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 8376–8400 of 10420 papers

TitleStatusHype
Metric-Based Few-Shot Learning for Video Action Recognition—0
A Hybrid Architecture for On-Device Compressive Machine Learning—0
Semantic and Visual Similarities for Efficient Knowledge Transfer in CNN Training—0
SegNAS3D: Network Architecture Search with Derivative-Free Global Optimization for 3D Image Segmentation—0
An Empirical Investigation of Randomized Defenses against Adversarial AttacksCode0
Effective training of deep convolutional neural networks for hyperspectral image classification through artificial labeling—0
Differentially Private Meta-Learning—0
Modular Meta-Learning with Shrinkage—0
Deep Elastic Networks with Model Selection for Multi-Task Learning—0
Spatial Graph Convolutional NetworksCode0
HHHFL: Hierarchical Heterogeneous Horizontal Federated Learning for ElectroencephalographyCode0
Learning to predict visual brain activity by predicting future sensory states—0
ScieNet: Deep Learning with Spike-assisted Contextual Information Extraction—0
FDA: Feature Disruptive AttackCode0
Localized Adversarial Training for Increased Accuracy and Robustness in Image Classification—0
DaTscan SPECT Image Classification for Parkinson's Disease—0
Neural Architecture Search in Embedding Space—0
DeepObfuscator: Obfuscating Intermediate Representations with Privacy-Preserving Adversarial Learning on Smartphones—0
Learning to Sample: an Active Learning Framework—0
Understanding the Effects of Pre-Training for Object Detectors via Eigenspectrum—0
L_DMI: An Information-theoretic Noise-robust Loss FunctionCode0
Exploring Temporal Differences in 3D Convolutional Neural Networks—0
Linear Context Transform Block—0
A Baseline for Few-Shot Image ClassificationCode0
Testing Deep Learning Models for Image Analysis Using Object-Relevant Metamorphic Relations—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
5DaViT-HTop 1 Accuracy90.2—Unverified
6Meta Pseudo Labels (EfficientNet-L2)Top 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