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 12761300 of 10419 papers

TitleStatusHype
Beyond Categorical Label Representations for Image ClassificationCode1
Convolution-enhanced Evolving Attention NetworksCode1
ELSA: Enhanced Local Self-Attention for Vision TransformerCode1
CorGAN: Correlation-Capturing Convolutional Generative Adversarial Networks for Generating Synthetic Healthcare RecordsCode1
Co-Tuning for Transfer LearningCode1
AutoMix: Unveiling the Power of Mixup for Stronger ClassifiersCode1
BiTr-Unet: a CNN-Transformer Combined Network for MRI Brain Tumor SegmentationCode1
Enhanced OoD Detection through Cross-Modal Alignment of Multi-Modal RepresentationsCode1
Enhance Image Classification via Inter-Class Image Mixup with Diffusion ModelCode1
AlphaNet: Improved Training of Supernets with Alpha-DivergenceCode1
Enhancing Few-Shot Image Classification through Learnable Multi-Scale Embedding and Attention MechanismsCode1
AutoSpeech: Neural Architecture Search for Speaker RecognitionCode1
Convolutional Sequence to Sequence LearningCode1
Convolutional Channel-wise Competitive Learning for the Forward-Forward AlgorithmCode1
Entroformer: A Transformer-based Entropy Model for Learned Image CompressionCode1
AutoVP: An Automated Visual Prompting Framework and BenchmarkCode1
EPSANet: An Efficient Pyramid Squeeze Attention Block on Convolutional Neural NetworkCode1
Convolutional Spiking Neural Networks for Spatio-Temporal Feature ExtractionCode1
Error-Bounded Correction of Noisy LabelsCode1
Systematic comparison of semi-supervised and self-supervised learning for medical image classificationCode1
Evaluating histopathology transfer learning with ChampKitCode1
Evaluating the Adversarial Robustness of Adaptive Test-time DefensesCode1
Evaluating the visualization of what a Deep Neural Network has learnedCode1
EViT: An Eagle Vision Transformer with Bi-Fovea Self-AttentionCode1
No Routing Needed Between CapsulesCode1
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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