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 5676–5700 of 10420 papers

TitleStatusHype
A New Compensatory Genetic Algorithm-Based Method for Effective Compressed Multi-function Convolutional Neural Network Model Selection with Multi-Objective Optimization—0
Does Visual Pretraining Help End-to-End Reasoning?—0
Does Saliency-Based Training bring Robustness for Deep Neural Networks in Image Classification?—0
Broad Adversarial Training with Data Augmentation in the Output Space—0
A New Clustering-Based Technique for the Acceleration of Deep Convolutional Networks—0
A Compact DNN: Approaching GoogLeNet-Level Accuracy of Classification and Domain Adaptation—0
Does Robustness on ImageNet Transfer to Downstream Tasks?—0
Does Normalization Methods Play a Role for Hyperspectral Image Classification?—0
Does Haze Removal Help CNN-based Image Classification?—0
Does Distributionally Robust Supervised Learning Give Robust Classifiers?—0
Just Noticeable Difference for Deep Machine Vision—0
Bridging the Gap between Spatial and Spectral Domains: A Survey on Graph Neural Networks—0
Does deep learning model calibration improve performance in class-imbalanced medical image classification?—0
Does Data Augmentation Benefit from Split BatchNorms—0
Increasing Depth Leads to U-Shaped Test Risk in Over-parameterized Convolutional Networks—0
Bridging Classical and Quantum Machine Learning: Knowledge Transfer From Classical to Quantum Neural Networks Using Knowledge Distillation—0
DocXplain: A Novel Model-Agnostic Explainability Method for Document Image Classification—0
Bridge the Modality and Capability Gaps in Vision-Language Model Selection—0
An Evoked Potential-Guided Deep Learning Brain Representation For Visual Classification—0
ADFQ-ViT: Activation-Distribution-Friendly Post-Training Quantization for Vision Transformers—0
A Committee of Convolutional Neural Networks for Image Classication in the Concurrent Presence of Feature and Label Noise—0
Document image classification, with a specific view on applications of patent images—0
Document AI: Benchmarks, Models and Applications—0
Do Convolutional Neural Networks Learn Class Hierarchy?—0
Breast Ultrasound Tumor Classification Using a Hybrid Multitask CNN-Transformer Network—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
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
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90—Unverified