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

Showing 17261750 of 10420 papers

TitleStatusHype
Better (pseudo-)labels for semi-supervised instance segmentation0
Multiple Teachers-Meticulous Student: A Domain Adaptive Meta-Knowledge Distillation Model for Medical Image ClassificationCode0
Potential of Domain Adaptation in Machine Learning in Ecology and Hydrology to Improve Model Extrapolability0
Fuzzy Rank-based Late Fusion Technique for Cytology image Segmentation0
Understanding Robustness of Visual State Space Models for Image ClassificationCode0
RetMIL: Retentive Multiple Instance Learning for Histopathological Whole Slide Image Classification0
Automatic location detection based on deep learningCode0
Forward Learning of Graph Neural NetworksCode1
When Training-Free NAS Meets Vision Transformer: A Neural Tangent Kernel Perspective0
InterLUDE: Interactions between Labeled and Unlabeled Data to Enhance Semi-Supervised LearningCode1
PALM: Pushing Adaptive Learning Rate Mechanisms for Continual Test-Time AdaptationCode0
Few-Shot Image Classification and Segmentation as Visual Question Answering Using Vision-Language Models0
Fast and reliable uncertainty quantification with neural network ensembles for industrial image classification0
RadCLIP: Enhancing Radiologic Image Analysis through Contrastive Language-Image Pre-trainingCode1
Deep Learning for Multi-Level Detection and Localization of Myocardial Scars Based on Regional Strain Validated on Virtual Patients0
Multi-criteria Token Fusion with One-step-ahead Attention for Efficient Vision TransformersCode1
Frozen Feature Augmentation for Few-Shot Image Classification0
Learning on JPEG-LDPC Compressed Images: Classifying with Syndromes0
Achieving Pareto Optimality using Efficient Parameter Reduction for DNNs in Resource-Constrained Edge Environment0
Can We Talk Models Into Seeing the World Differently?Code1
CardioCaps: Attention-based Capsule Network for Class-Imbalanced Echocardiogram ClassificationCode0
XCoOp: Explainable Prompt Learning for Computer-Aided Diagnosis via Concept-guided Context Optimization0
The First to Know: How Token Distributions Reveal Hidden Knowledge in Large Vision-Language Models?Code1
Transformers Get Stable: An End-to-End Signal Propagation Theory for Language ModelsCode1
Randomized Principal Component Analysis for Hyperspectral Image Classification0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CoCa (finetuned)Top 1 Accuracy91Unverified
2Model soups (BASIC-L)Top 1 Accuracy90.98Unverified
3Model soups (ViT-G/14)Top 1 Accuracy90.94Unverified
4DaViT-GTop 1 Accuracy90.4Unverified
5Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2Unverified
6DaViT-HTop 1 Accuracy90.2Unverified
7SwinV2-GTop 1 Accuracy90.17Unverified
8MAWS (ViT-6.5B)Top 1 Accuracy90.1Unverified
9Florence-CoSwin-HTop 1 Accuracy90.05Unverified
10RevCol-HTop 1 Accuracy90Unverified