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 10011025 of 10420 papers

TitleStatusHype
Curriculum Temperature for Knowledge DistillationCode1
Robust Learning Meets Generative Models: Can Proxy Distributions Improve Adversarial Robustness?Code1
A Robust Feature Downsampling Module for Remote Sensing Visual TasksCode1
CvT: Introducing Convolutions to Vision TransformersCode1
CycleMLP: A MLP-like Architecture for Dense PredictionCode1
Inversion Circle Interpolation: Diffusion-based Image Augmentation for Data-scarce ClassificationCode1
Disentangling Label Distribution for Long-tailed Visual RecognitionCode1
DAF:re: A Challenging, Crowd-Sourced, Large-Scale, Long-Tailed Dataset For Anime Character RecognitionCode1
Counterfactual Explanations for Medical Image Classification and Regression using Diffusion AutoencoderCode1
Distilling Out-of-Distribution Robustness from Vision-Language Foundation ModelsCode1
ACPL: Anti-curriculum Pseudo-labelling for Semi-supervised Medical Image ClassificationCode1
DAM: Dynamic Adapter Merging for Continual Video QA LearningCode1
Danish Fungi 2020 -- Not Just Another Image Recognition DatasetCode1
DarkneTZ: Towards Model Privacy at the Edge using Trusted Execution EnvironmentsCode1
data2vec: A General Framework for Self-supervised Learning in Speech, Vision and LanguageCode1
Diversify and Disambiguate: Learning From Underspecified DataCode1
Data-Efficient Deep Learning Method for Image Classification Using Data Augmentation, Focal Cosine Loss, and EnsembleCode1
Data Augmentation with norm-VAE for Unsupervised Domain AdaptationCode1
A Second-Order Approach to Learning with Instance-Dependent Label NoiseCode1
BAGAN: Data Augmentation with Balancing GANCode1
DATA: Domain-Aware and Task-Aware Self-supervised LearningCode1
DISCO: Adversarial Defense with Local Implicit FunctionsCode1
Discretization-Aware Architecture SearchCode1
BadHash: Invisible Backdoor Attacks against Deep Hashing with Clean LabelCode1
DISC: Learning From Noisy Labels via Dynamic Instance-Specific Selection and CorrectionCode1
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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