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 2926–2950 of 10420 papers

TitleStatusHype
Class Incremental Learning with Task-Specific Batch Normalization and Out-of-Distribution Detection—0
Retrieval-enriched zero-shot image classification in low-resource domains—0
How many classifiers do we need?—0
FISHing in Uncertainty: Synthetic Contrastive Learning for Genetic Aberration DetectionCode0
Aerial Flood Scene Classification Using Fine-Tuned Attention-based Architecture for Flood-Prone Countries in South Asia—0
Semantic Knowledge Distillation for Onboard Satellite Earth Observation Image ClassificationCode0
Learning local discrete features in explainable-by-design convolutional neural networksCode0
Domain-decomposed image classification algorithms using linear discriminant analysis and convolutional neural networks—0
CLIPErase: Efficient Unlearning of Visual-Textual Associations in CLIP—0
Multilingual Vision-Language Pre-training for the Remote Sensing DomainCode0
Developing Convolutional Neural Networks using a Novel Lamarckian Co-Evolutionary Algorithm—0
Multi-Level Feature Distillation of Joint Teachers Trained on Distinct Image DatasetsCode0
Bayesian Optimization for Hyperparameters Tuning in Neural Networks—0
FakeFormer: Efficient Vulnerability-Driven Transformers for Generalisable Deepfake DetectionCode0
Breast Cancer Histopathology Classification using CBAM-EfficientNetV2 with Transfer Learning—0
Saliency-Based diversity and fairness Metric and FaceKeepOriginalAugment: A Novel Approach for Enhancing Fairness and Diversity—0
Active Learning for Vision-Language Models—0
AiSciVision: A Framework for Specializing Large Multimodal Models in Scientific Image ClassificationCode0
Sequential Large Language Model-Based Hyper-parameter OptimizationCode0
Historical Test-time Prompt Tuning for Vision Foundation Models—0
Annotation Efficiency: Identifying Hard Samples via Blocked Sparse Linear Bandits—0
Enhancing CNN Classification with Lamarckian Memetic Algorithms and Local Search—0
OReole-FM: successes and challenges toward billion-parameter foundation models for high-resolution satellite imagery—0
A Multimodal Approach For Endoscopic VCE Image Classification Using BiomedCLIP-PubMedBERTCode0
Learning the Regularization Strength for Deep Fine-Tuning via a Data-Emphasized Variational ObjectiveCode0
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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
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90—Unverified