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 9201–9225 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
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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
10RevCol-HTop 1 Accuracy90—Unverified