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 7101–7150 of 10420 papers

TitleStatusHype
On the Neural Tangent Kernel Analysis of Randomly Pruned Neural Networks—0
End-to-end optimized image compression for multiple machine tasks—0
Continual Learning with Bayesian Model based on a Fixed Pre-trained Feature Extractor—0
Function-Space Regularization for Deep Bayesian Classification—0
A study of the effect of JPG compression on adversarial images—0
Energy-Based Spherical Sparse Coding—0
ParFormer: A Vision Transformer with Parallel Mixer and Sparse Channel Attention Patch Embedding—0
On the rate of convergence of image classifiers based on convolutional neural networks—0
Energy-efficient Amortized Inference with Cascaded Deep Classifiers—0
On the Reproducibility of Neural Network Predictions—0
A Framework for Generalizing Critical Heat Flux Detection Models Using Unsupervised Image-to-Image Translation—0
On the Robustness of Kolmogorov-Arnold Networks: An Adversarial Perspective—0
On the Robustness of Large Multimodal Models Against Image Adversarial Attacks—0
Energy-Efficient Classification at the Wireless Edge with Reliability Guarantees—0
Active Generative Adversarial Network for Image Classification—0
Fully Hyperbolic Convolutional Neural Networks—0
On the Sampling Strategy for Evaluation of Spectral-spatial Methods in Hyperspectral Image Classification—0
On the Shift Invariance of Max Pooling Feature Maps in Convolutional Neural Networks—0
Fully Convolutional Neural Networks to Detect Clinical Dermoscopic Features—0
A Study of Image Analysis with Tangent Distance—0
Energy-Efficient Model Compression and Splitting for Collaborative Inference Over Time-Varying Channels—0
On the Transferability of Adversarial Examples between Encrypted Models—0
On the universality of neural encodings in CNNs—0
On the unreasonable vulnerability of transformers for image restoration -- and an easy fix—0
Unsupervised Continual Learning Via Pseudo Labels—0
Fully Connected Deep Structured Networks—0
Abnormal Client Behavior Detection in Federated Learning—0
Ontology-based n-ball Concept Embeddings Informing Few-shot Image Classification—0
Classification and Retrieval of Digital Pathology Scans: A New Dataset—0
Ontology-Driven Semantic Alignment of Artificial Neurons and Visual Concepts—0
Full-attention based Neural Architecture Search using Context Auto-regression—0
Ontology of Visual Objects—0
A Study of Compositional Generalization in Neural Models—0
Parametric Matrix Models—0
PARIC: Probabilistic Attention Regularization for Language Guided Image Classification from Pre-trained Vison Language Models—0
ParticleAugment: Sampling-Based Data Augmentation—0
OOD-CV-v2: An extended Benchmark for Robustness to Out-of-Distribution Shifts of Individual Nuisances in Natural Images—0
FSNet: Compression of Deep Convolutional Neural Networks by Filter Summary—0
Enhanced Infield Agriculture with Interpretable Machine Learning Approaches for Crop Classification—0
Continual Learning for Class- and Domain-Incremental Semantic Segmentation—0
Classification Driven Dynamic Image Enhancement—0
OpenMedIA: Open-Source Medical Image Analysis Toolbox and Benchmark under Heterogeneous AI Computing Platforms—0
Fruit-HSNet: A Machine Learning Approach for Hyperspectral Image-Based Fruit Ripeness Prediction—0
Continual learning benefits from multiple sleep mechanisms: NREM, REM, and Synaptic Downscaling—0
A Study of BFLOAT16 for Deep Learning Training—0
Anticipate, Ensemble and Prune: Improving Convolutional Neural Networks via Aggregated Early Exits—0
Frugal Reinforcement-based Active Learning—0
Open-Set: ID Card Presentation Attack Detection using Neural Transfer Style—0
Open Set Learning with Counterfactual Images—0
Efficient Online ML API Selection for Multi-Label Classification Tasks—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