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

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Showing 301–350 of 10420 papers

TitleStatusHype
Open-Set Plankton Recognition—0
Falcon: A Remote Sensing Vision-Language Foundation ModelCode3
Leveraging Vision-Language Embeddings for Zero-Shot Learning in Histopathology Images—0
(, δ) Considered Harmful: Best Practices for Reporting Differential Privacy GuaranteesCode0
Do We Always Need the Simplicity Bias? Looking for Optimal Inductive Biases in the Wild—0
Multiplicative Learning—0
A Multi-Modal Federated Learning Framework for Remote Sensing Image Classification—0
Interpretable Image Classification via Non-parametric Part Prototype LearningCode1
Learning Interpretable Logic Rules from Deep Vision Models—0
Extreme Learning Machines for Attention-based Multiple Instance Learning in Whole-Slide Image Classification—0
Discovering Influential Neuron Path in Vision Transformers—0
ForAug: Recombining Foregrounds and Backgrounds to Improve Vision Transformer Training with Bias MitigationCode0
Bayesian Test-Time Adaptation for Vision-Language Models—0
Probing Network Decisions: Capturing Uncertainties and Unveiling Vulnerabilities Without Label Information—0
Deep Learning for Climate Action: Computer Vision Analysis of Visual Narratives on X—0
Membership Inference Attacks fueled by Few-Short Learning to detect privacy leakage tackling data integrity—0
Double-Stage Feature-Level Clustering-Based Mixture of Experts Framework—0
Fair Federated Medical Image Classification Against Quality Shift via Inter-Client Progressive State MatchingCode1
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
A Zero-shot Learning Method Based on Large Language Models for Multi-modal Knowledge Graph Embedding—0
Brain Inspired Adaptive Memory Dual-Net for Few-Shot Image Classification—0
Understanding the Learning Dynamics of LoRA: A Gradient Flow Perspective on Low-Rank Adaptation in Matrix Factorization—0
Keeping Representation Similarity in Finetuning for Medical Image Analysis—0
Task Vector Quantization for Memory-Efficient Model MergingCode0
MADS: Multi-Attribute Document Supervision for Zero-Shot Image Classification—0
Distilling Knowledge into Quantum Vision Transformers for Biomedical Image Classification—0
Enhancing Layer Attention Efficiency through Pruning Redundant Retrievals—0
M^3amba: CLIP-driven Mamba Model for Multi-modal Remote Sensing ClassificationCode1
Disrupting Model Merging: A Parameter-Level Defense Without Sacrificing Accuracy—0
Feature-EndoGaussian: Feature Distilled Gaussian Splatting in Surgical Deformable Scene Reconstruction—0
Data-Free Black-Box Federated Learning via Zeroth-Order Gradient Estimation—0
AF-KAN: Activation Function-Based Kolmogorov-Arnold Networks for Efficient Representation Learning—0
Pathological Prior-Guided Multiple Instance Learning For Mitigating Catastrophic Forgetting in Breast Cancer Whole Slide Image Classification—0
Minion Gated Recurrent Unit for Continual Learning—0
Randomized based restricted kernel machine for hyperspectral image classification—0
XFMamba: Cross-Fusion Mamba for Multi-View Medical Image ClassificationCode1
Remote Sensing Image Classification Using Convolutional Neural Network (CNN) and Transfer Learning Techniques—0
Sharpness-Aware Minimization: General Analysis and Improved RatesCode0
Measurement noise scaling laws for cellular representation learningCode0
Mathematical Foundation of Interpretable Equivariant Surrogate Models—0
Label Ranker: Self-Aware Preference for Classification Label Position in Visual Masked Self-Supervised Pre-Trained ModelCode0
Mamba base PKD for efficient knowledge compression—0
SAR-W-MixMAE: SAR Foundation Model Training Using Backscatter Power Weighting—0
ViKANformer: Embedding Kolmogorov Arnold Networks in Vision Transformers for Pattern-Based Learning—0
Visual-RFT: Visual Reinforcement Fine-TuningCode7
AMUN: Adversarial Machine UNlearning—0
Delving into Out-of-Distribution Detection with Medical Vision-Language ModelsCode1
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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