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 901950 of 10419 papers

TitleStatusHype
Adversarial Continual LearningCode1
AutoVP: An Automated Visual Prompting Framework and BenchmarkCode1
Diagnose Like a Pathologist: Transformer-Enabled Hierarchical Attention-Guided Multiple Instance Learning for Whole Slide Image ClassificationCode1
Compressive Visual RepresentationsCode1
Fusion of Dual Spatial Information for Hyperspectral Image ClassificationCode1
A Partially Reversible U-Net for Memory-Efficient Volumetric Image SegmentationCode1
Learning Hierarchical Image Segmentation For Recognition and By RecognitionCode1
GAN-based Priors for Quantifying UncertaintyCode1
Gated Attention Coding for Training High-performance and Efficient Spiking Neural NetworksCode1
Concept Learners for Few-Shot LearningCode1
Generalizable Whole Slide Image Classification with Fine-Grained Visual-Semantic InteractionCode1
Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy LabelsCode1
DGSSC: A Deep Generative Spectral-Spatial Classifier for Imbalanced Hyperspectral ImageryCode1
Generalized Few-Shot Video Classification with Video Retrieval and Feature GenerationCode1
Confidence-aware multi-modality learning for eye disease screeningCode1
Anytime Dense Prediction with Confidence AdaptivityCode1
Confidence Regularized Self-TrainingCode1
GIST: Generating Image-Specific Text for Fine-grained Object ClassificationCode1
AutoSpeech: Neural Architecture Search for Speaker RecognitionCode1
DHP: Differentiable Meta Pruning via HyperNetworksCode1
Adversarial Examples in Deep Learning for Multivariate Time Series RegressionCode1
Generative Interventions for Causal LearningCode1
A Comprehensive Survey on Graph Neural NetworksCode1
Consistency-based Active Learning for Object DetectionCode1
Generic Neural Architecture Search via RegressionCode1
Generic-to-Specific Distillation of Masked AutoencodersCode1
Diagnosing Colorectal Polyps in the Wild with Capsule NetworksCode1
Contextual Diversity for Active LearningCode1
Automating Continual LearningCode1
GhostNet: More Features from Cheap OperationsCode1
AutoMix: Unveiling the Power of Mixup for Stronger ClassifiersCode1
GLiT: Neural Architecture Search for Global and Local Image TransformerCode1
Approaching Deep Learning through the Spectral Dynamics of WeightsCode1
Global Filter Networks for Image ClassificationCode1
Contextual Transformer Networks for Visual RecognitionCode1
Contextual Squeeze-and-Excitation for Efficient Few-Shot Image ClassificationCode1
Continual atlas-based segmentation of prostate MRICode1
Going deeper with Image TransformersCode1
DGMIL: Distribution Guided Multiple Instance Learning for Whole Slide Image ClassificationCode1
Continual Hippocampus Segmentation with TransformersCode1
All-in-One Image Coding for Joint Human-Machine Vision with Multi-Path AggregationCode1
Continual Learning for LiDAR Semantic Segmentation: Class-Incremental and Coarse-to-Fine strategies on Sparse DataCode1
Continual Learning with Scaled Gradient ProjectionCode1
Contrasting Intra-Modal and Ranking Cross-Modal Hard Negatives to Enhance Visio-Linguistic Compositional UnderstandingCode1
ConTNet: Why not use convolution and transformer at the same time?Code1
Gradient Centralization: A New Optimization Technique for Deep Neural NetworksCode1
Contrastive Deep SupervisionCode1
Gradient Projection Memory for Continual LearningCode1
GradInit: Learning to Initialize Neural Networks for Stable and Efficient TrainingCode1
Babel-ImageNet: Massively Multilingual Evaluation of Vision-and-Language RepresentationsCode1
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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
5DaViT-HTop 1 Accuracy90.2Unverified
6Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2Unverified
7SwinV2-GTop 1 Accuracy90.17Unverified
8MAWS (ViT-6.5B)Top 1 Accuracy90.1Unverified
9Florence-CoSwin-HTop 1 Accuracy90.05Unverified
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90Unverified