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 9201–9250 of 10420 papers

TitleStatusHype
FedSLD: Federated Learning with Shared Label Distribution for Medical Image Classification—0
FedSN: A Federated Learning Framework over Heterogeneous LEO Satellite Networks—0
FedWSIDD: Federated Whole Slide Image Classification via Dataset Distillation—0
Feedback Control for Online Training of Neural Networks—0
Feedforward Legendre Memory Unit—0
FENAS: Flexible and Expressive Neural Architecture Search—0
FermiNets: Learning generative machines to generate efficient neural networks via generative synthesis—0
Ferrograph image classification—0
FETCH: A Memory-Efficient Replay Approach for Continual Learning in Image Classification—0
Few-Shot Action Recognition with Compromised Metric via Optimal Transport—0
Few-shot Action Recognition with Implicit Temporal Alignment and Pair Similarity Optimization—0
Few-shot Algorithm Assurance—0
Few-Shot Classification & Segmentation Using Large Language Models Agent—0
Few-shot crack image classification using clip based on bayesian optimization—0
Few-Shot Histopathology Image Classification: Evaluating State-of-the-Art Methods and Unveiling Performance Insights—0
Few-Shot Hyperspectral Image Classification With Unknown Classes Using Multitask Deep Learning—0
Few-Shot Image Classification Along Sparse Graphs—0
Few-Shot Image Classification and Segmentation as Visual Question Answering Using Vision-Language Models—0
Few-shot Image Classification based on Gradual Machine Learning—0
Few-Shot Image Classification via Contrastive Self-Supervised Learning—0
Few-shot Image Classification with Multi-Facet Prototypes—0
Few-Shot Learning Approach on Tuberculosis Classification Based on Chest X-Ray Images—0
A Multi-stage Transfer Learning Framework for Diabetic Retinopathy Grading on Small Data—0
Few-shot Learning for Domain-specific Fine-grained Image Classification—0
Few-Shot Learning of Compact Models via Task-Specific Meta Distillation—0
Few-shot medical image classification with simple shape and texture text descriptors using vision-language models—0
Few-Shot Non-Parametric Learning with Deep Latent Variable Model—0
Few-shot Unsupervised Domain Adaptation with Image-to-class Sparse Similarity Encoding—0
FHIST: A Benchmark for Few-shot Classification of Histological Images—0
FickleNet: Weakly and Semi-supervised Semantic Image Segmentation using Stochastic Inference—0
Fighting Fire with Fire: Avoiding DNN Shortcuts through Priming—0
FILM: How can Few-Shot Image Classification Benefit from Pre-Trained Language Models?—0
Filter Distribution Templates in Convolutional Networks for Image Classification Tasks—0
Finding Better Topologies for Deep Convolutional Neural Networks by Evolution—0
Finding Optimal Kernel Size and Dimension in Convolutional Neural Networks An Architecture Optimization Approach—0
Finding Original Image Of A Sub Image Using CNNs—0
Fine-graind Image Classification via Combining Vision and Language—0
Fine-grained Classification of Solder Joints with α-skew Jensen-Shannon Divergence—0
Fine-Grained Classification via Mixture of Deep Convolutional Neural Networks—0
Fine-grained Discriminative Localization via Saliency-guided Faster R-CNN—0
Fine-Grained Few Shot Learning with Foreground Object Transformation—0
Fine-grained Image Classification by Exploring Bipartite-Graph Labels—0
Fine-Grained Image Classification via Combining Vision and Language—0
Fine-Grained Neural Architecture Search—0
Fine-Grained Recognition as HSnet Search for Informative Image Parts—0
Fine-Grained Sports, Yoga, and Dance Postures Recognition: A Benchmark Analysis—0
Fine-to-coarse Knowledge Transfer For Low-Res Image Classification—0
Fine-tuning Convolutional Neural Networks for fine art classification—0
Fine-Tuning DARTS for Image Classification—0
Top-Tuning: a study on transfer learning for an efficient alternative to fine tuning for image classification with fast kernel methods—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