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 14011450 of 10419 papers

TitleStatusHype
DAF:re: A Challenging, Crowd-Sourced, Large-Scale, Long-Tailed Dataset For Anime Character RecognitionCode1
Interpreting and Analysing CLIP's Zero-Shot Image Classification via Mutual KnowledgeCode1
CAMIL: Context-Aware Multiple Instance Learning for Cancer Detection and Subtyping in Whole Slide ImagesCode1
Benchmarking Adversarial Robustness on Image ClassificationCode1
Benchmarking Bias Mitigation Algorithms in Representation Learning through Fairness MetricsCode1
Analysis and evaluation of Deep Learning based Super-Resolution algorithms to improve performance in Low-Resolution Face RecognitionCode1
Learning Loss for Active LearningCode1
DARTS: Differentiable Architecture SearchCode1
data2vec: A General Framework for Self-supervised Learning in Speech, Vision and LanguageCode1
Deblurring Masked Autoencoder is Better Recipe for Ultrasound Image RecognitionCode1
Data Augmentation with norm-VAE for Unsupervised Domain AdaptationCode1
Data Determines Distributional Robustness in Contrastive Language Image Pre-training (CLIP)Code1
It's All in the Head: Representation Knowledge Distillation through Classifier SharingCode1
JigsawHSI: a network for Hyperspectral Image classificationCode1
Benchmarking Test-Time Adaptation against Distribution Shifts in Image ClassificationCode1
ExCon: Explanation-driven Supervised Contrastive Learning for Image ClassificationCode1
Data-Efficient Deep Learning Method for Image Classification Using Data Augmentation, Focal Cosine Loss, and EnsembleCode1
Just Train Twice: Improving Group Robustness without Training Group InformationCode1
Maximum Likelihood with Bias-Corrected Calibration is Hard-To-Beat at Label Shift AdaptationCode1
No Routing Needed Between CapsulesCode1
Kernel Attention Transformer (KAT) for Histopathology Whole Slide Image ClassificationCode1
DataMUX: Data Multiplexing for Neural NetworksCode1
EXACT: How to Train Your AccuracyCode1
Expediting Large-Scale Vision Transformer for Dense Prediction without Fine-tuningCode1
Explaining in Style: Training a GAN to explain a classifier in StyleSpaceCode1
Knowledge Diffusion for DistillationCode1
A Survey of Classical And Quantum Sequence ModelsCode1
DC3DCD: unsupervised learning for multiclass 3D point cloud change detectionCode1
Evolving Attention with Residual ConvolutionsCode1
Circumventing Outliers of AutoAugment with Knowledge DistillationCode1
Active Learning for Convolutional Neural Networks: A Core-Set ApproachCode1
DEAL: Deep Evidential Active Learning for Image ClassificationCode1
Evolutionary Neural AutoML for Deep LearningCode1
BEV-LGKD: A Unified LiDAR-Guided Knowledge Distillation Framework for BEV 3D Object DetectionCode1
DiG-IN: Diffusion Guidance for Investigating Networks -- Uncovering Classifier Differences Neuron Visualisations and Visual Counterfactual ExplanationsCode1
DCT-CryptoNets: Scaling Private Inference in the Frequency DomainCode1
Evolving Normalization-Activation LayersCode1
Beyond Categorical Label Representations for Image ClassificationCode1
A Survey: Deep Learning for Hyperspectral Image Classification with Few Labeled SamplesCode1
Beyond Class-Conditional Assumption: A Primary Attempt to Combat Instance-Dependent Label NoiseCode1
DecAug: Out-of-Distribution Generalization via Decomposed Feature Representation and Semantic AugmentationCode1
LangXAI: Integrating Large Vision Models for Generating Textual Explanations to Enhance Explainability in Visual Perception TasksCode1
Large-scale Dataset Pruning with Dynamic UncertaintyCode1
Fcaformer: Forward Cross Attention in Hybrid Vision TransformerCode1
Beyond Gradient Averaging in Parallel Optimization: Improved Robustness through Gradient Agreement FilteringCode1
Decision Stream: Cultivating Deep Decision TreesCode1
Calibration of Neural Networks using SplinesCode1
AutoAssist: A Framework to Accelerate Training of Deep Neural NetworksCode1
Analyzing Vision Transformers for Image Classification in Class Embedding SpaceCode1
Cached Transformers: Improving Transformers with Differentiable Memory CacheCode1
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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