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 6901–6925 of 10420 papers

TitleStatusHype
How to distribute data across tasks for meta-learning?—0
CrossoverScheduler: Overlapping Multiple Distributed Training Applications in a Crossover Manner—0
Efficient Sparse Artificial Neural Networks—0
Uncertainty-guided Model Generalization to Unseen Domains—0
Learnable Companding Quantization for Accurate Low-bit Neural Networks—0
Interleaving Learning, with Application to Neural Architecture Search—0
Evaluating COPY-BLEND Augmentation for Low Level Vision Tasks—0
Why flatness does and does not correlate with generalization for deep neural networks—0
TransMed: Transformers Advance Multi-modal Medical Image Classification—0
Cut-Thumbnail: A Novel Data Augmentation for Convolutional Neural NetworkCode0
Improving Global Adversarial Robustness Generalization With Adversarially Trained GAN—0
A Retrospective Approximation Approach for Smooth Stochastic Optimization—0
Efficient Model Performance Estimation via Feature Histories—0
End-to-end optimized image compression for multiple machine tasks—0
LongReMix: Robust Learning with High Confidence Samples in a Noisy Label EnvironmentCode0
Selective Replay Enhances Learning in Online Continual Analogical ReasoningCode0
Contextual Dropout: An Efficient Sample-Dependent Dropout ModuleCode0
SCRIB: Set-classifier with Class-specific Risk Bounds for Blackbox Models—0
Unified Robust Training for Graph NeuralNetworks against Label Noise—0
Don't Forget to Sign the Gradients!Code0
Measuring Model Biases in the Absence of Ground Truth—0
QAIR: Practical Query-efficient Black-Box Attacks for Image Retrieval—0
Unsupervised Domain Adaptation for Image Classification via Structure-Conditioned Adversarial Learning—0
Redundant Information Neural Estimation—0
Hard-label Manifolds: Unexpected Advantages of Query Efficiency for Finding On-manifold Adversarial Examples—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