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 7676–7700 of 10420 papers

TitleStatusHype
Incremental Learning of NCM Forests for Large-Scale Image Classification—0
Putting GPT-4o to the Sword: A Comprehensive Evaluation of Language, Vision, Speech, and Multimodal Proficiency—0
Incremental Learning In Online Scenario—0
Deep Generative Modeling for Protein Design—0
A Weakly Supervised Fine Label Classifier Enhanced by Coarse Supervision—0
AMD: Automatic Multi-step Distillation of Large-scale Vision Models—0
Incremental Learning in Deep Convolutional Neural Networks Using Partial Network Sharing—0
Incremental Few-Shot Learning via Implanting and Compressing—0
Increasing Trustworthiness of Deep Neural Networks via Accuracy Monitoring—0
Pyramidal RoR for Image Classification—0
PyramidCLIP: Hierarchical Feature Alignment for Vision-language Model Pretraining—0
Increasing the Robustness of Semantic Segmentation Models with Painting-by-Numbers—0
Deep Gaussian Processes with Convolutional Kernels—0
Increasing the Inference and Learning Speed of Tsetlin Machines with Clause Indexing—0
Increasing Shape Bias in ImageNet-Trained Networks Using Transfer Learning and Domain-Adversarial Methods—0
Increasing Model Generalizability for Unsupervised Domain Adaptation—0
Avoiding Overfitting: A Survey on Regularization Methods for Convolutional Neural Networks—0
Incorporating Semantic Attention in Video Description Generation—0
Deep Fisher Networks for Large-Scale Image Classification—0
Deep FisherNet for Object Classification—0
Q-GADMM: Quantized Group ADMM for Communication Efficient Decentralized Machine Learning—0
Incoporating Weighted Board Learning System for Accurate Occupational Pneumoconiosis Staging—0
In-Context Learning for Label-Efficient Cancer Image Classification in Oncology—0
Deep Features for training Support Vector Machine—0
In-context learning enables multimodal large language models to classify cancer pathology images—0
Show:102550
← PrevPage 308 of 417Next →
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