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 6351–6400 of 10420 papers

TitleStatusHype
Asynchronous Multi-Server Federated Learning for Geo-Distributed Clients—0
Convolutional XGBoost (C-XGBOOST) Model for Brain Tumor Detection—0
MiSuRe is all you need to explain your image segmentation—0
A Graph Neural Network Approach for Product Relationship Prediction—0
Multi-method Integration with Confidence-based Weighting for Zero-shot Image Classification—0
Bridging the Gap between Spatial and Spectral Domains: A Survey on Graph Neural Networks—0
Mitigating Bias: Enhancing Image Classification by Improving Model Explanations—0
Advancing Supervised Local Learning Beyond Classification with Long-term Feature Bank—0
Asynchronous Hierarchical Federated Learning—0
Glasses Detection Using Convolutional Neural Networks—0
Mitochondria-based Renal Cell Carcinoma Subtyping: Learning from Deep vs. Flat Feature Representations—0
MixDefense: A Defense-in-Depth Framework for Adversarial Example Detection Based on Statistical and Semantic Analysis—0
Mixed-Block Neural Architecture Search for Medical Image Segmentation—0
Mixed-Precision Quantized Neural Network with Progressively Decreasing Bitwidth For Image Classification and Object Detection—0
Mixed-Privacy Forgetting in Deep Networks—0
Active Learning Under Malicious Mislabeling and Poisoning Attacks—0
Multimodal Approaches to Fair Image Classification: An Ethical Perspective—0
Mixer: DNN Watermarking using Image Mixup—0
Does Visual Pretraining Help End-to-End Reasoning?—0
Does Saliency-Based Training bring Robustness for Deep Neural Networks in Image Classification?—0
Give Me Your Attention: Dot-Product Attention Considered Harmful for Adversarial Patch Robustness—0
Convolutional Spiking Neural Network for Image Classification—0
GIST: Greedy Independent Set Thresholding for Diverse Data Summarization—0
MixModule: Mixed CNN Kernel Module for Medical Image Segmentation—0
Asynchronous Bioplausible Neuron for SNN for Event Vision—0
Convolutional Patch Representations for Image Retrieval: an Unsupervised Approach—0
GIFAIR-FL: A Framework for Group and Individual Fairness in Federated Learning—0
Multi-loss ensemble deep learning for chest X-ray classification—0
Gibbs Sampling with Low-Power Spiking Digital Neurons—0
Mixture of Experts in Image Classification: What's the Sweet Spot?—0
A Gradient-based Kernel Approach for Efficient Network Architecture Search—0
Mixture of Physical Priors Adapter for Parameter-Efficient Fine-Tuning—0
A GPU-accelerated Algorithm for Distinct Discriminant Canonical Correlation Network—0
Multi-manifold Attention for Vision Transformers—0
GhostNetV3: Exploring the Training Strategies for Compact Models—0
Achieving Generalizable Robustness of Deep Neural Networks by Stability Training—0
Ghost Loss to Question the Reliability of Training Data—0
Mix-up Self-Supervised Learning for Contrast-agnostic Applications—0
Mixup-Transformer: Dynamic Data Augmentation for NLP Tasks—0
Multi-level Residual Networks from Dynamical Systems View—0
ML Attack Models: Adversarial Attacks and Data Poisoning Attacks—0
Multilingual Image Corpus: Annotation Protocol—0
MLIP: Enhancing Medical Visual Representation with Divergence Encoder and Knowledge-guided Contrastive Learning—0
MLP-ASR: Sequence-length agnostic all-MLP architectures for speech recognition—0
MLP-based architecture with variable length input for automatic speech recognition—0
G-EvoNAS: Evolutionary Neural Architecture Search Based on Network Growth—0
Getting ViT in Shape: Scaling Laws for Compute-Optimal Model Design—0
Get Rid Of Your Trail: Remotely Erasing Backdoors in Federated Learning—0
Convolutional Neural Networks from Image Markers—0
Multilingual Image Corpus – Towards a Multimodal and Multilingual Dataset—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