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

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Showing 3751–3800 of 10419 papers

TitleStatusHype
SWIFT: Rapid Decentralized Federated Learning via Wait-Free Model CommunicationCode1
Provably Learning Diverse Features in Multi-View Data with Midpoint MixupCode0
Analyzing the Use of Influence Functions for Instance-Specific Data Filtering in Neural Machine Translation—0
MetaFormer Baselines for VisionCode3
Revisiting Sparse Convolutional Model for Visual RecognitionCode1
Drastically Reducing the Number of Trainable Parameters in Deep CNNs by Inter-layer Kernel-sharingCode1
Unsupervised Few-Shot Image Classification by Learning Features into Clustering SpaceCode1
Efficient Automatic Machine Learning via Design GraphsCode0
Imbalanced Classification in Medical Imaging via Regrouping—0
Collaborative Image Understanding—0
Boosting vision transformers for image retrievalCode1
Diffusion Visual Counterfactual ExplanationsCode1
Analysing Training-Data Leakage from Gradients through Linear Systems and Gradient MatchingCode0
Similarity of Neural Architectures using Adversarial Attack Transferability—0
Robustcaps: a transformation-robust capsule network for image classification—0
G2NetPL: Generic Game-Theoretic Network for Partial-Label Image Classification—0
Visual-Semantic Contrastive Alignment for Few-Shot Image Classification—0
Iterative collaborative routing among equivariant capsules for transformation-robust capsule networks—0
Standardized Medical Image Classification across Medical DisciplinesCode1
TTTFlow: Unsupervised Test-Time Training with Normalizing FlowCode1
Sustainable Personalisation and Explainability in Dyadic Data Systems—0
DGSSC: A Deep Generative Spectral-Spatial Classifier for Imbalanced Hyperspectral ImageryCode1
Efficient, probabilistic analysis of combinatorial neural codes—0
Training set cleansing of backdoor poisoning by self-supervised representation learning—0
A Unified View of Masked Image ModelingCode0
CroCo: Self-Supervised Pre-training for 3D Vision Tasks by Cross-View CompletionCode2
Few-Shot Learning of Compact Models via Task-Specific Meta Distillation—0
Pareto Manifold Learning: Tackling multiple tasks via ensembles of single-task modelsCode1
Layer-wise Relevance Propagation for Echo State Networks applied to Earth System Variability—0
On effects of Knowledge Distillation on Transfer Learning—0
2nd Place Solution to Google Universal Image EmbeddingCode1
Signal Processing for Implicit Neural Representations—0
Thinking Two Moves Ahead: Anticipating Other Users Improves Backdoor Attacks in Federated LearningCode0
Scaling & Shifting Your Features: A New Baseline for Efficient Model TuningCode1
Early Diagnosis of Retinal Blood Vessel Damage via Deep Learning-Powered Collective Intelligence ModelsCode0
Bootstrapping the Relationship Between Images and Their Clean and Noisy LabelsCode0
Multiple Instance Learning via Iterative Self-Paced Supervised Contrastive LearningCode1
Packed-Ensembles for Efficient Uncertainty Estimation—0
Self-Supervised Learning Through Efference CopiesCode0
A Novel Membership Inference Attack against Dynamic Neural Networks by Utilizing Policy Networks Information—0
Vision-Language Pre-training: Basics, Recent Advances, and Future TrendsCode3
Explaining Image Classification with Visual DebatesCode0
Histopathological Image Classification based on Self-Supervised Vision Transformer and Weak LabelsCode0
Federated Learning with Privacy-Preserving Ensemble Attention Distillation—0
Compressive Image Classification using Deterministic Sensing Matrices—0
POGD: Gradient Descent with New Stochastic Rules—0
Attention Regularized Laplace Graph for Domain Adaptation—0
Providing Error Detection for Deep Learning Image Classifiers Using Self-Explainability—0
MergedNET: A simple approach for one-shot learning in siamese networks based on similarity layersCode0
CAP: Correlation-Aware Pruning for Highly-Accurate Sparse Vision Models—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