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 13011350 of 10419 papers

TitleStatusHype
Averaging Weights Leads to Wider Optima and Better GeneralizationCode1
Explanation as a Watermark: Towards Harmless and Multi-bit Model Ownership Verification via Watermarking Feature AttributionCode1
Exploring Vision Transformers for Fine-grained ClassificationCode1
Extending CAM-based XAI methods for Remote Sensing Imagery SegmentationCode1
A Visual Analytics Framework for Explaining and Diagnosing Transfer Learning ProcessesCode1
Eye-gaze Guided Multi-modal Alignment for Medical Representation LearningCode1
No Routing Needed Between CapsulesCode1
Failure Detection in Medical Image Classification: A Reality Check and Benchmarking TestbedCode1
Adaptive DropBlock Enhanced Generative Adversarial Networks for Hyperspectral Image ClassificationCode1
AwesomeMeta+: A Mixed-Prototyping Meta-Learning System Supporting AI Application Design AnywhereCode1
A Whac-A-Mole Dilemma: Shortcuts Come in Multiples Where Mitigating One Amplifies OthersCode1
Cross-Domain Ensemble Distillation for Domain GeneralizationCode1
Cross-Iteration Batch NormalizationCode1
Fast AutoAugmentCode1
Adaptive Edge Offloading for Image Classification Under Rate LimitCode1
Babel-ImageNet: Massively Multilingual Evaluation of Vision-and-Language RepresentationsCode1
Faster Meta Update Strategy for Noise-Robust Deep LearningCode1
CSP: Self-Supervised Contrastive Spatial Pre-Training for Geospatial-Visual RepresentationsCode1
Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary CellsCode1
Backdoor Attacks on Crowd CountingCode1
A Survey of Classical And Quantum Sequence ModelsCode1
CPrune: Compiler-Informed Model Pruning for Efficient Target-Aware DNN ExecutionCode1
Blacklight: Scalable Defense for Neural Networks against Query-Based Black-Box AttacksCode1
FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture SearchCode1
FCCNs: Fully Complex-valued Convolutional Networks using Complex-valued Color Model and Loss FunctionCode1
FC-KAN: Function Combinations in Kolmogorov-Arnold NetworksCode1
"BNN - BN = ?": Training Binary Neural Networks without Batch NormalizationCode1
Feature Interaction Interpretability: A Case for Explaining Ad-Recommendation Systems via Neural Interaction DetectionCode1
BadHash: Invisible Backdoor Attacks against Deep Hashing with Clean LabelCode1
FedBABU: Towards Enhanced Representation for Federated Image ClassificationCode1
FedDefender: Backdoor Attack Defense in Federated LearningCode1
Federated Learning via Input-Output Collaborative DistillationCode1
Federated Semi-supervised Medical Image Classification via Inter-client Relation MatchingCode1
BAGAN: Data Augmentation with Balancing GANCode1
Boosting Active Learning via Improving Test PerformanceCode1
COVID-CXNet: Detecting COVID-19 in Frontal Chest X-ray Images using Deep LearningCode1
Bag of Tricks for Image Classification with Convolutional Neural NetworksCode1
FedIIC: Towards Robust Federated Learning for Class-Imbalanced Medical Image ClassificationCode1
Domain Prompt Learning for Efficiently Adapting CLIP to Unseen DomainsCode1
Boosting Co-teaching with Compression Regularization for Label NoiseCode1
Balanced Contrastive Learning for Long-Tailed Visual RecognitionCode1
Balanced Energy Regularization Loss for Out-of-distribution DetectionCode1
FFT-based Dynamic Token Mixer for VisionCode1
FIBA: Frequency-Injection based Backdoor Attack in Medical Image AnalysisCode1
FILIP: Fine-grained Interactive Language-Image Pre-TrainingCode1
Balanced-MixUp for Highly Imbalanced Medical Image ClassificationCode1
Fine-grained Image Classification and Retrieval by Combining Visual and Locally Pooled Textual FeaturesCode1
Mixture Outlier Exposure: Towards Out-of-Distribution Detection in Fine-grained EnvironmentsCode1
Fine-grained Recognition with Learnable Semantic Data AugmentationCode1
Active Learning for Convolutional Neural Networks: A Core-Set ApproachCode1
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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