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

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Showing 5101–5150 of 10420 papers

TitleStatusHype
Representative Teacher Keys for Knowledge Distillation Model Compression Based on Attention Mechanism for Image Classification—0
Deep Axial Hypercomplex Networks—0
Knowledge-Free Black-Box Watermark and Ownership Proof for Image Classification Neural Networks—0
Kolmogorov-Arnold networks for metal surface defect classification—0
Revisiting the Role of Label Smoothing in Enhanced Text Sentiment Classification—0
Deep Boosting: Joint Feature Selection and Analysis Dictionary Learning in Hierarchy—0
Large Language Models Implicitly Learn to See and Hear Just By Reading—0
Improving Bag-of-Visual-Words Towards Effective Facial Expressive Image Classification—0
ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts—0
Improving Chest X-Ray Classification by RNN-based Patient Monitoring—0
Deep Cervix Model Development from Heterogeneous and Partially Labeled Image Datasets—0
Improving CNN classifiers by estimating test-time priors—0
Airway Label Prediction in Video Bronchoscopy: Capturing Temporal Dependencies Utilizing Anatomical Knowledge—0
Cryo-Electron Microscopy Image Analysis Using Multi-Frequency Vector Diffusion Maps—0
Knowledge Distillation for Object Detection via Rank Mimicking and Prediction-guided Feature Imitation—0
Improving Deep Learning through Automatic Programming—0
Deep CNNs for large scale species classification—0
Improving Deep Neural Networks with Probabilistic Maxout Units—0
Histopathological Image Classification and Vulnerability Analysis using Federated Learning—0
Histograms of Pattern Sets for Image Classification and Object Recognition—0
Crowdsourcing with Meta-Workers: A New Way to Save the Budget—0
Crowd-Powered Data Mining—0
Deep Collaborative Learning for Visual Recognition—0
Improving Few-Shot Image Classification Using Machine- and User-Generated Natural Language Descriptions—0
Enhancing Few-Shot Image Classification with Unlabelled Examples—0
Improving Few-Shot Visual Classification with Unlabelled Examples—0
HistoFS: Non-IID Histopathologic Whole Slide Image Classification via Federated Style Transfer with RoI-Preserving—0
Cross-View-Prediction: Exploring Contrastive Feature for Hyperspectral Image Classification—0
A Low-Cost Neural ODE with Depthwise Separable Convolution for Edge Domain Adaptation on FPGAs—0
Adaptive Class Preserving Representation for Image Classification—0
Knowledge Distillation for Incremental Learning in Semantic Segmentation—0
Knowledge Distillation in Generations: More Tolerant Teachers Educate Better Students—0
Improving Global Adversarial Robustness Generalization With Adversarially Trained GAN—0
Improving greedy core-set configurations for active learning with uncertainty-scaled distances—0
Improving High Resolution Histology Image Classification with Deep Spatial Fusion Network—0
Improving Human-AI Collaboration With Descriptions of AI Behavior—0
Improving Hyperbolic Representations via Gromov-Wasserstein Regularization—0
AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning—0
Improving Image Classification of Knee Radiographs: An Automated Image Labeling Approach—0
Improving Image Classification Robustness through Selective CNN-Filters Fine-Tuning—0
Improving Image Classification with Location Context—0
Improving Image Recognition by Retrieving from Web-Scale Image-Text Data—0
Hippocampus Temporal Lobe Epilepsy Detection using a Combination of Shape-based Features and Spherical Harmonics Representation—0
Improving Interpretability and Accuracy in Neuro-Symbolic Rule Extraction Using Class-Specific Sparse Filters—0
Deep Convolutional Neural Networks as Generic Feature Extractors—0
Improving Label Error Detection and Elimination with Uncertainty Quantification—0
Improving Layer-wise Adaptive Rate Methods using Trust Ratio Clipping—0
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning—0
Improving Machine Reading Comprehension via Adversarial Training—0
HindSight: A Graph-Based Vision Model Architecture For Representing Part-Whole Hierarchies—0
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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