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 5401–5450 of 10420 papers

TitleStatusHype
Transfering Low-Frequency Features for Domain Adaptation—0
Compound Figure Separation of Biomedical Images: Mining Large Datasets for Self-supervised LearningCode0
Weakly Supervised Faster-RCNN+FPN to classify animals in camera trap images—0
Video-based Cross-modal Auxiliary Network for Multimodal Sentiment AnalysisCode0
An efficient and flexible inference system for serving heterogeneous ensembles of deep neural networks—0
Probing Contextual Diversity for Dense Out-of-Distribution DetectionCode0
PanorAMS: Automatic Annotation for Detecting Objects in Urban Context—0
Robustness and invariance properties of image classifiers—0
An Access Control Method with Secret Key for Semantic Segmentation Models—0
Dense Depth Distillation with Out-of-Distribution Simulated Images—0
Constraining Pseudo-label in Self-training Unsupervised Domain Adaptation with Energy-based Model—0
Calibrated Selective ClassificationCode0
gSwin: Gated MLP Vision Model with Hierarchical Structure of Shifted Window—0
Radial Basis Function Networks for Convolutional Neural Networks to Learn Similarity Distance Metric and Improve InterpretabilityCode0
TMIC: App Inventor Extension for the Deployment of Image Classification Models Exported from Teachable Machine—0
Learning from Noisy Labels with Coarse-to-Fine Sample Credibility Modeling—0
Time-lapse image classification using a diffractive neural network—0
PLMCL: Partial-Label Momentum Curriculum Learning for Multi-Label Image Classification—0
Multilayer deep feature extraction for visual texture recognition—0
Towards Calibrated Hyper-Sphere Representation via Distribution Overlap Coefficient for Long-tailed LearningCode0
Minimizing the Effect of Noise and Limited Dataset Size in Image Classification Using Depth Estimation as an Auxiliary Task with Deep Multitask Learning—0
Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language TasksCode0
GCISG: Guided Causal Invariant Learning for Improved Syn-to-real Generalization—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
Revisiting ensembling for improving the performance of deep learning models—0
Effectiveness of Function Matching in Driving Scene Recognition—0
Exploring Adversarial Robustness of Vision Transformers in the Spectral PerspectiveCode0
Improved Image Classification with Token Fusion—0
Test-time Training for Data-efficient UCDRCode0
Quantifying the Knowledge in a DNN to Explain Knowledge Distillation for Classification—0
Resisting Adversarial Attacks in Deep Neural Networks using Diverse Decision Boundaries—0
Local Low-Rank Approximation With Superpixel-Guided Locality Preserving Graph for Hyperspectral Image ClassificationCode0
DLCFT: Deep Linear Continual Fine-Tuning for General Incremental Learning—0
Deep Autoencoder Model Construction Based on Pytorch—0
Conviformers: Convolutionally guided Vision TransformerCode0
Teacher Guided Training: An Efficient Framework for Knowledge Transfer—0
The SVD of Convolutional Weights: A CNN Interpretability Framework—0
Multi-Attribute Open Set RecognitionCode0
Surrogate-assisted Multi-objective Neural Architecture Search for Real-time Semantic Segmentation—0
Entropy Induced Pruning Framework for Convolutional Neural Networks—0
Incoporating Weighted Board Learning System for Accurate Occupational Pneumoconiosis Staging—0
Simulating Personal Food Consumption Patterns using a Modified Markov Chain—0
Dropout is NOT All You Need to Prevent Gradient LeakageCode0
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
The Weighting Game: Evaluating Quality of Explainability MethodsCode0
Self-Knowledge Distillation via Dropout—0
Region-Based Evidential Deep Learning to Quantify Uncertainty and Improve Robustness of Brain Tumor Segmentation—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