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 2101–2125 of 10420 papers

TitleStatusHype
Graph Convolutions Enrich the Self-Attention in Transformers!Code1
On the Robustness of Large Multimodal Models Against Image Adversarial Attacks—0
Foundation Model Assisted Weakly Supervised Semantic SegmentationCode1
Riemannian Complex Matrix Convolution Network for PolSAR Image Classification—0
Unsupervised learning on spontaneous retinal activity leads to efficient neural representation geometry—0
Diversified in-domain synthesis with efficient fine-tuning for few-shot classificationCode1
Classification for everyone : Building geography agnostic models for fairer recognition—0
GDN: A Stacking Network Used for Skin Cancer DiagnosisCode0
Federated Active Learning for Target Domain GeneralisationCode0
CLAMP: Contrastive LAnguage Model Prompt-tuning—0
A Comprehensive Literature Review on Sweet Orange Leaf Diseases—0
MABViT -- Modified Attention Block Enhances Vision Transformers—0
Visual Prompting Upgrades Neural Network Sparsification: A Data-Model PerspectiveCode1
TranSegPGD: Improving Transferability of Adversarial Examples on Semantic Segmentation—0
SASSL: Enhancing Self-Supervised Learning via Neural Style Transfer—0
Rethinking Multiple Instance Learning for Whole Slide Image Classification: A Bag-Level Classifier is a Good Instance-Level TeacherCode1
Acoustic Signal Analysis with Deep Neural Network for Detecting Fault Diagnosis in Industrial Machines—0
A Comprehensive Study of Vision Transformers in Image Classification Tasks—0
Physics Inspired Criterion for Pruning-Quantization Joint LearningCode0
EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment AnythingCode4
SCHEME: Scalable Channel Mixer for Vision Transformers—0
Improving Normalization with the James-Stein Estimator—0
Developmental Pretraining (DPT) for Image Classification NetworksCode0
Benchmarking Multi-Domain Active Learning on Image Classification—0
LightCLIP: Learning Multi-Level Interaction for Lightweight Vision-Language Models—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