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

Showing 101150 of 10419 papers

TitleStatusHype
Asymmetric Duos: Sidekicks Improve Uncertainty0
MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images0
Scaling Up Biomedical Vision-Language Models: Fine-Tuning, Instruction Tuning, and Multi-Modal LearningCode4
Feature Preserving Shrinkage on Bayesian Neural Networks via the R2D2 Prior0
Ownership Verification of DNN Models Using White-Box Adversarial Attacks with Specified Probability Manipulation0
SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond ClassificationCode0
EVM-Fusion: An Explainable Vision Mamba Architecture with Neural Algorithmic Fusion0
COLORA: Efficient Fine-Tuning for Convolutional Models with a Study Case on Optical Coherence Tomography Image Classification0
Accelerating Targeted Hard-Label Adversarial Attacks in Low-Query Black-Box SettingsCode0
Swin Transformer for Robust CGI Images Detection: Intra- and Inter-Dataset Analysis across Multiple Color Spaces0
When VLMs Meet Image Classification: Test Sets Renovation via Missing Label Identification0
Fusion of Foundation and Vision Transformer Model Features for Dermatoscopic Image Classification0
TULiP: Test-time Uncertainty Estimation via Linearization and Weight Perturbation0
GradPCA: Leveraging NTK Alignment for Reliable Out-of-Distribution Detection0
Domain Adaptive Skin Lesion Classification via Conformal Ensemble of Vision Transformers0
SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision TasksCode0
Aligning Explanations with Human CommunicationCode0
Beyond Linearity: Squeeze-and-Recalibrate Blocks for Few-Shot Whole Slide Image Classification0
FragFake: A Dataset for Fine-Grained Detection of Edited Images with Vision Language ModelsCode0
Adaptive Temperature Scaling with Conformal Prediction0
Parameter-Efficient Fine-Tuning of Multispectral Foundation Models for Hyperspectral Image Classification0
Large Language Models Implicitly Learn to See and Hear Just By Reading0
KO: Kinetics-inspired Neural Optimizer with PDE Simulation Approaches0
Domain Adaptation for Multi-label Image Classification: a Discriminator-free ApproachCode1
Intra-class Patch Swap for Self-DistillationCode0
Learning Concept-Driven Logical Rules for Interpretable and Generalizable Medical Image ClassificationCode1
Scaling Vision Mamba Across Resolutions via Fractal Traversal0
Enhancing Transformers Through Conditioned Embedded Tokens0
AGI-Elo: How Far Are We From Mastering A Task?Code1
Synthetic-Powered Predictive InferenceCode0
EPIC: Explanation of Pretrained Image Classification Networks via PrototypeCode0
Unlabeled Data or Pre-trained Model: Rethinking Semi-Supervised Learning and Pretrain-Finetuning0
Learning to Adapt to Position Bias in Vision Transformer ClassifiersCode0
An approach based on class activation maps for investigating the effects of data augmentation on neural networks for image classification0
Expert-Like Reparameterization of Heterogeneous Pyramid Receptive Fields in Efficient CNNs for Fair Medical Image Classification0
Emergence of Fixational and Saccadic Movements in a Multi-Level Recurrent Attention Model for Vision0
When majority rules, minority loses: bias amplification of gradient descent0
A Physics-Inspired Optimizer: Velocity Regularized Adam0
SRLoRA: Subspace Recomposition in Low-Rank Adaptation via Importance-Based Fusion and Reinitialization0
Spectral-Spatial Self-Supervised Learning for Few-Shot Hyperspectral Image ClassificationCode1
Denoising Mutual Knowledge Distillation in Bi-Directional Multiple Instance Learning0
SGD-Mix: Enhancing Domain-Specific Image Classification with Label-Preserving Data Augmentation0
A Training Framework for Optimal and Stable Training of Polynomial Neural NetworksCode0
Optimal Control for Transformer Architectures: Enhancing Generalization, Robustness and Efficiency0
Humble your Overconfident Networks: Unlearning Overfitting via Sequential Monte Carlo Tempered Deep Ensembles0
CheX-DS: Improving Chest X-ray Image Classification with Ensemble Learning Based on DenseNet and Swin Transformer0
Privacy-Aware Lifelong LearningCode0
MCU: Improving Machine Unlearning through Mode Connectivity0
CLIP Embeddings for AI-Generated Image Detection: A Few-Shot Study with Lightweight Classifier0
Marigold: Affordable Adaptation of Diffusion-Based Image Generators for Image AnalysisCode7
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CoCa (finetuned)Top 1 Accuracy91Unverified
2Model soups (BASIC-L)Top 1 Accuracy90.98Unverified
3Model soups (ViT-G/14)Top 1 Accuracy90.94Unverified
4DaViT-GTop 1 Accuracy90.4Unverified
5Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2Unverified
6DaViT-HTop 1 Accuracy90.2Unverified
7SwinV2-GTop 1 Accuracy90.17Unverified
8MAWS (ViT-6.5B)Top 1 Accuracy90.1Unverified
9Florence-CoSwin-HTop 1 Accuracy90.05Unverified
10RevCol-HTop 1 Accuracy90Unverified