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 5901–5950 of 10420 papers

TitleStatusHype
Attention Enables Zero Approximation Error—0
Mixed-Block Neural Architecture Search for Medical Image Segmentation—0
Deep Learning Reproducibility and Explainable AI (XAI)—0
Efficient and Differentiable Conformal Prediction with General Function ClassesCode0
Retrieval Augmented Classification for Long-Tail Visual Recognition—0
Cut and Continuous Paste towards Real-time Deep Fall Detection—0
Rethinking the Zigzag Flattening for Image Reading—0
On the Implicit Bias Towards Minimal Depth of Deep Neural Networks—0
Out of Distribution Data Detection Using Dropout Bayesian Neural Networks—0
Differentially Private Federated Learning via Inexact ADMM with Multiple Local Updates—0
Rethinking Pareto Frontier for Performance Evaluation of Deep Neural Networks—0
EBHI:A New Enteroscope Biopsy Histopathological H&E Image Dataset for Image Classification Evaluation—0
Dynamic Object Comprehension: A Framework For Evaluating Artificial Visual Perception—0
MLP-ASR: Sequence-length agnostic all-MLP architectures for speech recognition—0
Generalizable Information Theoretic Causal Representation—0
Two-stage architectural fine-tuning with neural architecture search using early-stopping in image classification—0
Unified smoke and fire detection in an evolutionary framework with self-supervised progressive data augment—0
Applying adversarial networks to increase the data efficiency and reliability of Self-Driving Cars—0
Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision—0
Measuring Unintended Memorisation of Unique Private Features in Neural Networks—0
Auxiliary Cross-Modal Representation Learning with Triplet Loss Functions for Online Handwriting Recognition—0
Meta Knowledge Distillation—0
A precortical module for robust CNNs to light variations—0
Improving the repeatability of deep learning models with Monte Carlo dropoutCode0
A Theory of PAC Learnability under Transformation Invariances—0
Prior Gradient Mask Guided Pruning-Aware Fine-TuningCode0
Discriminability-enforcing loss to improve representation learning—0
Wukong: A 100 Million Large-scale Chinese Cross-modal Pre-training BenchmarkCode0
BViT: Broad Attention based Vision TransformerCode0
Excitement Surfeited Turns to Errors: Deep Learning Testing Framework Based on Excitable NeuronsCode0
Fuzzy Pooling—0
Exemplar-free Online Continual Learning—0
Feature-level augmentation to improve robustness of deep neural networks to affine transformations—0
Multi-relation Message Passing for Multi-label Text ClassificationCode0
Spherical Transformer—0
Improving greedy core-set configurations for active learning with uncertainty-scaled distances—0
Multi-Label Classification of Thoracic Diseases using Dense Convolutional Network on Chest RadiographsCode0
TransformNet: Self-supervised representation learning through predicting geometric transformationsCode0
Comparative Study Between Distance Measures On Supervised Optimum-Path Forest ClassificationCode0
Equivariance versus Augmentation for Spherical ImagesCode0
Methods for the frugal labeler: Multi-class semantic segmentation on heterogeneous labelsCode0
Modeling Structure with Undirected Neural NetworksCode0
If a Human Can See It, So Should Your System: Reliability Requirements for Machine Vision Components—0
Data Consistency for Weakly Supervised Learning—0
OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning FrameworkCode0
Corrupted Image Modeling for Self-Supervised Visual Pre-Training—0
Simple Control Baselines for Evaluating Transfer Learning—0
Decision boundaries and convex hulls in the feature space that deep learning functions learn from images—0
Choosing an Appropriate Platform and Workflow for Processing Camera Trap Data using Artificial Intelligence—0
Learning with Neighbor Consistency for Noisy Labels—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
5DaViT-HTop 1 Accuracy90.2—Unverified
6Meta Pseudo Labels (EfficientNet-L2)Top 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
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90—Unverified