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 9826–9850 of 10420 papers

TitleStatusHype
Improving Multi-fidelity Optimization with a Recurring Learning Rate for Hyperparameter Tuning—0
Improving Normalization with the James-Stein Estimator—0
Improving Object Detection with Selective Self-supervised Self-training—0
Improving Performance of Semi-Supervised Learning by Adversarial Attacks—0
Improving plant disease classification by adaptive minimal ensembling—0
Improving Quaternion Neural Networks with Quaternionic Activation Functions—0
Improving Resnet-9 Generalization Trained on Small Datasets—0
Robust Contrastive Active Learning with Feature-guided Query Strategies—0
Improving Robustness and Reliability in Medical Image Classification with Latent-Guided Diffusion and Nested-Ensembles—0
Improving Robustness and Uncertainty Modelling in Neural Ordinary Differential Equations—0
Improving Sample Complexity with Observational Supervision—0
Improving Semantic Embedding Consistency by Metric Learning for Zero-Shot Classification—0
Improving Feature Stability during Upsampling -- Spectral Artifacts and the Importance of Spatial Context—0
Improving STDP-based Visual Feature Learning with Whitening—0
Improving Strong-Scaling of CNN Training by Exploiting Finer-Grained Parallelism—0
Improving Tail-Class Representation with Centroid Contrastive Learning—0
Improving the Accuracy of Learning Example Weights for Imbalance Classification—0
Improving the accuracy of neural networks in analog computing-in-memory systems by a generalized quantization method—0
Improving the Deployment of Recycling Classification through Efficient Hyper-Parameter Analysis—0
Improving the Effectiveness of Deep Generative Data—0
Improving the Reliability for Confidence Estimation—0
Improving training of deep neural networks via Singular Value Bounding—0
Improving Transferability of Deep Neural Networks—0
Improving Whole Slide Segmentation Through Visual Context - A Systematic Study—0
IMWA: Iterative Model Weight Averaging Benefits Class-Imbalanced Learning Tasks—0
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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
10RevCol-HTop 1 Accuracy90—Unverified