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 6301–6325 of 10420 papers

TitleStatusHype
Multi-branch CNN and grouping cascade attention for medical image classification—0
Methods for Segmentation and Classification of Digital Microscopy Tissue Images—0
Diving into Optimization of Topology in Neural Networks—0
Metric-Based Few-Shot Learning for Video Action Recognition—0
A Systematic Comparison of Encrypted Machine Learning Solutions for Image Classification—0
MetricOpt: Learning to Optimize Black-Box Evaluation Metrics—0
CopilotCAD: Empowering Radiologists with Report Completion Models and Quantitative Evidence from Medical Image Foundation Models—0
Using Sum-Product Networks to Assess Uncertainty in Deep Active Learning—0
Multi-branch fusion network for hyperspectral image classification—0
MFNet: Multi-class Few-shot Segmentation Network with Pixel-wise Metric Learning—0
Multi-Class Unlearning for Image Classification via Weight Filtering—0
Multi-concept adversarial attacks—0
GLoMo: Unsupervised Learning of Transferable Relational Graphs—0
Global Weighted Average Pooling Bridges Pixel-level Localization and Image-level Classification—0
DM-CT: Consistency Training with Data and Model Perturbation—0
MiAMix: Enhancing Image Classification through a Multi-stage Augmented Mixed Sample Data Augmentation Method—0
MIAShield: Defending Membership Inference Attacks via Preemptive Exclusion of Members—0
Asynchronous SGD without gradient delay for efficient distributed training—0
Global-to-Local Support Spectrums for Language Model Explainability—0
Microscopic fine-grained instance classification through deep attention—0
Do Better ImageNet Models Transfer Better?—0
Microstructure quality control of steels using deep learning—0
Global Meets Local: Effective Multi-Label Image Classification via Category-Aware Weak Supervision—0
MTSpark: Enabling Multi-Task Learning with Spiking Neural Networks for Generalist Agents—0
Global Interaction Modelling in Vision Transformer via Super Tokens—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