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 6451–6500 of 10420 papers

TitleStatusHype
Robustness of on-device Models: Adversarial Attack to Deep Learning Models on Android AppsCode1
Mixup Without HesitationCode1
3D-ANAS: 3D Asymmetric Neural Architecture Search for Fast Hyperspectral Image ClassificationCode0
Explaining the Black-box Smoothly- A Counterfactual Approach—0
RepVGG: Making VGG-style ConvNets Great AgainCode2
Lesion2Vec: Deep Metric Learning for Few-Shot Multiple Lesions Recognition in Wireless Capsule Endoscopy Video—0
Spherical Transformer: Adapting Spherical Signal to CNNs—0
Resolution-Based Distillation for Efficient Histology Image Classification—0
Quantum Tensor Network in Machine Learning: An Application to Tiny Object ClassificationCode1
Combining pretrained CNN feature extractors to enhance clustering of complex natural images—0
Who's a Good Boy? Reinforcing Canine Behavior in Real-Time using Machine LearningCode0
Practical Evaluation of Out-of-Distribution Detection Methods for Image Classification—0
Contextual Classification Using Self-Supervised Auxiliary Models for Deep Neural NetworksCode0
Self Supervision for Attention NetworksCode0
LightLayers: Parameter Efficient Dense and Convolutional Layers for Image ClassificationCode0
AutoDropout: Learning Dropout Patterns to Regularize Deep Networks—0
Fast Ensemble Learning Using Adversarially-Generated Restricted Boltzmann MachinesCode0
Recommending Accurate and Diverse Items Using Bilateral Branch Network—0
Transformers in Vision: A Survey—0
A Framework for Fast Scalable BNN Inference using Googlenet and Transfer Learning—0
RegNet: Self-Regulated Network for Image ClassificationCode4
Few-shot Image Classification: Just Use a Library of Pre-trained Feature Extractors and a Simple ClassifierCode1
Uncertainty-sensitive Activity Recognition: a Reliability Benchmark and the CARING Models—0
A multi-modal approach towards mining social media data during natural disasters -- a case study of Hurricane Irma—0
Co2L: Contrastive Continual LearningCode1
Domain-Invariant Disentangled Network for Generalizable Object Detection—0
Distilling Global and Local Logits With Densely Connected RelationsCode0
Student Customized Knowledge Distillation: Bridging the Gap Between Student and Teacher—0
Kernel Methods in Hyperbolic Spaces—0
Synthesized Feature Based Few-Shot Class-Incremental Learning on a Mixture of Subspaces—0
P-Swish: Activation Function with Learnable Parameters Based on Swish Activation Function in Deep Learning—0
Energy-constrained Self-training for Unsupervised Domain Adaptation—0
Iranis: A Large-scale Dataset of Farsi License Plate CharactersCode1
Active Learning Under Malicious Mislabeling and Poisoning Attacks—0
Generative Max-Mahalanobis Classifiers for Image Classification, Generation and MoreCode0
AFINets: Attentive Feature Integration Networks for Image Classification—0
Exploring Target Driven Image Classification—0
Protecting DNNs from Theft using an Ensemble of Diverse Models—0
USING OBJECT-FOCUSED IMAGES AS AN IMAGE AUGMENTATION TECHNIQUE TO IMPROVE THE ACCURACY OF IMAGE-CLASSIFICATION MODELS WHEN VERY LIMITED DATA SETS ARE AVAILABLE—0
Detection Booster Training: A detection booster training method for improving the accuracy of classifiers.—0
Learning the Connections in Direct Feedback Alignment—0
Improve Object Detection with Feature-based Knowledge Distillation: Towards Accurate and Efficient DetectorsCode1
General Adversarial Defense via Pixel Level and Feature Level Distribution Alignment—0
Policy-Driven Attack: Learning to Query for Hard-label Black-box Adversarial Examples—0
Toward Understanding Supervised Representation Learning with RKHS and GAN—0
Explicit Connection Distillation—0
CNN Based Analysis of the Luria’s Alternating Series Test for Parkinson’s Disease Diagnostics—0
Unsupervised Domain Adaptation via Minimized Joint Error—0
Category Disentangled Context: Turning Category-irrelevant Features Into Treasures—0
Towards Practical Second Order Optimization for Deep 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