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 726–750 of 10419 papers

TitleStatusHype
Heuristical Comparison of Vision Transformers Against Convolutional Neural Networks for Semantic Segmentation on Remote Sensing ImageryCode0
SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision TransformersCode0
Computed tomography using meta-optics—0
Efficient Whole Slide Image Classification through Fisher Vector Representation—0
ScaleNet: Scale Invariance Learning in Directed GraphsCode0
Semantic segmentation on multi-resolution optical and microwave data using deep learning—0
HMIL: Hierarchical Multi-Instance Learning for Fine-Grained Whole Slide Image ClassificationCode1
Can KAN Work? Exploring the Potential of Kolmogorov-Arnold Networks in Computer Vision—0
ScaleKD: Strong Vision Transformers Could Be Excellent TeachersCode2
Deep Active Learning in the Open World—0
Exploring Structural Nonlinearity in Binary Polariton-Based Neuromorphic Architectures—0
Mutual-energy inner product optimization method for constructing feature coordinates and image classification in Machine Learning—0
AI-Compass: A Comprehensive and Effective Multi-module Testing Tool for AI Systems—0
GCI-ViTAL: Gradual Confidence Improvement with Vision Transformers for Active Learning on Label Noise—0
Training objective drives the consistency of representational similarity across datasetsCode1
FisherMask: Enhancing Neural Network Labeling Efficiency in Image Classification Using Fisher InformationCode0
Visual-TCAV: Concept-based Attribution and Saliency Maps for Post-hoc Explainability in Image ClassificationCode0
Saliency Assisted Quantization for Neural Networks—0
Zero-Shot Temporal Resolution Domain Adaptation for Spiking Neural Networks—0
Neural Fingerprints for Adversarial Attack DetectionCode0
Attention Masks Help Adversarial Attacks to Bypass Safety DetectorsCode0
Is network fragmentation a useful complexity measure?—0
Multimodal Structure-Aware Quantum Data ProcessingCode0
RaVL: Discovering and Mitigating Spurious Correlations in Fine-Tuned Vision-Language ModelsCode1
Deferred Poisoning: Making the Model More Vulnerable via Hessian Singularization—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