural Networks—0
CLIP Embeddings for AI-Generated Image Detection: A Few-Shot Study with Lightweight Classifier—0
MedUnifier: Unifying Vision-and-Language Pre-training on Medical Data with Vision Generation Task using Discrete Visual Representations—0
DomainSiam: Domain-Aware Siamese Network for Visual Object Tracking—0
Self-Attention Capsule Networks for Object Classification—0
Improving Deep Neural Networks with Probabilistic Maxout Units—0
Deep CNNs for large scale species classification—0
Improving Deep Learning through Automatic Programming—0
Automatic Skin Lesion Analysis using Large-scale Dermoscopy Images and Deep Residual Networks—0
Improving CNN classifiers by estimating test-time priors—0
Resampled Datasets Are Not Enough: Mitigating Societal Bias Beyond Single Attributes—0
Deep Cervix Model Development from Heterogeneous and Partially Labeled Image Datasets—0
Automatic Script Identification in the Wild—0
ResBit: Residual Bit Vector for Categorical Values—0
ResBuilder: Automated Learning of Depth with Residual Structures—0
A Lost Opportunity for Vision-Language Models: A Comparative Study of Online Test-Time Adaptation for Vision-Language Models—0
Improving Chest X-Ray Classification by RNN-based Patient Monitoring—0
Improving Bag-of-Visual-Words Towards Effective Facial Expressive Image Classification—0
ReSet: Learning Recurrent Dynamic Routing in ResNet-like Neural Networks—0
A Pseudo-labelling Auto-Encoder for unsupervised image classification—0
Deep Boosting: Layered Feature Mining for General Image Classification—0
Improving Adversarially Robust Few-Shot Image Classification With Generalizable Representations—0
Improved Techniques for Adversarial Discriminative Domain Adaptation—0
Deep Boosting: Joint Feature Selection and Analysis Dictionary Learning in Hierarchy—0
ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts—0
Adaptive Classification of Interval-Valued Time Series—0
Residual Error: a New Performance Measure for Adversarial Robustness—0
Deep Axial Hypercomplex Networks—0
Improve Unsupervised Domain Adaptation with Mixup Training—0
Deep Autoencoder Model Construction Based on Pytorch—0
Residual Squeeze VGG16—0
Improvement Strategies for Few-Shot Learning in OCT Image Classification of Rare Retinal Diseases—0
Resisting Adversarial Attacks in Deep Neural Networks using Diverse Decision Boundaries—0
ResizeMix: Mixing Data with Preserved Object Information and True Labels—0
Improvement of image classification by multiple optical scattering—0
Deep AUC Maximization for Medical Image Classification: Challenges and Opportunities—0
Automatic Radiology Report Generation based on Multi-view Image Fusion and Medical Concept Enrichment—0
Identify ambiguous tasks combining crowdsourced labels by weighting Areas Under the Margin—0
Improved Training Speed, Accuracy, and Data Utilization via Loss Function Optimization—0
Deep Attributes from Context-Aware Regional Neural Codes—0
Improved Trainable Calibration Method for Neural Networks on Medical Imaging 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
10RevCol-HTop 1 Accuracy90—Unverified