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 2601–2650 of 10419 papers

TitleStatusHype
Data-Centric Diet: Effective Multi-center Dataset Pruning for Medical Image Segmentation—0
PerceptionCLIP: Visual Classification by Inferring and Conditioning on ContextsCode1
Fine-Grained Sports, Yoga, and Dance Postures Recognition: A Benchmark Analysis—0
NormKD: Normalized Logits for Knowledge DistillationCode1
Addressing Uncertainty in Imbalanced Histopathology Image Classification of HER2 Breast Cancer: An interpretable Ensemble Approach with Threshold Filtered Single Instance Evaluation (SIE)—0
Beyond One-Hot-Encoding: Injecting Semantics to Drive Image ClassifiersCode0
Gradient Scaling on Deep Spiking Neural Networks with Spike-Dependent Local Information—0
DINO-CXR: A self supervised method based on vision transformer for chest X-ray classification—0
ViT2EEG: Leveraging Hybrid Pretrained Vision Transformers for EEG DataCode1
Automated COVID-19 CT Image Classification using Multi-head Channel Attention in Deep CNN—0
T-Fusion Net: A Novel Deep Neural Network Augmented with Multiple Localizations based Spatial Attention Mechanisms for Covid-19 Detection—0
Hybrid quantum transfer learning for crack image classification on NISQ hardware—0
CDUL: CLIP-Driven Unsupervised Learning for Multi-Label Image ClassificationCode1
L3DMC: Lifelong Learning using Distillation via Mixed-Curvature SpaceCode0
Class-Specific Distribution Alignment for Semi-Supervised Medical Image Classification—0
Cross-dimensional transfer learning in medical image segmentation with deep learningCode0
Task-Oriented Channel Attention for Fine-Grained Few-Shot ClassificationCode1
Few-shot Image Classification based on Gradual Machine Learning—0
Multiple Instance Learning Framework with Masked Hard Instance Mining for Whole Slide Image ClassificationCode1
Cross-Modal Concept Learning and Inference for Vision-Language Models—0
Quantum-limited stochastic optical neural networks operating at a few quanta per activationCode1
Detection of Children Abuse by Voice and Audio Classification by Short-Time Fourier Transform Machine Learning implemented on Nvidia Edge GPU device—0
PromptStyler: Prompt-driven Style Generation for Source-free Domain GeneralizationCode1
Self-Supervised Learning for Improved Synthetic Aperture Sonar Target Recognition—0
Text-guided Foundation Model Adaptation for Pathological Image ClassificationCode1
Mixture of Self-Supervised LearningCode0
Understanding Silent Failures in Medical Image ClassificationCode1
Contrastive Knowledge Amalgamation for Unsupervised Image Classification—0
Federated Model Aggregation via Self-Supervised Priors for Highly Imbalanced Medical Image ClassificationCode1
ECO: Ensembling Context Optimization for Vision-Language Models—0
MiDaS v3.1 -- A Model Zoo for Robust Monocular Relative Depth Estimation—0
Exploring the Sharpened Cosine Similarity—0
On the unreasonable vulnerability of transformers for image restoration -- and an easy fix—0
Overcoming Distribution Mismatch in Quantizing Image Super-Resolution NetworksCode0
Compact & Capable: Harnessing Graph Neural Networks and Edge Convolution for Medical Image Classification—0
Maximal Independent Sets for Pooling in Graph Neural Networks—0
Few-shot 1/a Anomalies Feedback : Damage Vision Mining Opportunity and Embedding Feature Imbalance—0
Concept-based explainability for an EEG transformer modelCode0
Towards a Visual-Language Foundation Model for Computational Pathology—0
An X3D Neural Network Analysis for Runner's Performance Assessment in a Wild Sporting Environment—0
An Intelligent Remote Sensing Image Quality Inspection SystemCode0
Sparse then Prune: Toward Efficient Vision TransformersCode0
GEM: Boost Simple Network for Glass Surface Segmentation via Vision Foundation ModelsCode1
Why Is Prompt Tuning for Vision-Language Models Robust to Noisy Labels?Code1
CLR: Channel-wise Lightweight Reprogramming for Continual LearningCode1
GIST: Generating Image-Specific Text for Fine-grained Object ClassificationCode1
Tuning Pre-trained Model via Moment ProbingCode1
Quantized Feature Distillation for Network Quantization—0
Deep learning for classification of noisy QR codes—0
FedSoup: Improving Generalization and Personalization in Federated Learning via Selective Model InterpolationCode1
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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
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90—Unverified