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 6376–6400 of 10420 papers

TitleStatusHype
Ontology-based n-ball Concept Embeddings Informing Few-shot Image Classification—0
Splitfed learning without client-side synchronization: Analyzing client-side split network portion size to overall performance—0
The Unreasonable Effectiveness of the Final Batch Normalization Layer—0
Decision Tree Learning with Spatial Modal Logics—0
Transformer-Unet: Raw Image Processing with Unet—0
Exploiting Activation based Gradient Output Sparsity to Accelerate Backpropagation in CNNs—0
Deep Algorithmic Question Answering: Towards a Compositionally Hybrid AI for Algorithmic Reasoning—0
Sign-MAML: Efficient Model-Agnostic Meta-Learning by SignSGDCode0
Partner-Assisted Learning for Few-Shot Image Classification—0
AdaPruner: Adaptive Channel Pruning and Effective Weights Inheritance—0
A trainable monogenic ConvNet layer robust in front of large contrast changes in image classificationCode0
Task Guided Compositional Representation Learning for ZDA—0
PAT: Pseudo-Adversarial Training For Detecting Adversarial Videos—0
Robust Contrastive Active Learning with Feature-guided Query Strategies—0
Fine-Grained Few Shot Learning with Foreground Object Transformation—0
Check Your Other Door! Creating Backdoor Attacks in the Frequency Domain—0
BioLCNet: Reward-modulated Locally Connected Spiking Neural NetworksCode0
On the Initial Behavior Monitoring Issues in Federated Learning—0
Saliency Guided Experience Packing for Replay in Continual LearningCode0
Fair Comparison: Quantifying Variance in Resultsfor Fine-grained Visual Categorization—0
Tom: Leveraging trend of the observed gradients for faster convergenceCode0
Quantum-Classical Hybrid Machine Learning for Image Classification (ICCAD Special Session Paper)—0
Generatively Augmented Neural Network Watchdog for Image Classification Networks—0
Vision Transformers For Weeds and Crops Classification Of High Resolution UAV Images—0
Rethinking Crowdsourcing Annotation: Partial Annotation with Salient Labels for Multi-Label Image Classification—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
5DaViT-HTop 1 Accuracy90.2—Unverified
6Meta Pseudo Labels (EfficientNet-L2)Top 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
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90—Unverified