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 1251–1300 of 10419 papers

TitleStatusHype
Denoised Smoothing: A Provable Defense for Pretrained ClassifiersCode1
FixCaps: An Improved Capsules Network for Diagnosis of Skin CancerCode1
Automated Learning Rate Scheduler for Large-batch TrainingCode1
BitQ: Tailoring Block Floating Point Precision for Improved DNN Efficiency on Resource-Constrained DevicesCode1
A Bregman Learning Framework for Sparse Neural NetworksCode1
Efficient Image-to-Image Diffusion Classifier for Adversarial RobustnessCode1
BiTr-Unet: a CNN-Transformer Combined Network for MRI Brain Tumor SegmentationCode1
Aggregated Residual Transformations for Deep Neural NetworksCode1
Efficient Model for Image Classification With Regularization TricksCode1
Automatically designing CNN architectures using genetic algorithm for image classificationCode1
Clusterability as an Alternative to Anchor Points When Learning with Noisy LabelsCode1
A survey on attention mechanisms for medical applications: are we moving towards better algorithms?Code1
Efficient Classification of Very Large Images with Tiny ObjectsCode1
Co2L: Contrastive Continual LearningCode1
Focal and Global Knowledge Distillation for DetectorsCode1
CMW-Net: Learning a Class-Aware Sample Weighting Mapping for Robust Deep LearningCode1
CNN Filter DB: An Empirical Investigation of Trained Convolutional FiltersCode1
Co^2L: Contrastive Continual LearningCode1
All you need is a good initCode1
FOCUS: Knowledge-enhanced Adaptive Visual Compression for Few-shot Whole Slide Image ClassificationCode1
Almost-Orthogonal Layers for Efficient General-Purpose Lipschitz NetworksCode1
CoAtNet: Marrying Convolution and Attention for All Data SizesCode1
Adaptive Checkpoint Adjoint Method for Gradient Estimation in Neural ODECode1
Forward Learning of Graph Neural NetworksCode1
Automatic Recognition of Abdominal Organs in Ultrasound Images based on Deep Neural Networks and K-Nearest-Neighbor ClassificationCode1
CoCa: Contrastive Captioners are Image-Text Foundation ModelsCode1
Co-Correcting: Noise-tolerant Medical Image Classification via mutual Label CorrectionCode1
CODE-CL: Conceptor-Based Gradient Projection for Deep Continual LearningCode1
Automating Continual LearningCode1
Communication-Efficient and Privacy-Preserving Feature-based Federated Transfer LearningCode1
AutoMix: Unveiling the Power of Mixup for Stronger ClassifiersCode1
Combating Label Noise in Deep Learning Using AbstentionCode1
Combining GANs and AutoEncoders for Efficient Anomaly DetectionCode1
Combining Metric Learning and Attention Heads For Accurate and Efficient Multilabel Image ClassificationCode1
AlphaNet: Improved Training of Supernets with Alpha-DivergenceCode1
Combining Human Predictions with Model Probabilities via Confusion Matrices and CalibrationCode1
BinaryViT: Pushing Binary Vision Transformers Towards Convolutional ModelsCode1
Efficient-CapsNet: Capsule Network with Self-Attention RoutingCode1
Benchmarking and scaling of deep learning models for land cover image classificationCode1
Billion-scale semi-supervised learning for image classificationCode1
AutoVP: An Automated Visual Prompting Framework and BenchmarkCode1
Bilinear MLPs enable weight-based mechanistic interpretabilityCode1
Function-Consistent Feature DistillationCode1
BionoiNet: ligand-binding site classification with off-the-shelf deep neural networkCode1
Systematic comparison of semi-supervised and self-supervised learning for medical image classificationCode1
Complementary-Label Learning for Arbitrary Losses and ModelsCode1
Gated Attention Coding for Training High-performance and Efficient Spiking Neural NetworksCode1
Density Adaptive Attention is All You Need: Robust Parameter-Efficient Fine-Tuning Across Multiple ModalitiesCode1
General E(2)-Equivariant Steerable CNNsCode1
Object Segmentation Without Labels with Large-Scale Generative ModelsCode1
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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