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 601625 of 10419 papers

TitleStatusHype
Adaptive Risk Minimization: Learning to Adapt to Domain ShiftCode1
Combining Human Predictions with Model Probabilities via Confusion Matrices and CalibrationCode1
Combining Metric Learning and Attention Heads For Accurate and Efficient Multilabel Image ClassificationCode1
Complementary-Label Learning for Arbitrary Losses and ModelsCode1
Combating Label Noise in Deep Learning Using AbstentionCode1
Achieving Fairness Through Channel Pruning for Dermatological Disease DiagnosisCode1
Combating noisy labels by agreement: A joint training method with co-regularizationCode1
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
Combining GANs and AutoEncoders for Efficient Anomaly DetectionCode1
Compositional Explanations of NeuronsCode1
Co2L: Contrastive Continual LearningCode1
Co^2L: Contrastive Continual LearningCode1
CoAtNet: Marrying Convolution and Attention for All Data SizesCode1
DiG-IN: Diffusion Guidance for Investigating Networks -- Uncovering Classifier Differences Neuron Visualisations and Visual Counterfactual ExplanationsCode1
AdaptiveMix: Improving GAN Training via Feature Space ShrinkageCode1
CNN Filter DB: An Empirical Investigation of Trained Convolutional FiltersCode1
CoCa: Contrastive Captioners are Image-Text Foundation ModelsCode1
ClusterFormer: Clustering As A Universal Visual LearnerCode1
Adaptive Mask Sampling and Manifold to Euclidean Subspace Learning with Distance Covariance Representation for Hyperspectral Image ClassificationCode1
Cluster-to-Conquer: A Framework for End-to-End Multi-Instance Learning for Whole Slide Image ClassificationCode1
CLR: Channel-wise Lightweight Reprogramming for Continual LearningCode1
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
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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