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 6201–6250 of 10420 papers

TitleStatusHype
Incorporating Convolution Designs into Visual TransformersCode1
ScanMix: Learning from Severe Label Noise via Semantic Clustering and Semi-Supervised LearningCode0
Hierarchical Representation based Query-Specific Prototypical Network for Few-Shot Image Classification—0
Robust Models Are More Interpretable Because Attributions Look NormalCode1
Local Patch AutoAugment with Multi-Agent CollaborationCode1
Transfer learning for automatic brain tumor classification Using MRI Images.—0
ThanosNet: A Novel Trash Classification Method Using MetadataCode0
Variational Knowledge Distillation for Disease Classification in Chest X-Rays—0
Cluster-to-Conquer: A Framework for End-to-End Multi-Instance Learning for Whole Slide Image ClassificationCode1
Sewer-ML: A Multi-Label Sewer Defect Classification Dataset and BenchmarkCode0
Implementation of Artificial Neural Networks for the Nepta-Uranian Interplanetary (NUIP) Mission—0
ConViT: Improving Vision Transformers with Soft Convolutional Inductive BiasesCode0
Scalable Vision Transformers with Hierarchical PoolingCode1
3D Human Pose Estimation with Spatial and Temporal TransformersCode1
MSMatch: Semi-Supervised Multispectral Scene Classification with Few LabelsCode1
TPPI-Net: Towards Efficient and Practical Hyperspectral Image Classification—0
TrivialAugment: Tuning-free Yet State-of-the-Art Data AugmentationCode1
The Low-Rank Simplicity Bias in Deep NetworksCode1
Neural Network Attribution Methods for Problems in Geoscience: A Novel Synthetic Benchmark DatasetCode1
Danish Fungi 2020 -- Not Just Another Image Recognition DatasetCode1
Consistency-based Active Learning for Object DetectionCode1
Stride and Translation Invariance in CNNs—0
Large-Scale Zero-Shot Image Classification from Rich and Diverse Textual Descriptions—0
Quantitative Performance Assessment of CNN Units via Topological Entropy Calculation—0
HAMIL: Hierarchical Aggregation-Based Multi-Instance Learning for Microscopy Image Classification—0
Gradient Projection Memory for Continual LearningCode1
Triplet-Watershed for Hyperspectral Image ClassificationCode1
Adversarial YOLO: Defense Human Detection Patch Attacks via Detecting Adversarial Patches—0
Distributed Deep Learning Using Volunteer Computing-Like Paradigm—0
Learning Hyperbolic Representations of Topological FeaturesCode0
Learned Gradient Compression for Distributed Deep Learning—0
Reweighting Augmented Samples by Minimizing the Maximal Expected LossCode0
Is it enough to optimize CNN architectures on ImageNet?Code0
UPANets: Learning from the Universal Pixel Attention NetworksCode1
Deep Reinforcement Learning for Band Selection in Hyperspectral Image ClassificationCode1
Evolving parametrized Loss for Image Classification Learning on Small Datasets—0
Distance Metric-Based Learning with Interpolated Latent Features for Location Classification in Endoscopy Image and Video—0
How to distribute data across tasks for meta-learning?—0
Sampling-free Variational Inference for Neural Networks with Multiplicative Activation Noise—0
TransFG: A Transformer Architecture for Fine-grained RecognitionCode1
Membership Inference Attacks on Machine Learning: A SurveyCode1
CrossoverScheduler: Overlapping Multiple Distributed Training Applications in a Crossover Manner—0
Efficient Sparse Artificial Neural Networks—0
Revisiting ResNets: Improved Training and Scaling StrategiesCode1
Uncertainty-guided Model Generalization to Unseen Domains—0
Learnable Companding Quantization for Accurate Low-bit Neural Networks—0
Interleaving Learning, with Application to Neural Architecture Search—0
Information Maximization Clustering via Multi-View Self-LabellingCode1
Evaluating COPY-BLEND Augmentation for Low Level Vision Tasks—0
Why flatness does and does not correlate with generalization for deep neural networks—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