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 551–575 of 10419 papers

TitleStatusHype
HistoFS: Non-IID Histopathologic Whole Slide Image Classification via Federated Style Transfer with RoI-Preserving—0
MExD: An Expert-Infused Diffusion Model for Whole-Slide Image Classification—0
Star with Bilinear Mapping—0
DefMamba: Deformable Visual State Space Model—0
Ensuring superior learning outcomes and data security for authorized learner—0
A Novel Approach using CapsNet and Deep Belief Network for Detection and Identification of Oral Leukopenia—0
ACIL: Active Class Incremental Learning for Image Classification—0
Uncertainty-Aware Out-of-Distribution Detection with Gaussian Processes—0
FPGA-based Acceleration of Neural Network for Image Classification using Vitis AI—0
Hilbert Curve Based Molecular Sequence Analysis—0
Deep Learning in Image Classification: Evaluating VGG19's Performance on Complex Visual Data—0
Few-shot Algorithm Assurance—0
Cross-Modal Mapping: Mitigating the Modality Gap for Few-Shot Image Classification—0
A Robust Adversarial Ensemble with Causal (Feature Interaction) Interpretations for Image Classification—0
Enhancing Transfer Learning for Medical Image Classification with SMOTE: A Comparative Study—0
On dataset transferability in medical image classificationCode0
Image Classification with Deep Reinforcement Active Learning—0
Enhancing Fine-grained Image Classification through Attentive Batch Training—0
Data-Free Group-Wise Fully Quantized Winograd Convolution via Learnable Scales—0
Multi-label Classification using Deep Multi-order Context-aware Kernel Networks—0
An In-Depth Analysis of Adversarial Discriminative Domain Adaptation for Digit ClassificationCode0
Asymmetrical Reciprocity-based Federated Learning for Resolving Disparities in Medical DiagnosisCode0
Residual Feature-Reutilization Inception Network for Image Classification—0
Assessing Pre-trained Models for Transfer Learning through Distribution of Spectral Components—0
Adversarial Training for Graph Neural Networks via Graph Subspace Energy Optimization—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