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 301–325 of 10419 papers

TitleStatusHype
PARIC: Probabilistic Attention Regularization for Language Guided Image Classification from Pre-trained Vison Language Models—0
Open-Set Plankton Recognition—0
Leveraging Vision-Language Embeddings for Zero-Shot Learning in Histopathology Images—0
Do We Always Need the Simplicity Bias? Looking for Optimal Inductive Biases in the Wild—0
Learning Interpretable Logic Rules from Deep Vision Models—0
A Multi-Modal Federated Learning Framework for Remote Sensing Image Classification—0
Interpretable Image Classification via Non-parametric Part Prototype LearningCode1
Multiplicative Learning—0
(, δ) Considered Harmful: Best Practices for Reporting Differential Privacy GuaranteesCode0
Extreme Learning Machines for Attention-based Multiple Instance Learning in Whole-Slide Image Classification—0
Discovering Influential Neuron Path in Vision Transformers—0
Bayesian Test-Time Adaptation for Vision-Language Models—0
ForAug: Recombining Foregrounds and Backgrounds to Improve Vision Transformer Training with Bias MitigationCode0
Double-Stage Feature-Level Clustering-Based Mixture of Experts Framework—0
Deep Learning for Climate Action: Computer Vision Analysis of Visual Narratives on X—0
Probing Network Decisions: Capturing Uncertainties and Unveiling Vulnerabilities Without Label Information—0
Fair Federated Medical Image Classification Against Quality Shift via Inter-Client Progressive State MatchingCode1
Membership Inference Attacks fueled by Few-Short Learning to detect privacy leakage tackling data integrity—0
KAN-Mixers: a new deep learning architecture for image classification—0
MsaMIL-Net: An End-to-End Multi-Scale Aware Multiple Instance Learning Network for Efficient Whole Slide Image Classification—0
Tangentially Aligned Integrated Gradients for User-Friendly Explanations—0
Measuring directional bias amplification in image captions using predictability—0
MADS: Multi-Attribute Document Supervision for Zero-Shot Image Classification—0
Task Vector Quantization for Memory-Efficient Model MergingCode0
A Zero-shot Learning Method Based on Large Language Models for Multi-modal Knowledge Graph Embedding—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