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 4001–4050 of 10420 papers

TitleStatusHype
SAFE: Sensitivity-Aware Features for Out-of-Distribution Object DetectionCode1
An Access Control Method with Secret Key for Semantic Segmentation Models—0
Constraining Pseudo-label in Self-training Unsupervised Domain Adaptation with Energy-based Model—0
Disentangle and Remerge: Interventional Knowledge Distillation for Few-Shot Object Detection from A Conditional Causal PerspectiveCode1
Dense Depth Distillation with Out-of-Distribution Simulated Images—0
Calibrated Selective ClassificationCode0
Supervised Dimensionality Reduction and Image Classification Utilizing Convolutional AutoencodersCode1
gSwin: Gated MLP Vision Model with Hierarchical Structure of Shifted Window—0
TMIC: App Inventor Extension for the Deployment of Image Classification Models Exported from Teachable Machine—0
Radial Basis Function Networks for Convolutional Neural Networks to Learn Similarity Distance Metric and Improve InterpretabilityCode0
Learning from Noisy Labels with Coarse-to-Fine Sample Credibility Modeling—0
Time-lapse image classification using a diffractive neural network—0
Minimizing the Effect of Noise and Limited Dataset Size in Image Classification Using Depth Estimation as an Auxiliary Task with Deep Multitask Learning—0
PLMCL: Partial-Label Momentum Curriculum Learning for Multi-Label Image Classification—0
Multilayer deep feature extraction for visual texture recognition—0
GCISG: Guided Causal Invariant Learning for Improved Syn-to-real Generalization—0
Towards Calibrated Hyper-Sphere Representation via Distribution Overlap Coefficient for Long-tailed LearningCode0
Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language TasksCode0
Revisiting ensembling for improving the performance of deep learning models—0
Byzantines can also Learn from History: Fall of Centered Clipping in Federated Learning—0
DiscrimLoss: A Universal Loss for Hard Samples and Incorrect Samples Discrimination—0
Effectiveness of Function Matching in Driving Scene Recognition—0
Exploring Adversarial Robustness of Vision Transformers in the Spectral PerspectiveCode0
Net2Brain: A Toolbox to compare artificial vision models with human brain responsesCode1
Test-time Training for Data-efficient UCDRCode0
Improved Image Classification with Token Fusion—0
Local Low-Rank Approximation With Superpixel-Guided Locality Preserving Graph for Hyperspectral Image ClassificationCode0
Resisting Adversarial Attacks in Deep Neural Networks using Diverse Decision Boundaries—0
Quantifying the Knowledge in a DNN to Explain Knowledge Distillation for Classification—0
Conviformers: Convolutionally guided Vision TransformerCode0
DLCFT: Deep Linear Continual Fine-Tuning for General Incremental Learning—0
Deep Autoencoder Model Construction Based on Pytorch—0
Your ViT is Secretly a Hybrid Discriminative-Generative Diffusion ModelCode1
Teacher Guided Training: An Efficient Framework for Knowledge Transfer—0
Multi-Attribute Open Set RecognitionCode0
The SVD of Convolutional Weights: A CNN Interpretability Framework—0
Surrogate-assisted Multi-objective Neural Architecture Search for Real-time Semantic Segmentation—0
Shuffle Instances-based Vision Transformer for Pancreatic Cancer ROSE Image ClassificationCode1
Incoporating Weighted Board Learning System for Accurate Occupational Pneumoconiosis Staging—0
Entropy Induced Pruning Framework for Convolutional Neural Networks—0
Simulating Personal Food Consumption Patterns using a Modified Markov Chain—0
Dropout is NOT All You Need to Prevent Gradient LeakageCode0
The Weighting Game: Evaluating Quality of Explainability MethodsCode0
BEiT v2: Masked Image Modeling with Vector-Quantized Visual TokenizersCode0
Scale-free and Task-agnostic Attack: Generating Photo-realistic Adversarial Patterns with Patch Quilting Generator—0
Contrastive Learning for OOD in Object detectionCode0
MixSKD: Self-Knowledge Distillation from Mixup for Image RecognitionCode1
Region-Based Evidential Deep Learning to Quantify Uncertainty and Improve Robustness of Brain Tumor Segmentation—0
Shifted Windows Transformers for Medical Image Quality Assessment—0
WeightMom: Learning Sparse Networks using Iterative Momentum-based pruning—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