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 5451–5500 of 10420 papers

TitleStatusHype
Co-Correcting: Noise-tolerant Medical Image Classification via mutual Label CorrectionCode1
On the Initial Behavior Monitoring Issues in Federated Learning—0
Saliency Guided Experience Packing for Replay in Continual LearningCode0
LibFewShot: A Comprehensive Library for Few-shot LearningCode2
ConvMLP: Hierarchical Convolutional MLPs for VisionCode1
Generatively Augmented Neural Network Watchdog for Image Classification Networks—0
Fair Comparison: Quantifying Variance in Resultsfor Fine-grained Visual Categorization—0
Knowledge Distillation Using Hierarchical Self-Supervision Augmented DistributionCode1
Datasets: A Community Library for Natural Language ProcessingCode3
Quantum-Classical Hybrid Machine Learning for Image Classification (ICCAD Special Session Paper)—0
Tom: Leveraging trend of the observed gradients for faster convergenceCode0
Rethinking Crowdsourcing Annotation: Partial Annotation with Salient Labels for Multi-Label Image Classification—0
Vision Transformers For Weeds and Crops Classification Of High Resolution UAV Images—0
Less is More: Lighter and Faster Deep Neural Architecture for Tomato Leaf Disease ClassificationCode1
Automated Robustness with Adversarial Training as a Post-Processing Step—0
Cross-token Modeling with Conditional Computation—0
Real-World Adversarial Examples involving Makeup Application—0
Robust fine-tuning of zero-shot modelsCode1
ISyNet: Convolutional Neural Networks design for AI acceleratorCode0
Access Control Using Spatially Invariant Permutation of Feature Maps for Semantic Segmentation Models—0
Automated detection of COVID-19 cases from chest X-ray images using deep neural network and XGBoostCode1
CAM-loss: Towards Learning Spatially Discriminative Feature Representations—0
Ghost Loss to Question the Reliability of Training Data—0
Transfer of Pretrained Model Weights Substantially Improves Semi-Supervised Image ClassificationCode0
Better Self-training for Image Classification through Self-supervisionCode0
Impact of Attention on Adversarial Robustness of Image Classification Models—0
CrypTen: Secure Multi-Party Computation Meets Machine LearningCode2
Cross-Model Consensus of Explanations and Beyond for Image Classification Models: An Empirical Study—0
Multilingual Image Corpus: Annotation Protocol—0
Diverse Sample Generation: Pushing the Limit of Generative Data-free QuantizationCode1
The Impact of Reinitialization on Generalization in Convolutional Neural Networks—0
Deep Generative Modeling for Protein Design—0
Quantized Convolutional Neural Networks Through the Lens of Partial Differential Equations—0
Morphence: Moving Target Defense Against Adversarial ExamplesCode1
EG-Booster: Explanation-Guided Booster of ML Evasion AttacksCode0
Semi-supervised Image Classification with Grad-CAM ConsistencyCode1
PACE: Posthoc Architecture-Agnostic Concept Extractor for Explaining CNNs—0
AIP: Adversarial Iterative Pruning Based on Knowledge Transfer for Convolutional Neural Networks—0
Spike time displacement based error backpropagation in convolutional spiking neural networks—0
Communication-Computation Efficient Device-Edge Co-Inference via AutoML—0
Tune It or Don't Use It: Benchmarking Data-Efficient Image ClassificationCode1
Object-aware Long-short-range Spatial Alignment for Few-Shot Fine-Grained Image Classification—0
Hire-MLP: Vision MLP via Hierarchical RearrangementCode1
MEDIC: A Multi-Task Learning Dataset for Disaster Image ClassificationCode1
Privacy-preserving Machine Learning for Medical Image Classification—0
Towards Fine-grained Image Classification with Generative Adversarial Networks and Facial Landmark DetectionCode1
Representation Memorization for Fast Learning New Knowledge without Forgetting—0
New Pruning Method Based on DenseNet Network for Image Classification—0
Goal-driven text descriptions for images—0
An Automatic Image Content Retrieval Method for better Mobile Device Display User Experiences—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