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 801–850 of 10419 papers

TitleStatusHype
Development of CNN Architectures using Transfer Learning Methods for Medical Image Classification—0
KANICE: Kolmogorov-Arnold Networks with Interactive Convolutional ElementsCode0
Altogether: Image Captioning via Re-aligning Alt-text—0
Frontiers in Intelligent ColonoscopyCode2
Domain-Adaptive Pre-training of Self-Supervised Foundation Models for Medical Image Classification in Gastrointestinal EndoscopyCode0
Efficient Neural Network Training via Subset Pretraining—0
Visual Representation Learning Guided By Multi-modal Prior Knowledge—0
Bayesian Concept Bottleneck Models with LLM PriorsCode0
ViMoE: An Empirical Study of Designing Vision Mixture-of-Experts—0
P-YOLOv8: Efficient and Accurate Real-Time Detection of Distracted Driving—0
AutoTrain: No-code training for state-of-the-art modelsCode7
Open-vocabulary vs. Closed-set: Best Practice for Few-shot Object Detection Considering Text DescribabilityCode0
Visual Navigation of Digital Libraries: Retrieval and Classification of Images in the National Library of Norway's Digitised Book CollectionCode0
Spatial-Mamba: Effective Visual State Space Models via Structure-Aware State FusionCode2
Reflexive Guidance: Improving OoDD in Vision-Language Models via Self-Guided Image-Adaptive Concept Generation—0
On the Influence of Shape, Texture and Color for Learning Semantic Segmentation—0
Comparative Evaluation of Clustered Federated Learning MethodsCode0
How Do Training Methods Influence the Utilization of Vision Models?Code0
A Hybrid Feature Fusion Deep Learning Framework for Leukemia Cancer Detection in Microscopic Blood Sample Using Gated Recurrent Unit and Uncertainty Quantification—0
Reproducibility study of "LICO: Explainable Models with Language-Image Consistency"Code0
Performance of Gaussian Mixture Model Classifiers on Embedded Feature SpacesCode0
Augmentation Policy Generation for Image Classification Using Large Language Models—0
LoLDU: Low-Rank Adaptation via Lower-Diag-Upper Decomposition for Parameter-Efficient Fine-TuningCode0
Is Less More? Exploring Token Condensation as Training-free Adaptation for CLIPCode1
Interpreting and Analysing CLIP's Zero-Shot Image Classification via Mutual KnowledgeCode1
PND-Net: Plant Nutrition Deficiency and Disease Classification using Graph Convolutional Network—0
Feature Augmentation for Self-supervised Contrastive Learning: A Closer Look—0
Efficiera Residual Networks: Hardware-Friendly Fully Binary Weight with 2-bit Activation Model Achieves Practical ImageNet AccuracyCode0
SGLP: A Similarity Guided Fast Layer Partition Pruning for Compressing Large Deep ModelsCode0
Towards Better Multi-head Attention via Channel-wise Sample PermutationCode0
Towards a More Complete Theory of Function Preserving Transforms—0
GlobalMamba: Global Image Serialization for Vision MambaCode1
Will the Inclusion of Generated Data Amplify Bias Across Generations in Future Image Classification Models?—0
Ensemble of ConvNeXt V2 and MaxViT for Long-Tailed CXR Classification with View-Based AggregationCode0
big.LITTLE Vision Transformer for Efficient Visual Recognition—0
SkillAggregation: Reference-free LLM-Dependent Aggregation—0
Enhancing JEPAs with Spatial Conditioning: Robust and Efficient Representation Learning—0
Provably Reliable Conformal Prediction Sets in the Presence of Data Poisoning—0
Robust 3D Point Clouds Classification based on Declarative DefendersCode1
Understanding Robustness of Parameter-Efficient Tuning for Image ClassificationCode0
Deep Transfer Learning: Model Framework and Error Analysis—0
Diabetic retinopathy image classification method based on GreenBen data augmentation—0
EG-SpikeFormer: Eye-Gaze Guided Transformer on Spiking Neural Networks for Medical Image Analysis—0
Cross-Domain Evaluation of Few-Shot Classification Models: Natural Images vs. Histopathological Images—0
Accelerated Distributed Stochastic Non-Convex Optimization over Time-Varying Directed Networks—0
Efficient Hyperparameter Importance Assessment for CNNs—0
Semantic Token Reweighting for Interpretable and Controllable Text Embeddings in CLIP—0
DeBiFormer: Vision Transformer with Deformable Agent Bi-level Routing AttentionCode1
Frequency-Temporal Attention Network for Remote Sensing Imagery Change DetectionCode0
Bilinear MLPs enable weight-based mechanistic interpretabilityCode1
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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