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 6851–6900 of 10420 papers

TitleStatusHype
Fixing the Teacher-Student Knowledge Discrepancy in Distillation—0
Exploiting Invariance in Training Deep Neural NetworksCode0
DAP: Detection-Aware Pre-training with Weak SupervisionCode0
"Weak AI" is Likely to Never Become "Strong AI", So What is its Greatest Value for us?—0
Classifying Video based on Automatic Content Detection Overview—0
Capsule Network is Not More Robust than Convolutional Network—0
Automating Defense Against Adversarial Attacks: Discovery of Vulnerabilities and Application of Multi-INT Imagery to Protect Deployed Models—0
Data Augmentation in a Hybrid Approach for Aspect-Based Sentiment AnalysisCode0
Selective Output Smoothing Regularization: Regularize Neural Networks by Softening Output Distributions—0
Explaining Representation by Mutual Information—0
BA^2M: A Batch Aware Attention Module for Image Classification—0
On the benefits of robust models in modulation recognition—0
Going Deeper Into Face Detection: A Survey—0
Explore the Knowledge contained in Network Weights to Obtain Sparse Neural Networks—0
Unsupervised Robust Domain Adaptation without Source Data—0
Understanding Robustness of Transformers for Image Classification—0
Contrastive Learning based Hybrid Networks for Long-Tailed Image Classification—0
Preserve, Promote, or Attack? GNN Explanation via Topology Perturbation—0
A Comprehensive Review of Image Analysis Methods for Microorganism Counting: From Classical Image Processing to Deep Learning Approaches—0
ECINN: Efficient Counterfactuals from Invertible Neural NetworksCode0
Spatial-spectral Hyperspectral Image Classification via Multiple Random Anchor Graphs Ensemble Learning—0
Self-Supervised Training Enhances Online Continual Learning—0
Factors of Influence for Transfer Learning across Diverse Appearance Domains and Task Types—0
W2WNet: a two-module probabilistic Convolutional Neural Network with embedded data cleansing functionality—0
EPRNet: Efficient Pyramid Representation Network for Real-Time Street Scene SegmentationCode0
Enhanced Gradient for Differentiable Architecture Search—0
Characterizing and Improving the Robustness of Self-Supervised Learning through Background Augmentations—0
Deep Neural Networks Learn Meta-Structures from Noisy Labels in Semantic Segmentation—0
ScanMix: Learning from Severe Label Noise via Semantic Clustering and Semi-Supervised LearningCode0
Hierarchical Representation based Query-Specific Prototypical Network for Few-Shot Image Classification—0
Transfer learning for automatic brain tumor classification Using MRI Images.—0
Variational Knowledge Distillation for Disease Classification in Chest X-Rays—0
Sewer-ML: A Multi-Label Sewer Defect Classification Dataset and BenchmarkCode0
ThanosNet: A Novel Trash Classification Method Using MetadataCode0
Implementation of Artificial Neural Networks for the Nepta-Uranian Interplanetary (NUIP) Mission—0
ConViT: Improving Vision Transformers with Soft Convolutional Inductive BiasesCode0
Stride and Translation Invariance in CNNs—0
TPPI-Net: Towards Efficient and Practical Hyperspectral Image Classification—0
Quantitative Performance Assessment of CNN Units via Topological Entropy Calculation—0
Large-Scale Zero-Shot Image Classification from Rich and Diverse Textual Descriptions—0
HAMIL: Hierarchical Aggregation-Based Multi-Instance Learning for Microscopy Image Classification—0
Adversarial YOLO: Defense Human Detection Patch Attacks via Detecting Adversarial Patches—0
Distributed Deep Learning Using Volunteer Computing-Like Paradigm—0
Learning Hyperbolic Representations of Topological FeaturesCode0
Learned Gradient Compression for Distributed Deep Learning—0
Is it enough to optimize CNN architectures on ImageNet?Code0
Reweighting Augmented Samples by Minimizing the Maximal Expected LossCode0
Evolving parametrized Loss for Image Classification Learning on Small Datasets—0
Sampling-free Variational Inference for Neural Networks with Multiplicative Activation Noise—0
Distance Metric-Based Learning with Interpolated Latent Features for Location Classification in Endoscopy Image and Video—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