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 9401–9450 of 10420 papers

TitleStatusHype
Generative Data Mining with Longtail-Guided Diffusion—0
Generatively Augmented Neural Network Watchdog for Image Classification Networks—0
Generative Model for Models: Rapid DNN Customization for Diverse Tasks and Resource Constraints—0
Generative Negative Text Replay for Continual Vision-Language Pretraining—0
Generative NeuroEvolution for Deep Learning—0
Generic Image Classification Approaches Excel on Face Recognition—0
Generic Semi-Supervised Adversarial Subject Translation for Sensor-Based Human Activity Recognition—0
GeneSys: Enabling Continuous Learning through Neural Network Evolution in Hardware—0
Genetic Programming-Based Evolutionary Deep Learning for Data-Efficient Image Classification—0
GenMix: Combining Generative and Mixture Data Augmentation for Medical Image Classification—0
GenMix: Effective Data Augmentation with Generative Diffusion Model Image Editing—0
Geodesics of learned representations—0
Geometric Mean Improves Loss For Few-Shot Learning—0
Geometric Median Matching for Robust k-Subset Selection from Noisy Data—0
Geometric Neural Phrase Pooling: Modeling the Spatial Co-occurrence of Neurons—0
Geometric Scattering for Graph Data Analysis—0
Geometry aware convolutional filters for omnidirectional images representation—0
GeoNet: Benchmarking Unsupervised Adaptation across Geographies—0
GeoTop: Advancing Image Classification with Geometric-Topological Analysis—0
GeoWATCH for Detecting Heavy Construction in Heterogeneous Time Series of Satellite Images—0
Get Rid Of Your Trail: Remotely Erasing Backdoors in Federated Learning—0
Getting ViT in Shape: Scaling Laws for Compute-Optimal Model Design—0
G-EvoNAS: Evolutionary Neural Architecture Search Based on Network Growth—0
Ghost Loss to Question the Reliability of Training Data—0
GhostNetV3: Exploring the Training Strategies for Compact Models—0
Gibbs Sampling with Low-Power Spiking Digital Neurons—0
GIFAIR-FL: A Framework for Group and Individual Fairness in Federated Learning—0
GIST: Greedy Independent Set Thresholding for Diverse Data Summarization—0
Give Me Your Attention: Dot-Product Attention Considered Harmful for Adversarial Patch Robustness—0
Glasses Detection Using Convolutional Neural Networks—0
Advancing Supervised Local Learning Beyond Classification with Long-term Feature Bank—0
Global Deconvolutional Networks for Semantic Segmentation—0
Global Feature Guided Local Pooling—0
Global Interaction Modelling in Vision Transformer via Super Tokens—0
Global Meets Local: Effective Multi-Label Image Classification via Category-Aware Weak Supervision—0
Global-to-Local Support Spectrums for Language Model Explainability—0
Global Weighted Average Pooling Bridges Pixel-level Localization and Image-level Classification—0
GLoMo: Unsupervised Learning of Transferable Relational Graphs—0
GMM-IL: Image Classification using Incrementally Learnt, Independent Probabilistic Models for Small Sample Sizes—0
GM-MLIC: Graph Matching based Multi-Label Image Classification—0
GM-Net: Learning Features with More Efficiency—0
GmNet: Revisiting Gating Mechanisms From A Frequency View—0
GNN-ViTCap: GNN-Enhanced Multiple Instance Learning with Vision Transformers for Whole Slide Image Classification and Captioning—0
Goal-driven text descriptions for images—0
Goal-Oriented Communication for Edge Learning based on the Information Bottleneck—0
Goal-Oriented Source Coding using LDPC Codes for Compressed-Domain Image Classification—0
Going Deeper Into Face Detection: A Survey—0
Good Artists Copy, Great Artists Steal: Model Extraction Attacks Against Image Translation Models—0
GoogLe2Net: Going Transverse with Convolutions—0
Google is all you need: Semi-Supervised Transfer Learning Strategy For Light Multimodal Multi-Task Classification Model—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