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 8851–8900 of 10420 papers

TitleStatusHype
Embedding Complementary Deep Networks for Image Classification—0
Embedding Deep Networks into Visual Explanations—0
Embedding Label Structures for Fine-Grained Feature Representation—0
Embedding of FRPN in CNN architecture—0
Embedding Radiomics into Vision Transformers for Multimodal Medical Image Classification—0
Embeddings are all you need! Achieving High Performance Medical Image Classification through Training-Free Embedding Analysis—0
Embedding Semantic Hierarchy in Discrete Optimal Transport for Risk Minimization—0
Embedding Task Knowledge into 3D Neural Networks via Self-supervised Learning—0
EmbRace: Accelerating Sparse Communication for Distributed Training of NLP Neural Networks—0
Embracing the Dark Knowledge: Domain Generalization Using Regularized Knowledge Distillation—0
EMDS-5: Environmental Microorganism Image Dataset Fifth Version for Multiple Image Analysis Tasks—0
EMDS-6: Environmental Microorganism Image Dataset Sixth Version for Image Denoising, Segmentation, Feature Extraction, Classification and Detection Methods Evaluation—0
Emergence of Fixational and Saccadic Movements in a Multi-Level Recurrent Attention Model for Vision—0
EMIXER: End-to-end Multimodal X-ray Generation via Self-supervision—0
EmoCAM: Toward Understanding What Drives CNN-based Emotion Recognition—0
EMP: Enhance Memory in Data Pruning—0
Empirical Perspectives on One-Shot Semi-supervised Learning—0
Empirical Risk Minimization for Stochastic Convex Optimization: O(1/n)- and O(1/n^2)-type of Risk Bounds—0
Empowering Networks With Scale and Rotation Equivariance Using A Similarity Convolution—0
Empowering Vision Transformers with Multi-Scale Causal Intervention for Long-Tailed Image Classification—0
Enabling Deep Learning on Edge Devices through Filter Pruning and Knowledge Transfer—0
Enabling Efficient Processing of Spiking Neural Networks with On-Chip Learning on Commodity Neuromorphic Processors for Edge AI Systems—0
Enabling Small Models for Zero-Shot Selection and Reuse through Model Label Learning—0
EncodeNet: A Framework for Boosting DNN Accuracy with Entropy-driven Generalized Converting Autoencoder—0
Encoder Based Lifelong Learning—0
Encoders and Ensembles for Task-Free Continual Learning—0
Encoding Hierarchical Information in Neural Networks helps in Subpopulation Shift—0
Encoding High Dimensional Local Features by Sparse Coding Based Fisher Vectors—0
Endoscopy Classification Model Using Swin Transformer and Saliency Map—0
End-to-End Anti-Backdoor Learning on Images and Time Series—0
End-to-End Kernel Learning with Supervised Convolutional Kernel Networks—0
End-to-End Optimization of JPEG-Based Deep Learning Process for Image Classification—0
End-to-end optimized image compression for multiple machine tasks—0
End-to-end training of deep kernel map networks for image classification—0
Energy-Based Spherical Sparse Coding—0
Energy-constrained Self-training for Unsupervised Domain Adaptation—0
Energy-efficient Amortized Inference with Cascaded Deep Classifiers—0
Energy-Efficient and Federated Meta-Learning via Projected Stochastic Gradient Ascent—0
Energy-Efficient Classification at the Wireless Edge with Reliability Guarantees—0
Energy-Efficient Deployment of Machine Learning Workloads on Neuromorphic Hardware—0
Energy Efficient Hadamard Neural Networks—0
Energy Efficient Hardware for On-Device CNN Inference via Transfer Learning—0
Energy-Efficient Model Compression and Splitting for Collaborative Inference Over Time-Varying Channels—0
Energy-entropy competition and the effectiveness of stochastic gradient descent in machine learning—0
Enhanced Attacks on Defensively Distilled Deep Neural Networks—0
Enhanced Gradient for Differentiable Architecture Search—0
Enhanced Image Classification With a Fast-Learning Shallow Convolutional Neural Network—0
Enhanced Image Classification With Data Augmentation Using Position Coordinates—0
Enhanced Infield Agriculture with Interpretable Machine Learning Approaches for Crop Classification—0
Enhanced Object Detection via Fusion With Prior Beliefs from Image Classification—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