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 2001–2050 of 10419 papers

TitleStatusHype
Transferable Structural Sparse Adversarial Attack Via Exact Group Sparsity TrainingCode1
Bayesian Exploration of Pre-trained Models for Low-shot Image Classification—0
SLICE: Stabilized LIME for Consistent Explanations for Image ClassificationCode0
Not All Classes Stand on Same Embeddings: Calibrating a Semantic Distance with Metric Tensor—0
DiG-IN: Diffusion Guidance for Investigating Networks - Uncovering Classifier Differences Neuron Visualisations and Visual Counterfactual ExplanationsCode1
Circuit Design and Efficient Simulation of Quantum Inner Product and Empirical Studies of Its Effect on Near-Term Hybrid Quantum-Classic Machine LearningCode0
In-distribution Public Data Synthesis with Diffusion Models for Differentially Private Image ClassificationCode0
OmniVec2 - A Novel Transformer based Network for Large Scale Multimodal and Multitask Learning—0
Logarithmic Lenses: Exploring Log RGB Data for Image Classification—0
MultiFusionNet: Multilayer Multimodal Fusion of Deep Neural Networks for Chest X-Ray Image Classification—0
Self-supervised learning for skin cancer diagnosis with limited training dataCode0
Reviving the Context: Camera Trap Species Classification as Link Prediction on Multimodal Knowledge GraphsCode1
Pushing Boundaries: Exploring Zero Shot Object Classification with Large Multimodal Models—0
SSL-OTA: Unveiling Backdoor Threats in Self-Supervised Learning for Object Detection—0
Learning Vision from Models Rivals Learning Vision from DataCode2
RL-LOGO: Deep Reinforcement Learning Localization for Logo Recognition—0
Adversarial Attacks on Image Classification Models: Analysis and Defense—0
Replica Tree-based Federated Learning using Limited DataCode0
MobileVLM : A Fast, Strong and Open Vision Language Assistant for Mobile DevicesCode3
Gemini Pro Defeated by GPT-4V: Evidence from Education—0
Sorting of Smartphone Components for Recycling Through Convolutional Neural Networks—0
Domain Generalization with Vital Phase AugmentationCode0
State-of-the-Art in Nudity Classification: A Comparative AnalysisCode2
Error-free Training for Artificial Neural NetworkCode0
On the Promises and Challenges of Multimodal Foundation Models for Geographical, Environmental, Agricultural, and Urban Planning Applications—0
Sample selection with noise rate estimation in noise learning of medical image analysis—0
Make Me a BNN: A Simple Strategy for Estimating Bayesian Uncertainty from Pre-trained Models—0
Less or More From Teacher: Exploiting Trilateral Geometry For Knowledge Distillation—0
GROOD: Gradient-Aware Out-of-Distribution Detection—0
Federated Learning via Input-Output Collaborative DistillationCode1
TraceFL: Interpretability-Driven Debugging in Federated Learning via Neuron ProvenanceCode1
Joint Sensing and Task-Oriented Communications with Image and Wireless Data Modalities for Dynamic Spectrum Access—0
Q-SENN: Quantized Self-Explaining Neural NetworksCode1
Universal Noise Annotation: Unveiling the Impact of Noisy annotation on Object DetectionCode1
Open-Set: ID Card Presentation Attack Detection using Neural Transfer Style—0
Unlocking Pre-trained Image Backbones for Semantic Image Synthesis—0
Enhancing Neural Training via a Correlated Dynamics Model—0
Testing the Segment Anything Model on radiology data—0
Cached Transformers: Improving Transformers with Differentiable Memory CacheCode1
Integration and Performance Analysis of Artificial Intelligence and Computer Vision Based on Deep Learning Algorithms—0
Near-Optimal Resilient Aggregation Rules for Distributed Learning Using 1-Center and 1-Mean Clustering with OutliersCode0
Unsupervised Segmentation of Colonoscopy Images—0
Unveiling Spaces: Architecturally meaningful semantic descriptions from images of interior spaces—0
Convolutional Channel-wise Competitive Learning for the Forward-Forward AlgorithmCode1
Optimizing Neural Networks with Gradient Lexicase SelectionCode0
Adversarial AutoMixupCode1
I-CEE: Tailoring Explanations of Image Classification Models to User ExpertiseCode0
Delving Deeper Into Astromorphic Transformers—0
Learning Interpretable Queries for Explainable Image Classification with Information Pursuit—0
Semantic-Aware Autoregressive Image Modeling for Visual Representation LearningCode1
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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
5DaViT-HTop 1 Accuracy90.2—Unverified
6Meta Pseudo Labels (EfficientNet-L2)Top 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
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90—Unverified