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 3826–3850 of 10420 papers

TitleStatusHype
The Equalization Losses: Gradient-Driven Training for Long-tailed Object RecognitionCode2
Understanding the Failure of Batch Normalization for Transformers in NLPCode1
OPERA: Omni-Supervised Representation Learning with Hierarchical SupervisionsCode1
Energy-Efficient Deployment of Machine Learning Workloads on Neuromorphic Hardware—0
Continual Learning with Evolving Class Ontologies—0
Multi-Modal Fusion by Meta-InitializationCode0
Universal Adversarial Perturbations: Efficiency on a small image datasetCode0
Are All Vision Models Created Equal? A Study of the Open-Loop to Closed-Loop Causality Gap—0
A Differentiable Distance Approximation for Fairer Image ClassificationCode0
Online Training Through Time for Spiking Neural NetworksCode1
Unsupervised RGB-to-Thermal Domain Adaptation via Multi-Domain Attention NetworkCode1
Improving Data-Efficient Fossil Segmentation via Model Editing—0
Fast-ParC: Capturing Position Aware Global Feature for ConvNets and ViTs—0
SVL-Adapter: Self-Supervised Adapter for Vision-Language Pretrained ModelsCode1
Bi-directional Weakly Supervised Knowledge Distillation for Whole Slide Image ClassificationCode1
Deep Learning Mixture-of-Experts Approach for Cytotoxic Edema Assessment in Infants and Children—0
Gastrointestinal Disorder Detection with a Transformer Based Approach—0
Synthetic Dataset Generation for Privacy-Preserving Machine Learning—0
Topological Continual Learning with Wasserstein Distance and Barycenter—0
CLAD: A Contrastive Learning based Approach for Background DebiasingCode0
Misaligned orientations of 4f optical neural network for image classification accuracy on various datasets—0
ImpressLearn: Continual Learning via Combined Task Impressions—0
Why Random Pruning Is All We Need to Start SparseCode0
Multi-stream Fusion for Class Incremental Learning in Pill Image ClassificationCode0
Robustness Certification of Visual Perception Models via Camera Motion SmoothingCode1
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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