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 3901–3925 of 10420 papers

TitleStatusHype
Image Classification using Sequence of Pixels—0
Capsule Network based Contrastive Learning of Unsupervised Visual RepresentationsCode1
Robust Collaborative Learning with Linear Gradient OverheadCode0
Mega: Moving Average Equipped Gated AttentionCode2
I2DFormer: Learning Image to Document Attention for Zero-Shot Image Classification—0
HiFuse: Hierarchical Multi-Scale Feature Fusion Network for Medical Image ClassificationCode2
PicT: A Slim Weakly Supervised Vision Transformer for Pavement Distress ClassificationCode0
Dynamic Graph Message Passing Networks for Visual RecognitionCode1
Fine-grained Classification of Solder Joints with α-skew Jensen-Shannon Divergence—0
Frequency Dropout: Feature-Level Regularization via Randomized Filtering—0
Relaxed Attention for Transformer Models—0
CoV-TI-Net: Transferred Initialization with Modified End Layer for COVID-19 DiagnosisCode1
On the Shift Invariance of Max Pooling Feature Maps in Convolutional Neural Networks—0
S^3R: Self-supervised Spectral Regression for Hyperspectral Histopathology Image Classification—0
On the Adversarial Transferability of ConvMixer Models—0
Semantic Segmentation using Neural Ordinary Differential Equations—0
Deep tensor networks with matrix product operators—0
Enhance the Visual Representation via Discrete Adversarial TrainingCode0
Towards Bridging the Performance Gaps of Joint Energy-based ModelsCode0
A Mosquito is Worth 16x16 Larvae: Evaluation of Deep Learning Architectures for Mosquito Larvae ClassificationCode0
Top-Tuning: a study on transfer learning for an efficient alternative to fine tuning for image classification with fast kernel methods—0
Continual Learning with Dependency Preserving Hypernetworks—0
Continual Learning for Class- and Domain-Incremental Semantic Segmentation—0
Confidence-Guided Data Augmentation for Improved Semi-Supervised Training—0
A Continual Development Methodology for Large-scale Multitask Dynamic ML SystemsCode0
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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