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 626650 of 10419 papers

TitleStatusHype
Communication-Efficient Federated Learning Based on Explanation-Guided Pruning for Remote Sensing Image ClassificationCode1
Combining GANs and AutoEncoders for Efficient Anomaly DetectionCode1
Combating noisy labels by agreement: A joint training method with co-regularizationCode1
Combining Human Predictions with Model Probabilities via Confusion Matrices and CalibrationCode1
Comparing Kullback-Leibler Divergence and Mean Squared Error Loss in Knowledge DistillationCode1
CoDeNet: Efficient Deployment of Input-Adaptive Object Detection on Embedded FPGAsCode1
CODE-CL: Conceptor-Based Gradient Projection for Deep Continual LearningCode1
Collaborative Transformers for Grounded Situation RecognitionCode1
Adaptive Edge Offloading for Image Classification Under Rate LimitCode1
CoCa: Contrastive Captioners are Image-Text Foundation ModelsCode1
Co-Correcting: Noise-tolerant Medical Image Classification via mutual Label CorrectionCode1
Combating Label Noise in Deep Learning Using AbstentionCode1
Complementary-Label Learning for Arbitrary Losses and ModelsCode1
Adaptive DropBlock Enhanced Generative Adversarial Networks for Hyperspectral Image ClassificationCode1
CNN Filter DB: An Empirical Investigation of Trained Convolutional FiltersCode1
Co^2L: Contrastive Continual LearningCode1
ClusterFormer: Clustering As A Universal Visual LearnerCode1
Cluster-to-Conquer: A Framework for End-to-End Multi-Instance Learning for Whole Slide Image ClassificationCode1
Systematic comparison of semi-supervised and self-supervised learning for medical image classificationCode1
Clusterability as an Alternative to Anchor Points When Learning with Noisy LabelsCode1
CMW-Net: Learning a Class-Aware Sample Weighting Mapping for Robust Deep LearningCode1
Co2L: Contrastive Continual LearningCode1
CLIP the Gap: A Single Domain Generalization Approach for Object DetectionCode1
FocusNet: Classifying Better by Focusing on Confusing ClassesCode1
An Analysis on Ensemble Learning optimized Medical Image Classification with Deep Convolutional Neural NetworksCode1
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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