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 7551–7600 of 10420 papers

TitleStatusHype
Fooling Neural Networks for Motion Forecasting via Adversarial Attacks—0
Constrained Low-Rank Learning Using Least Squares-Based Regularization—0
Pushing the Limits of Narrow Precision Inferencing at Cloud Scale with Microsoft Floating Point—0
Explainable Knowledge Distillation for On-device Chest X-Ray Classification—0
Explainable Metric Learning for Deflating Data Bias—0
CNN-based Local Vision Transformer for COVID-19 Diagnosis—0
Fooling a Real Car with Adversarial Traffic Signs—0
Probabilistic Label Trees for Efficient Large Scale Image Classification—0
A Survey and Evaluation of Adversarial Attacks for Object Detection—0
Probabilistic Model-Based Dynamic Architecture Search—0
Probabilistic Spatial Analysis in Quantitative Microscopy with Uncertainty-Aware Cell Detection using Deep Bayesian Regression of Density Maps—0
Explainers in the Wild: Making Surrogate Explainers Robust to Distortions through Perception—0
Pushing Joint Image Denoising and Classification to the Edge—0
Probability Guided Loss for Long-Tailed Multi-Label Image Classification—0
Food Image Classification and Segmentation with Attention-based Multiple Instance Learning—0
Explaining Black-box Model Predictions via Two-level Nested Feature Attributions with Consistency Property—0
Pushing Boundaries: Exploring Zero Shot Object Classification with Large Multimodal Models—0
Probing Network Decisions: Capturing Uncertainties and Unveiling Vulnerabilities Without Label Information—0
Probing the Efficacy of Federated Parameter-Efficient Fine-Tuning of Vision Transformers for Medical Image Classification—0
Problem-dependent attention and effort in neural networks with applications to image resolution and model selection—0
Pushing the envelope in deep visual recognition for mobile platforms—0
Pushing the Limits of Radiology with Joint Modeling of Visual and Textual Information—0
P-YOLOv8: Efficient and Accurate Real-Time Detection of Distracted Driving—0
ProD: Prompting-To-Disentangle Domain Knowledge for Cross-Domain Few-Shot Image Classification—0
Product Sparse Coding—0
PROFIT: A Specialized Optimizer for Deep Fine Tuning—0
Quantifying the Knowledge in a DNN to Explain Knowledge Distillation for Classification—0
Progressive Class-based Expansion Learning For Image Classification—0
CNNs Avoid Curse of Dimensionality by Learning on Patches—0
Progressively Select and Reject Pseudo-labelled Samples for Open-Set Domain Adaptation—0
Quanv4EO: Empowering Earth Observation by means of Quanvolutional Neural Networks—0
Progressive Meta-Pooling Learning for Lightweight Image Classification Model—0
Food Classification using Joint Representation of Visual and Textual Data—0
Assessing Robustness to Noise: Low-Cost Head CT Triage—0
Generalized Coarse-to-Fine Visual Recognition with Progressive Training—0
CNN: Single-label to Multi-label—0
Projecting Trouble: Light Based Adversarial Attacks on Deep Learning Classifiers—0
ProjectionNet: Learning Efficient On-Device Deep Networks Using Neural Projections—0
Projective Skip-Connections for Segmentation Along a Subset of Dimensions in Retinal OCT—0
Explaining non-linear Classifier Decisions within Kernel-based Deep Architectures—0
Active Data Discovery: Mining Unknown Data using Submodular Information Measures—0
FolkTalent: Enhancing Classification and Tagging of Indian Folk Paintings—0
Explaining Representation by Mutual Information—0
Prompt-Driven Dynamic Object-Centric Learning for Single Domain Generalization—0
Constrained deep neural network architecture search for IoT devices accounting for hardware calibration—0
Prompt-Guided Adaptive Model Transformation for Whole Slide Image Classification—0
PSO-PS: Parameter Synchronization with Particle Swarm Optimization for Distributed Training of Deep Neural Networks—0
Constrained deep neural network architecture search for IoT devices accounting hardware calibration—0
Focusing on the Big Picture: Insights into a Systems Approach to Deep Learning for Satellite Imagery—0
Focused Active Learning for Histopathological 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