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 7876–7900 of 10420 papers

TitleStatusHype
Reflecting After Learning for Understanding—0
Reflexive Guidance: Improving OoDD in Vision-Language Models via Self-Guided Image-Adaptive Concept Generation—0
Region-Based Evidential Deep Learning to Quantify Uncertainty and Improve Robustness of Brain Tumor Segmentation—0
Unified Framework for Histopathology Image Augmentation and Classification via Generative Models—0
10,000+ Times Accelerated Robust Subset Selection (ARSS)—0
Reliable Explainability of Deep Learning Spatial-Spectral Classifiers for Improved Semantic Segmentation in Autonomous Driving—0
Fine-Grained Sports, Yoga, and Dance Postures Recognition: A Benchmark Analysis—0
Conditional Variational Autoencoder with Balanced Pre-training for Generative Adversarial Networks—0
A Framework for Double-Blind Federated Adaptation of Foundation Models—0
Relevance Prediction from Eye-movements Using Semi-interpretable Convolutional Neural Networks—0
Fast and Accurate Inference with Adaptive Ensemble Prediction for Deep Networks—0
Fine-Grained Recognition as HSnet Search for Informative Image Parts—0
Conditional Synthetic Food Image Generation—0
Conditional Sum-Product Networks: Imposing Structure on Deep Probabilistic Architectures—0
Relearning Forgotten Knowledge: on Forgetting, Overfit and Training-Free Ensembles of DNNs—0
Relevant-features based Auxiliary Cells for Energy Efficient Detection of Natural Errors—0
Reliable Probability Intervals For Classification Using Inductive Venn Predictors Based on Distance Learning—0
Fine-Grained Neural Architecture Search—0
Conditional Networks—0
Conditionally Deep Hybrid Neural Networks Across Edge and Cloud—0
Fast, Better Training Trick --- Random Gradient—0
Fine-Grained Image Classification via Combining Vision and Language—0
Fine-grained Image Classification by Exploring Bipartite-Graph Labels—0
Reinforced Attention for Few-Shot Learning and Beyond—0
Conditional Consistency Regularization for Semi-Supervised 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