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 21012150 of 10419 papers

TitleStatusHype
CosPGD: an efficient white-box adversarial attack for pixel-wise prediction tasksCode1
Consistency-based Active Learning for Object DetectionCode1
A Spectral-Spatial-Dependent Global Learning Framework for Insufficient and Imbalanced Hyperspectral Image ClassificationCode1
Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy LabelsCode1
It's All in the Head: Representation Knowledge Distillation through Classifier SharingCode1
Co-Tuning for Transfer LearningCode1
Counterfactual Visual ExplanationsCode1
Joint Modeling of Chest Radiographs and Radiology Reports for Pulmonary Edema AssessmentCode1
Just Train Twice: Improving Group Robustness without Training Group InformationCode1
Kaleidoscope: An Efficient, Learnable Representation For All Structured Linear MapsCode1
A Simple Baseline for Semi-supervised Semantic Segmentation with Strong Data AugmentationCode1
Cross-modal Adversarial ReprogrammingCode1
Data Determines Distributional Robustness in Contrastive Language Image Pre-training (CLIP)Code1
KNAS: Green Neural Architecture SearchCode1
InceptionMamba: An Efficient Hybrid Network with Large Band Convolution and Bottleneck MambaCode1
A Simple Baseline for Low-Budget Active LearningCode1
CondenseNet V2: Sparse Feature Reactivation for Deep NetworksCode1
Knowledge Distillation from A Stronger TeacherCode1
Incorporating Convolution Designs into Visual TransformersCode1
A fuzzy distance-based ensemble of deep models for cervical cancer detectionCode1
Cross-Domain Ensemble Distillation for Domain GeneralizationCode1
Content-aware Token Sharing for Efficient Semantic Segmentation with Vision TransformersCode1
Information Maximization Clustering via Multi-View Self-LabellingCode1
Cross-Iteration Batch NormalizationCode1
Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional NetworksCode1
Learning Hierarchical Image Segmentation For Recognition and By RecognitionCode1
Active Domain Adaptation via Clustering Uncertainty-weighted EmbeddingsCode1
Improving Vision Transformers by Revisiting High-frequency ComponentsCode1
Cross-modulated Few-shot Image Generation for Colorectal Tissue ClassificationCode1
Language-Guided Transformer for Federated Multi-Label ClassificationCode1
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
Language Quantized AutoEncoders: Towards Unsupervised Text-Image AlignmentCode1
LangXAI: Integrating Large Vision Models for Generating Textual Explanations to Enhance Explainability in Visual Perception TasksCode1
LR-Net: A Block-based Convolutional Neural Network for Low-Resolution Image ClassificationCode1
CSPNet: A New Backbone that can Enhance Learning Capability of CNNCode1
Improving the Resolution of CNN Feature Maps Efficiently with MultisamplingCode1
Contextual Convolutional Neural NetworksCode1
Improving Visual Prompt Tuning for Self-supervised Vision TransformersCode1
Age Estimation Using Expectation of Label Distribution LearningCode1
A Stitch in Time Saves Nine: A Train-Time Regularizing Loss for Improved Neural Network CalibrationCode1
Lawin Transformer: Improving Semantic Segmentation Transformer with Multi-Scale Representations via Large Window AttentionCode1
Active Finetuning: Exploiting Annotation Budget in the Pretraining-Finetuning ParadigmCode1
Curriculum Temperature for Knowledge DistillationCode1
Astroformer: More Data Might not be all you need for ClassificationCode1
Curriculum By SmoothingCode1
Improving robustness against common corruptions by covariate shift adaptationCode1
Contextual Transformer Networks for Visual RecognitionCode1
Curriculum Labeling: Revisiting Pseudo-Labeling for Semi-Supervised LearningCode1
Concept Learners for Few-Shot LearningCode1
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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