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 3226–3250 of 10420 papers

TitleStatusHype
Doubly Convolutional Neural Networks—0
Exploring Explainability in Video Action Recognition—0
Doubly Sparse: Sparse Mixture of Sparse Experts for Efficient Softmax Inference—0
C2AE: Class Conditioned Auto-Encoder for Open-set Recognition—0
Dilated Deep Residual Network for Image Denoising—0
Black-box adversarial attacks using Evolution Strategies—0
Exploring Explainability Methods for Graph Neural Networks—0
Black-box Adversarial Attacks on Monocular Depth Estimation Using Evolutionary Multi-objective Optimization—0
An Effective Gram Matrix Characterizes Generalization in Deep Networks—0
Exploring Feature Reuse in DenseNet Architectures—0
Exploring Modality Guidance to Enhance VFM-based Feature Fusion for UDA in 3D Semantic Segmentation—0
Exploring the significance of using perceptually relevant image decolorization method for scene classification—0
Extracurricular Learning: Knowledge Transfer Beyond Empirical Distribution—0
Diffusion models applied to skin and oral cancer classification—0
An Effective Fusion Method to Enhance the Robustness of CNN—0
A New Linear Scaling Rule for Private Adaptive Hyperparameter Optimization—0
DPSNN: A Differentially Private Spiking Neural Network with Temporal Enhanced Pooling—0
Exploring Cross-Domain Pretrained Model for Hyperspectral Image Classification—0
DQA: An Efficient Method for Deep Quantization of Deep Neural Network Activations—0
Diff-SySC: An Approach Using Diffusion Models for Semi-Supervised Image Classification—0
Drawing Multiple Augmentation Samples Per Image During Training Efficiently Decreases Test Error—0
Drift to Remember—0
DRO-Augment Framework: Robustness by Synergizing Wasserstein Distributionally Robust Optimization and Data Augmentation—0
An Extendable, Efficient and Effective Transformer-based Object Detector—0
DiffSpectralNet : Unveiling the Potential of Diffusion Models for Hyperspectral Image Classification—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
5Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2—Unverified
6DaViT-HTop 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