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 18261850 of 10419 papers

TitleStatusHype
Rethinking Multiple Instance Learning for Whole Slide Image Classification: A Good Instance Classifier is All You NeedCode1
Adversarial Example Detection for DNN Models: A Review and Experimental ComparisonCode1
Rethinking Recurrent Neural Networks and Other Improvements for Image ClassificationCode1
Deep Roto-Translation Scattering for Object ClassificationCode1
Rethinking the Inception Architecture for Computer VisionCode1
Deep Transfer Learning for Land Use and Land Cover Classification: A Comparative StudyCode1
Deep Networks with Stochastic DepthCode1
Reversible Vision TransformersCode1
Revising deep learning methods in parking lot occupancy detectionCode1
Revisiting Discriminative vs. Generative Classifiers: Theory and ImplicationsCode1
Revisiting Label Smoothing and Knowledge Distillation Compatibility: What was Missing?Code1
CLCC: Contrastive Learning for Color ConstancyCode1
CLCNet: Rethinking of Ensemble Modeling with Classification Confidence NetworkCode1
Cross-Layer Retrospective Retrieving via Layer AttentionCode1
Clean-Label Backdoor Attacks on Video Recognition ModelsCode1
CleanNet: Transfer Learning for Scalable Image Classifier Training with Label NoiseCode1
Revisiting Weakly Supervised Pre-Training of Visual Perception ModelsCode1
Adversarial Examples in Deep Learning for Multivariate Time Series RegressionCode1
Revitalizing CNN Attention via Transformers in Self-Supervised Visual Representation LearningCode1
Rethinking Channel Dimensions for Efficient Model DesignCode1
CLIP4IDC: CLIP for Image Difference CaptioningCode1
A Comprehensive Survey on Graph Neural NetworksCode1
A Fuzzy Rank-based Ensemble of CNN Models for Classification of Cervical CytologyCode1
Robotic Pick-and-Place of Novel Objects in Clutter with Multi-Affordance Grasping and Cross-Domain Image MatchingCode1
DeepNoise: Signal and Noise Disentanglement based on Classifying Fluorescent Microscopy Images via Deep 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