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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 5301–5350 of 10420 papers

TitleStatusHype
Deep Learning based CNN Model for Classification and Detection of Individuals Wearing Face Mask—0
Integrating Propositional and Relational Label Side Information for Hierarchical Zero-Shot Image Classification—0
Integrating Scene Text and Visual Appearance for Fine-Grained Image Classification—0
Integration and Performance Analysis of Artificial Intelligence and Computer Vision Based on Deep Learning Algorithms—0
Integration of Roadside Camera Images and Weather Data for Monitoring Winter Road Surface Conditions—0
Intelligent Cervical Spine Fracture Detection Using Deep Learning Methods—0
Accelerating Deep Neural Networks via Semi-Structured Activation Sparsity—0
Deep learning based prediction of Alzheimer's disease from magnetic resonance images—0
Hierarchical Vision Transformer with Prototypes for Interpretable Medical Image Classification—0
Interaction as Explanation: A User Interaction-based Method for Explaining Image Classification Models—0
Hierarchical Transfer Convolutional Neural Networks for Image Classification—0
Crossmodal Knowledge Distillation with WordNet-Relaxed Text Embeddings for Robust Image Classification—0
Hierarchical Sparse Attention Framework for Computationally Efficient Classification of Biological Cells—0
Improved and Explainable Cervical Cancer Classification using Ensemble Pooling of Block Fused Descriptors—0
Hierarchical Side-Tuning for Vision Transformers—0
Interleaving Learning, with Application to Neural Architecture Search—0
Deep Learning for Active Region Classification: A Systematic Study from Convolutional Neural Networks to Vision Transformers—0
Attentive CutMix: An Enhanced Data Augmentation Approach for Deep Learning Based Image Classification—0
AIQViT: Architecture-Informed Post-Training Quantization for Vision Transformers—0
Learning Dependency Structures for Weak Supervision Models—0
Interpolation between CNNs and ResNets—0
Learning Discriminative Representation via Metric Learning for Imbalanced Medical Image Classification—0
Learning Fine-grained Features via a CNN Tree for Large-scale Classification—0
Hierarchical Semantic Tree Concept Whitening for Interpretable Image Classification—0
Hierarchical ResNeXt Models for Breast Cancer Histology Image Classification—0
Interpretable Bilingual Multimodal Large Language Model for Diverse Biomedical Tasks—0
Interpretable Deep Models for Cardiac Resynchronisation Therapy Response Prediction—0
Interpretable Failure Detection with Human-Level Concepts—0
Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts—0
Cross-Modality Attention with Semantic Graph Embedding for Multi-Label Classification—0
Cross-Modal Information Maximization for Medical Imaging: CMIM—0
Hierarchical Representation based Query-Specific Prototypical Network for Few-Shot Image Classification—0
Interpretable Mammographic Image Classification using Case-Based Reasoning and Deep Learning—0
Attention Tree: Learning Hierarchies of Visual Features for Large-Scale Image Recognition—0
Deep learning for lithological classification of carbonate rock micro-CT images—0
Learning Deep Context-Network Architectures for Image Annotation—0
Hierarchical Recurrent Neural Encoder for Video Representation with Application to Captioning—0
Cross-Modal Concept Learning and Inference for Vision-Language Models—0
Bonseyes AI Pipeline -- bringing AI to you. End-to-end integration of data, algorithms and deployment tools—0
Interpreting Equivariant Representations—0
Interpreting Interpretations: Organizing Attribution Methods by Criteria—0
Interpreting the Predictions of Complex ML Models by Layer-wise Relevance Propagation—0
Auxiliary Cross-Modal Representation Learning with Triplet Loss Functions for Online Handwriting Recognition—0
Deep Learning Generalization and the Convex Hull of Training Sets—0
Interventional Black-Box Explanations—0
Deep Learning Generalization, Extrapolation, and Over-parameterization—0
Hierarchical Image Classification with A Literally Toy Dataset—0
Intriguing Frequency Interpretation of Adversarial Robustness for CNNs and ViTs—0
Robustness via Deep Low-Rank Representations—0
Attention Spiking Neural Networks—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