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 3601–3625 of 10420 papers

TitleStatusHype
Bi-directional Feature Reconstruction Network for Fine-Grained Few-Shot Image ClassificationCode1
Optimizing Explanations by Network Canonization and Hyperparameter Search—0
Nonlinear Advantage: Trained Networks Might Not Be As Complex as You Think—0
AIO-P: Expanding Neural Performance Predictors Beyond Image ClassificationCode1
Exploiting Category Names for Few-Shot Classification with Vision-Language Models—0
AdvMask: A Sparse Adversarial Attack Based Data Augmentation Method for Image Classification—0
SimCS: Simulation for Domain Incremental Online Continual Segmentation—0
Impact of Automatic Image Classification and Blind Deconvolution in Improving Text Detection Performance of the CRAFT Algorithm—0
Curriculum Temperature for Knowledge DistillationCode1
Entropy-Driven Mixed-Precision Quantization for Deep Network Design—0
SgVA-CLIP: Semantic-guided Visual Adapting of Vision-Language Models for Few-shot Image ClassificationCode0
Establishment of Neural Networks Robust to Label Noise—0
Forged Image Detection using SOTA Image Classification Deep Learning Methods for Image Forensics with Error Level Analysis—0
Class Adaptive Network CalibrationCode1
Explaining Deep Convolutional Neural Networks for Image Classification by Evolving Local Interpretable Model-agnostic Explanations—0
Learning to Learn: How to Continuously Teach Humans and Machines—0
A Call to Reflect on Evaluation Practices for Failure Detection in Image ClassificationCode1
RankDNN: Learning to Rank for Few-shot LearningCode1
SI-GAT: A method based on improved Graph Attention Network for sonar image classification—0
Context-Adaptive Deep Neural Networks via Bridge-Mode Connectivity—0
Semantic-Aware Local-Global Vision Transformer—0
A Particle-based Sparse Gaussian Process Optimizer—0
Looking at the posterior: accuracy and uncertainty of neural-network predictions—0
CLIP-ReID: Exploiting Vision-Language Model for Image Re-Identification without Concrete Text LabelsCode2
Cross-Domain Ensemble Distillation for Domain GeneralizationCode1
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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