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 7701–7725 of 10420 papers

TitleStatusHype
Avoiding Overfitting: A Survey on Regularization Methods for Convolutional Neural Networks—0
Incorporating Semantic Attention in Video Description Generation—0
Deep Fisher Networks for Large-Scale Image Classification—0
Deep FisherNet for Object Classification—0
Incoporating Weighted Board Learning System for Accurate Occupational Pneumoconiosis Staging—0
In-Context Learning for Label-Efficient Cancer Image Classification in Oncology—0
Deep Features for training Support Vector Machine—0
Quantifying Task Complexity Through Generalized Information Measures—0
Quantifying task-relevant representational similarity using decision variable correlation—0
Quantifying the Knowledge in a DNN to Explain Knowledge Distillation for Classification—0
In-context learning enables multimodal large language models to classify cancer pathology images—0
Incomplete Dot Products for Dynamic Computation Scaling in Neural Network Inference—0
Deep Features Analysis with Attention Networks—0
A Vision-based Solution for Track Misalignment Detection—0
A Mathematical Framework for Quantifying Transferability in Multi-source Transfer Learning—0
Adaptive Deep Pyramid Matching for Remote Sensing Scene Classification—0
Quantization of Deep Neural Networks for Accumulator-constrained Processors—0
Quantized and Interpretable Learning Scheme for Deep Neural Networks in Classification Task—0
Deep Feature Augmentation for Occluded Image Classification—0
Quantized Feature Distillation for Network Quantization—0
A Very Compact Embedded CNN Processor Design Based on Logarithmic Computing—0
InceptionCapsule: Inception-Resnet and CapsuleNet with self-attention for medical image Classification—0
In Automation We Trust: Investigating the Role of Uncertainty in Active Learning Systems—0
A Main/Subsidiary Network Framework for Simplifying Binary Neural Networks—0
IMWA: Iterative Model Weight Averaging Benefits Class-Imbalanced Learning Tasks—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