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 601625 of 10419 papers

TitleStatusHype
MCUFormer: Deploying Vision Transformers on Microcontrollers with Limited MemoryCode1
Interpretable Medical Image Classification using Prototype Learning and Privileged InformationCode1
Vision-Language Pseudo-Labels for Single-Positive Multi-Label LearningCode1
SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and GenerationCode1
Multi‑camera trajectory matching based on hierarchical clustering and constraintsCode1
Image Clustering with External GuidanceCode1
RefConv: Re-parameterized Refocusing Convolution for Powerful ConvNetsCode1
Real-Fake: Effective Training Data Synthesis Through Distribution MatchingCode1
PaLI-3 Vision Language Models: Smaller, Faster, StrongerCode1
AutoVP: An Automated Visual Prompting Framework and BenchmarkCode1
Leveraging Vision-Language Models for Improving Domain Generalization in Image ClassificationCode1
EViT: An Eagle Vision Transformer with Bi-Fovea Self-AttentionCode1
Efficient Adaptation of Large Vision Transformer via Adapter Re-ComposingCode1
Transformer Fusion with Optimal TransportCode1
Progressive Neural Compression for Adaptive Image Offloading under Timing ConstraintsCode1
Why Do We Need Weight Decay in Modern Deep Learning?Code1
TiC: Exploring Vision Transformer in ConvolutionCode1
ViT-ReciproCAM: Gradient and Attention-Free Visual Explanations for Vision TransformerCode1
SemiReward: A General Reward Model for Semi-supervised LearningCode1
Extending CAM-based XAI methods for Remote Sensing Imagery SegmentationCode1
Trainable Noise Model as an XAI evaluation method: application on Sobol for remote sensing image segmentationCode1
A Framework for Inference Inspired by Human Memory MechanismsCode1
Enhancing Sharpness-Aware Optimization Through Variance SuppressionCode1
ClusterFormer: Clustering As A Universal Visual LearnerCode1
MUSTANG: Multi-Stain Self-Attention Graph Multiple Instance Learning Pipeline for Histopathology Whole Slide ImagesCode1
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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