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

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Showing 7251–7300 of 10420 papers

TitleStatusHype
Performing Image Classification for 10 Different Monkey Species using CNN—0
Image Classification by Reinforcement Learning with Two-State Q-LearningCode1
Compositional Convolutional Neural Networks: A Robust and Interpretable Model for Object Recognition under Occlusion—0
Frequency learning for image classification—0
An Evoked Potential-Guided Deep Learning Brain Representation For Visual Classification—0
ReMarNet: Conjoint Relation and Margin Learning for Small-Sample Image ClassificationCode0
Unsupervised Deep Representation Learning and Few-Shot Classification of PolSAR Images—0
A Comparative Analysis on Bangla Handwritten Digit Recognition with Data Augmentation and Non-Augmentation ProcessCode0
​4S-DT: Self Supervised Super Sample Decomposition for Transfer learning with application to COVID-19 detection—0
4S-DT: Self Supervised Super Sample Decomposition for Transfer learning with application to COVID-19 detection—0
ULSAM: Ultra-Lightweight Subspace Attention Module for Compact Convolutional Neural NetworksCode1
End-to-end training of deep kernel map networks for image classification—0
Not all Failure Modes are Created Equal: Training Deep Neural Networks for Explicable (Mis)Classification—0
E2GC: Energy-efficient Group Convolution in Deep Neural NetworksCode0
Diverse Knowledge Distillation (DKD): A Solution for Improving The Robustness of Ensemble Models Against Adversarial Attacks—0
Deep Prototypical Networks with Hybrid Residual Attention for Hyperspectral Image ClassificationCode1
Target Consistency for Domain Adaptation: when Robustness meets Transferability—0
Set Based Stochastic Subsampling—0
Learning Data Augmentation with Online Bilevel Optimization for Image ClassificationCode1
Compositional Explanations of NeuronsCode1
Blacklight: Scalable Defense for Neural Networks against Query-Based Black-Box AttacksCode1
Learning Semantically Enhanced Feature for Fine-Grained Image ClassificationCode1
Learning Interclass Relations for Image Classification—0
Time for a Background Check! Uncovering the impact of Background Features on Deep Neural Networks—0
Normalized Loss Functions for Deep Learning with Noisy LabelsCode1
Ramanujan Bipartite Graph Products for Efficient Block Sparse Neural Networks—0
When Do Neural Networks Outperform Kernel Methods?Code0
Interpretable Deep Models for Cardiac Resynchronisation Therapy Response Prediction—0
Hyperparameter Ensembles for Robustness and Uncertainty QuantificationCode0
Calibration of Neural Networks using SplinesCode1
Post-hoc Calibration of Neural Networks by g-Layers—0
Don’t Wait, Just Weight: Improving Unsupervised Representations by Learning Goal-Driven Instance Weights—0
Auxiliary Learning by Implicit Differentiation—0
RP2K: A Large-Scale Retail Product Dataset for Fine-Grained Image Classification—0
Effective Version Space Reduction for Convolutional Neural Networks—0
DO-Conv: Depthwise Over-parameterized Convolutional LayerCode1
On Creating Benchmark Dataset for Aerial Image Interpretation: Reviews, Guidances and Million-AIDCode1
The color out of space: learning self-supervised representations for Earth Observation imageryCode1
Self-Knowledge Distillation with Progressive Refinement of TargetsCode1
A Universal Representation Transformer Layer for Few-Shot Image ClassificationCode1
FNA++: Fast Network Adaptation via Parameter Remapping and Architecture SearchCode1
MaxVA: Fast Adaptation of Step Sizes by Maximizing Observed Variance of GradientsCode0
Gradient-EM Bayesian Meta-learning—0
A Bayesian Evaluation Framework for Subjectively Annotated Visual Recognition TasksCode0
Adversarial Transfer of Pose Estimation Regression—0
Unsupervised Image Classification for Deep Representation LearningCode0
Deep Polynomial Neural NetworksCode1
Pyramidal Convolution: Rethinking Convolutional Neural Networks for Visual RecognitionCode1
Paying more attention to snapshots of Iterative Pruning: Improving Model Compression via Ensemble DistillationCode1
AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning—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