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 6251–6300 of 10420 papers

TitleStatusHype
Reliable Probability Intervals For Classification Using Inductive Venn Predictors Based on Distance Learning—0
On the Privacy Risks of Deploying Recurrent Neural Networks in Machine Learning Models—0
Test-time Batch Statistics Calibration for Covariate Shift—0
Seed Classification using Synthetic Image Datasets Generated from Low-Altitude UAV Imagery—0
Spectral Bias in Practice: The Role of Function Frequency in Generalization—0
Adversarial Attacks on Black Box Video Classifiers: Leveraging the Power of Geometric TransformationsCode0
WDCCNet: Weighted Double-Classifier Constraint Neural Network for Mammographic Image Classification—0
Effectiveness of Optimization Algorithms in Deep Image ClassificationCode0
Improving Axial-Attention Network Classification via Cross-Channel Weight SharingCode0
Stochastic Anderson Mixing for Nonconvex Stochastic Optimization—0
HYPPO: A Surrogate-Based Multi-Level Parallelism Tool for Hyperparameter Optimization—0
Automated Seed Quality Testing System using GAN & Active LearningCode0
Perturbated Gradients Updating within Unit Space for Deep LearningCode0
Transferability Estimation for Semantic Segmentation Task—0
Towards Better Data Augmentation using Wasserstein Distance in Variational Auto-encoder—0
Compositional Training for End-to-End Deep AUC Maximization—0
UFO-ViT: High Performance Linear Vision Transformer without SoftmaxCode0
FedBABU: Toward Enhanced Representation for Federated Image Classification—0
Feature Kernel Distillation—0
Are Vision Transformers Robust to Patch-wise Perturbations?—0
AAVAE: Augmentation-Augmented Variational Autoencoders—0
SVMnet: Non-parametric image classification based on convolutional SVM ensembles for small training sets—0
Towards Unknown-aware Learning with Virtual Outlier Synthesis—0
A precortical module for robust CNNs to light variations—0
Self-supervised Models are Good Teaching Assistants for Vision Transformers—0
EXPLAINABLE AI-BASED DYNAMIC FILTER PRUNING OF CONVOLUTIONAL NEURAL NETWORKS—0
Clustered Task-Aware Meta-Learning by Learning from Learning PathsCode0
Revisiting Linear Decision Boundaries for Few-Shot Learning with Transformer Hypernetworks—0
Evaluating Language-biased image classification based on semantic compositionality—0
Ontology-Driven Semantic Alignment of Artificial Neurons and Visual Concepts—0
Chest X-Rays Image Classification from beta-Variational Autoencoders Latent Features—0
Noise-Contrastive Variational Information Bottleneck Networks—0
Causally Focused Convolutional Networks Through Minimal Human Guidance—0
Use of small auxiliary networks and scarce data to improve the adversarial robustness of deep learning models—0
An Investigation on Hardware-Aware Vision Transformer Scaling—0
UNCERTAINTY QUANTIFICATION USING VARIATIONAL INFERENCE FOR BIOMEDICAL IMAGE SEGMENTATION—0
A Dot Product Attention Free Transformer—0
On the Convergence of Nonconvex Continual Learning with Adaptive Learning Rate—0
Does deep learning model calibration improve performance in class-imbalanced medical image classification?—0
DM-CT: Consistency Training with Data and Model Perturbation—0
Meta-OLE: Meta-learned Orthogonal Low-Rank Embedding—0
Diurnal or Nocturnal? Federated Learning of Multi-branch Networks from Periodically Shifting Distributions—0
Measuring the Interpretability of Unsupervised Representations via Quantized Reversed Probing—0
Measuring the Effectiveness of Self-Supervised Learning using Calibrated Learning Curves—0
MDFL: A UNIFIED FRAMEWORK WITH META-DROPOUT FOR FEW-SHOT LEARNING—0
Sphere2Vec: Self-Supervised Location Representation Learning on Spherical Surfaces—0
Sequence Approximation using Feedforward Spiking Neural Network for Spatiotemporal Learning: Theory and Optimization Methods—0
Mako: Semi-supervised continual learning with minimal labeled data via data programming—0
m-mix: Generating hard negatives via multiple samples mixing for contrastive learning—0
Self-Supervised Prime-Dual Networks for Few-Shot Image Classification—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