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 5051–5100 of 10420 papers

TitleStatusHype
Evolutionary Neural Architecture Search for Image Restoration—0
CLOG-CD: Curriculum Learning based on Oscillating Granularity of Class Decomposed Medical Image Classification—0
Evolutionary Multi-objective Optimisation in Neurotrajectory Prediction—0
Evolutionary Cell Aided Design for Neural Network Architectures—0
EVM-Fusion: An Explainable Vision Mamba Architecture with Neural Algorithmic Fusion—0
Applying adversarial networks to increase the data efficiency and reliability of Self-Driving Cars—0
Adversarial Feature Alignment: Avoid Catastrophic Forgetting in Incremental Task Lifelong Learning—0
Evidential Federated Learning for Skin Lesion Image Classification—0
Evidence-empowered Transfer Learning for Alzheimer's Disease—0
CLIP-Q: Deep Network Compression Learning by In-Parallel Pruning-Quantization—0
Applications of the Streaming Networks—0
Event-Based Control for Online Training of Neural Networks—0
Evaluation, Tuning and Interpretation of Neural Networks for Meteorological Applications—0
Evaluation of Transfer Learning for Classification of: (1) Diabetic Retinopathy by Digital Fundus Photography and (2) Diabetic Macular Edema, Choroidal Neovascularization and Drusen by Optical Coherence Tomography—0
CLIP-QDA: An Explainable Concept Bottleneck Model—0
Applications of Sequential Learning for Medical Image Classification—0
Adversarial Examples Versus Cloud-based Detectors: A Black-box Empirical Study—0
A Comprehensive Survey on Hardware-Aware Neural Architecture Search—0
A 3-stage Spectral-spatial Method for Hyperspectral Image Classification—0
PreMix: Addressing Label Scarcity in Whole Slide Image Classification with Pre-trained Multiple Instance Learning Aggregators—0
Fidelity of Interpretability Methods and Perturbation Artifacts in Neural Networks—0
Applications and Effect Evaluation of Generative Adversarial Networks in Semi-Supervised Learning—0
Evaluation of Deep Learning on an Abstract Image Classification Dataset—0
Evaluation of Deep Convolutional Nets for Document Image Classification and Retrieval—0
CLIP meets Model Zoo Experts: Pseudo-Supervision for Visual Enhancement—0
Evaluation of Confidence-based Ensembling in Deep Learning Image Classification—0
Evaluation of Big Data based CNN Models in Classification of Skin Lesions with Melanoma—0
Application of the Neural Network Dependability Kit in Real-World Environments—0
Adversarial Examples on Segmentation Models Can be Easy to Transfer—0
Evaluating The Robustness of Self-Supervised Representations to Background/Foreground Removal—0
Evaluating the Robustness of Bayesian Neural Networks Against Different Types of Attacks—0
CLIPErase: Efficient Unlearning of Visual-Textual Associations in CLIP—0
Application of the Modified Fractal Signature Method for Terrain Classification from Synthetic Aperture Radar Images—0
Evaluating the Progress of Deep Learning for Visual Relational Concepts—0
Evaluating the performance of the LIME and Grad-CAM explanation methods on a LEGO multi-label image classification task—0
Evaluating the Fairness of Neural Collapse in Medical Image Classification—0
CLIPAG: Towards Generator-Free Text-to-Image Generation—0
Application of Sensitivity Analysis Methods for Studying Neural Network Models—0
Adversarial Examples in Remote Sensing—0
Evaluating the Effectiveness of Efficient Neural Architecture Search for Sentence-Pair Tasks—0
CLINICAL: Targeted Active Learning for Imbalanced Medical Image Classification—0
Evaluating Language-biased image classification based on semantic compositionality—0
ClickBAIT-v2: Training an Object Detector in Real-Time—0
A New Quantum CNN Model for Image Classification—0
ClickBAIT: Click-based Accelerated Incremental Training of Convolutional Neural Networks—0
c-Eval: A Unified Metric to Evaluate Feature-based Explanations via Perturbation—0
Enhancing Core Image Classification Using Generative Adversarial Networks (GANs)—0
Evaluating Deep Convolutional Neural Networks for Material Classification—0
Evaluating Data Augmentation Techniques for Coffee Leaf Disease Classification—0
Evaluating COPY-BLEND Augmentation for Low Level Vision 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