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 3076–3100 of 10420 papers

TitleStatusHype
Discriminative Distillation to Reduce Class Confusion in Continual Learning—0
An empirical study of domain-agnostic semi-supervised learning via energy-based models: joint-training and pre-training—0
An Empirical Study of Adder Neural Networks for Object Detection—0
ACLS: Adaptive and Conditional Label Smoothing for Network Calibration—0
Evolved Explainable Classifications for Lymph Node Metastases—0
Boosting Mapping Functionality of Neural Networks via Latent Feature Generation based on Reversible Learning—0
Disentangling Visual Transformers: Patch-level Interpretability for Image Classification—0
Disguised-Nets: Image Disguising for Privacy-preserving Outsourced Deep Learning—0
Boosting Medical Image Classification with Segmentation Foundation Model—0
Discriminative and Geometry Aware Unsupervised Domain Adaptation—0
Disrupting Model Training with Adversarial Shortcuts—0
Dissonance Between Human and Machine Understanding—0
Boosting Network Weight Separability via Feed-Backward Reconstruction—0
Distance-based Composable Representations with Neural Networks—0
An empirical study of the relation between network architecture and complexity—0
Adaptive Gradient Clipping for Robust Federated Learning—0
Distangling Biological Noise in Cellular Images with a focus on Explainability—0
An Empirical Investigation into Benchmarking Model Multiplicity for Trustworthy Machine Learning: A Case Study on Image Classification—0
DistilDoc: Knowledge Distillation for Visually-Rich Document Applications—0
Evolution of Activation Functions for Deep Learning-Based Image Classification—0
Distillation from heterogeneous unlabeled collections—0
Self-Attention Capsule Networks for Object Classification—0
A Deep Bayesian Convolutional Spiking Neural Network-based CAD system with Uncertainty Quantification for Medical Images Classification—0
Evolution of active categorical image classification via saccadic eye movement—0
Evolved Part Masking for Self-Supervised Learning—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