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 4351–4400 of 10420 papers

TitleStatusHype
Human Activity Recognition Based on Wearable Sensor Data: A Standardization of the State-of-the-ArtCode0
Human-imperceptible, Machine-recognizable ImagesCode0
Fractional Max-PoolingCode0
FractalNet: Ultra-Deep Neural Networks without ResidualsCode0
Gall Bladder Cancer Detection from US Images with Only Image Level LabelsCode0
Assisted Perception: Optimizing Observations to Communicate StateCode0
Game of Gradients: Mitigating Irrelevant Clients in Federated LearningCode0
Drop Clause: Enhancing Performance, Interpretability and Robustness of the Tsetlin MachineCode0
Context-Aware Compilation of DNN Training Pipelines across Edge and CloudCode0
Fixed-Point Convolutional Neural Network for Real-Time Video Processing in FPGACode0
Contrastive-Based Deep Embeddings for Label Noise-Resilient Histopathology Image ClassificationCode0
HSI-CNN: A Novel Convolution Neural Network for Hyperspectral ImageCode0
Human-in-the-Loop Visual Re-ID for Population Size EstimationCode0
Hyperspectral Image Classification via Sparse Representation With Incremental DictionariesCode0
Gated Convolutional Networks with Hybrid Connectivity for Image ClassificationCode0
Gated Linear NetworksCode0
How transfer learning is used in generative models for image classification: improved accuracyCode0
How to Use Dropout Correctly on Residual Networks with Batch NormalizationCode0
Fourier Analysis on Robustness of Graph Convolutional Neural Networks for Skeleton-based Action RecognitionCode0
Gaussian-Based Pooling for Convolutional Neural NetworksCode0
Foundation Model Makes Clustering A Better Initialization For Cold-Start Active LearningCode0
Fossil Image Identification using Deep Learning Ensembles of Data Augmented MultiviewsCode0
Enhancing Cross-task Transferability of Adversarial Examples with Dispersion ReductionCode0
SynerMix: Synergistic Mixup Solution for Enhanced Intra-Class Cohesion and Inter-Class Separability in Image ClassificationCode0
How Flawed Is ECE? An Analysis via Logit SmoothingCode0
How Do Training Methods Influence the Utilization of Vision Models?Code0
Why Random Pruning Is All We Need to Start SparseCode0
Constructing Multiple Tasks for Augmentation: Improving Neural Image Classification With K-means FeaturesCode0
Forget Vectors at Play: Universal Input Perturbations Driving Machine Unlearning in Image ClassificationCode0
Contrastive Learning for OOD in Object detectionCode0
Histopathological Image Classification based on Self-Supervised Vision Transformer and Weak LabelsCode0
Contrastive Learning for Predicting Cancer Prognosis Using Gene Expression ValuesCode0
Histopathological Image Classification using Discriminative Feature-oriented Dictionary LearningCode0
ISLE: An Intelligent Streaming Framework for High-Throughput AI Inference in Medical ImagingCode0
Use HiResCAM instead of Grad-CAM for faithful explanations of convolutional neural networksCode0
Forced Spatial Attention for Driver Foot Activity ClassificationCode0
Ablation study of self-supervised learning for image classificationCode0
General Greedy De-bias LearningCode0
High Performance Offline Handwritten Chinese Character Recognition Using GoogLeNet and Directional Feature MapsCode0
Histogram Layers for Neural Engineered FeaturesCode0
HOLMES: HOLonym-MEronym based Semantic inspection for Convolutional Image ClassifiersCode0
ForAug: Recombining Foregrounds and Backgrounds to Improve Vision Transformer Training with Bias MitigationCode0
Assessing Sample Quality via the Latent Space of Generative ModelsCode0
Improving the Gating Mechanism of Recurrent Neural NetworksCode0
Assessing The Impact of CNN Auto Encoder-Based Image Denoising on Image Classification TasksCode0
High Definition image classification in Geoscience using Machine LearningCode0
High-fidelity Pseudo-labels for Boosting Weakly-Supervised SegmentationCode0
Constrained Linear Data-feature Mapping for Image ClassificationCode0
Food Image Recognition by Using Convolutional Neural Networks (CNNs)Code0
Hierarchical Mask-Enhanced Dual Reconstruction Network for Few-Shot Fine-Grained Image ClassificationCode0
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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