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 2826–2850 of 10420 papers

TitleStatusHype
Improving Visual Prompt Tuning for Self-supervised Vision TransformersCode1
Revising deep learning methods in parking lot occupancy detectionCode1
T-ADAF: Adaptive Data Augmentation Framework for Image Classification Network based on Tensor T-product Operator—0
GeoDiffusion: Text-Prompted Geometric Control for Object Detection Data Generation—0
Towards Label-free Scene Understanding by Vision Foundation ModelsCode1
Quick-Tune: Quickly Learning Which Pretrained Model to Finetune and How—0
Quantitative Analysis of Primary Attribution Explainable Artificial Intelligence Methods for Remote Sensing Image ClassificationCode0
Human-imperceptible, Machine-recognizable ImagesCode0
Semantically-Prompted Language Models Improve Visual Descriptions—0
Input-gradient space particle inference for neural network ensemblesCode0
Continual Learning with Pretrained Backbones by Tuning in the Input Space—0
Resilient Constrained Learning—0
Towards Robust Feature Learning with t-vFM Similarity for Continual Learning—0
Memorization Capacity of Multi-Head Attention in TransformersCode0
Content-aware Token Sharing for Efficient Semantic Segmentation with Vision TransformersCode1
Enhancing CLIP with CLIP: Exploring Pseudolabeling for Limited-Label Prompt TuningCode1
Concurrent Classifier Error Detection (CCED) in Large Scale Machine Learning Systems—0
Break a Lag: Triple Exponential Moving Average for Enhanced Optimization—0
A Novel Vision Transformer with Residual in Self-attention for Biomedical Image Classification—0
Scaling Up Semi-supervised Learning with Unconstrained Unlabelled DataCode0
A Data-Driven Measure of Relative Uncertainty for Misclassification DetectionCode0
Evaluating The Robustness of Self-Supervised Representations to Background/Foreground Removal—0
Long-Range Feedback Spiking Network Captures Dynamic and Static Representations of the Visual Cortex under Movie StimuliCode0
Exploring Robustness of Image Recognition Models on Hardware AcceleratorsCode0
Transformer-based Multi-Modal Learning for Multi Label Remote Sensing Image Classification—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
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