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 7226–7250 of 10420 papers

TitleStatusHype
Frozen Overparameterization: A Double Descent Perspective on Transfer Learning of Deep Neural Networks—0
Iterative Attention Mining for Weakly Supervised Thoracic Disease Pattern Localization in Chest X-Rays—0
Iterative Averaging in the Quest for Best Test Error—0
IT^3: Idempotent Test-Time Training—0
Over-parameterization: A Necessary Condition for Models that Extrapolate—0
Deep Metric Learning and Image Classification with Nearest Neighbour Gaussian Kernels—0
Over-the-Air Edge Inference via End-to-End Metasurfaces-Integrated Artificial Neural Networks—0
Banana Sub-Family Classification and Quality Prediction using Computer Vision—0
CAP: Correlation-Aware Pruning for Highly-Accurate Sparse Vision Models—0
A Multi-modal Approach to Single-modal Visual Place Classification—0
Is Pretraining Necessary for Hyperspectral Image Classification?—0
DeepMAD: Mathematical Architecture Design for Deep Convolutional Neural Network—0
PACE: Posthoc Architecture-Agnostic Concept Extractor for Explaining CNNs—0
Balancing the Causal Effects in Class-Incremental Learning—0
Is network fragmentation a useful complexity measure?—0
Deeply Supervised Layer Selective Attention Network: Towards Label-Efficient Learning for Medical Image Classification—0
PAC Synthesis of Machine Learning Programs—0
Sparsely-gated Mixture-of-Expert Layers for CNN Interpretability—0
Is my Driver Observation Model Overconfident? Input-guided Calibration Networks for Reliable and Interpretable Confidence Estimates—0
Is More Data All You Need? A Causal Exploration—0
Is Label Smoothing Truly Incompatible with Knowledge Distillation: An Empirical Study—0
Deeply Coupled Auto-encoder Networks for Cross-view Classification—0
Less is more: Selecting informative and diverse subsets with balancing constraints—0
Is it all a cluster game? -- Exploring Out-of-Distribution Detection based on Clustering in the Embedding Space—0
Is the aspect ratio of cells important in deep learning? A robust comparison of deep learning methods for multi-scale cytopathology cell image classification: from convolutional neural networks to visual transformers—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