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 351375 of 10419 papers

TitleStatusHype
Clusterability as an Alternative to Anchor Points When Learning with Noisy LabelsCode1
CoWs on Pasture: Baselines and Benchmarks for Language-Driven Zero-Shot Object NavigationCode1
CLIP meets DINO for Tuning Zero-Shot Classifier using Unlabeled Image CollectionsCode1
Shredder: Learning Noise Distributions to Protect Inference PrivacyCode1
CLIP the Gap: A Single Domain Generalization Approach for Object DetectionCode1
ClusterFormer: Clustering As A Universal Visual LearnerCode1
A Comprehensive Survey on Graph Neural NetworksCode1
CleanNet: Transfer Learning for Scalable Image Classifier Training with Label NoiseCode1
CLIP4IDC: CLIP for Image Difference CaptioningCode1
CLCNet: Rethinking of Ensemble Modeling with Classification Confidence NetworkCode1
CLCC: Contrastive Learning for Color ConstancyCode1
Clean-Label Backdoor Attacks on Video Recognition ModelsCode1
Class Distance Weighted Cross-Entropy Loss for Ulcerative Colitis Severity EstimationCode1
Class-Difficulty Based Methods for Long-Tailed Visual RecognitionCode1
Optimized spiking neurons classify images with high accuracy through temporal coding with two spikesCode1
Adversarial Robustness on In- and Out-Distribution Improves ExplainabilityCode1
Embedded Prompt Tuning: Towards Enhanced Calibration of Pretrained Models for Medical ImagesCode1
Class-Balanced Loss Based on Effective Number of SamplesCode1
Class-Incremental Grouping Network for Continual Audio-Visual LearningCode1
CLIP-guided Federated Learning on Heterogeneous and Long-Tailed DataCode1
Cluster-to-Conquer: A Framework for End-to-End Multi-Instance Learning for Whole Slide Image ClassificationCode1
Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional NetworksCode1
Circumventing Outliers of AutoAugment with Knowledge DistillationCode1
Class Adaptive Network CalibrationCode1
CheXWorld: Exploring Image World Modeling for Radiograph Representation LearningCode1
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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
5DaViT-HTop 1 Accuracy90.2Unverified
6Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2Unverified
7SwinV2-GTop 1 Accuracy90.17Unverified
8MAWS (ViT-6.5B)Top 1 Accuracy90.1Unverified
9Florence-CoSwin-HTop 1 Accuracy90.05Unverified
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90Unverified