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

Showing 39764000 of 10420 papers

TitleStatusHype
iCAR: Bridging Image Classification and Image-text Alignment for Visual RecognitionCode0
I-CEE: Tailoring Explanations of Image Classification Models to User ExpertiseCode0
Fast Solar Image Classification Using Deep Learning and its Importance for Automation in Solar PhysicsCode0
Curriculum Meta-Learning for Few-shot ClassificationCode0
AdaPlus: Integrating Nesterov Momentum and Precise Stepsize Adjustment on AdamW BasisCode0
Curriculum DropoutCode0
I Bet You Did Not Mean That: Testing Semantic Importance via BettingCode0
iCLIP: Bridging Image Classification and Contrastive Language-Image Pre-Training for Visual RecognitionCode0
Tuned Compositional Feature Replays for Efficient Stream LearningCode0
Hyperspectral Image Classification With Contrastive Graph Convolutional NetworkCode0
A Kernelized Manifold Mapping to Diminish the Effect of Adversarial PerturbationsCode0
HyperZZW Operator Connects Slow-Fast Networks for Full Context InteractionCode0
FCA: Taming Long-tailed Federated Medical Image Classification by Classifier AnchoringCode0
Hysteresis Activation Function for Efficient InferenceCode0
A Twofold Siamese Network for Real-Time Object TrackingCode0
CTRL-F: Pairing Convolution with Transformer for Image Classification via Multi-Level Feature Cross-Attention and Representation Learning FusionCode0
Hyperspectral Image Classification with Markov Random Fields and a Convolutional Neural NetworkCode0
Comparing the Efficacy of Fine-Tuning and Meta-Learning for Few-Shot Policy ImitationCode0
Feasible LearningCode0
Arithmetic addition of two integers by deep image classification networks: experiments to quantify their autonomous reasoning abilityCode0
Structural Pruning via Spatial-aware Information Redundancy for Semantic SegmentationCode0
CTARR: A fast and robust method for identifying anatomical regions on CT images via atlas registrationCode0
Structured Analysis Dictionary Learning for Image ClassificationCode0
Ajwa or Medjool: a binary balanced dataset to teach machine learning عجوة أو مجدول: مجموعة بيانات متوازنة الصنفين لتدريس تعلم الآلة‏Code0
A Tunable Robust Pruning Framework Through Dynamic Network Rewiring of DNNsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CoCa (finetuned)Top 1 Accuracy91Unverified
2Model soups (BASIC-L)Top 1 Accuracy90.98Unverified
3Model soups (ViT-G/14)Top 1 Accuracy90.94Unverified
4DaViT-GTop 1 Accuracy90.4Unverified
5Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2Unverified
6DaViT-HTop 1 Accuracy90.2Unverified
7SwinV2-GTop 1 Accuracy90.17Unverified
8MAWS (ViT-6.5B)Top 1 Accuracy90.1Unverified
9Florence-CoSwin-HTop 1 Accuracy90.05Unverified
10RevCol-HTop 1 Accuracy90Unverified