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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 8601–8650 of 10420 papers

TitleStatusHype
DISCO: Dynamic and Invariant Sensitive Channel Obfuscation for deep neural networks—0
Generating Hard Examples for Pixel-wise Classification—0
Discovering Discriminative Cell Attributes for HEp-2 Specimen Image Classification—0
Discovering Fine-Grained Visual-Concept Relations by Disentangled Optimal Transport Concept Bottleneck Models—0
Discovering Influential Neuron Path in Vision Transformers—0
Discovering Parametric Activation Functions—0
Discrete Simulation Optimization for Tuning Machine Learning Method Hyperparameters—0
Discrete Wavelet Transform-Based Capsule Network for Hyperspectral Image Classification—0
Discriminability-enforcing loss to improve representation learning—0
Discriminative and Geometry Aware Unsupervised Domain Adaptation—0
Discriminative Distillation to Reduce Class Confusion in Continual Learning—0
Discriminative k-shot learning using probabilistic models—0
Discriminative Label Consistent Domain Adaptation—0
Discriminative Learning of Sum-Product Networks—0
Discriminative Localization in CNNs for Weakly-Supervised Segmentation of Pulmonary Nodules—0
Discriminative models for robust image classification—0
Discriminative Nonlinear Analysis Operator Learning: When Cosparse Model Meets Image Classification—0
Discriminative Pattern Mining for Breast Cancer Histopathology Image Classification via Fully Convolutional Autoencoder—0
Discriminative Robust Deep Dictionary Learning for Hyperspectral Image Classification—0
Discriminative Transfer Learning with Tree-based Priors—0
DiscrimLoss: A Universal Loss for Hard Samples and Incorrect Samples Discrimination—0
Disentangled Deep Autoencoding Regularization for Robust Image Classification—0
Disentangling CLIP for Multi-Object Perception—0
Disentangling Visual Transformers: Patch-level Interpretability for Image Classification—0
Disguised-Nets: Image Disguising for Privacy-preserving Outsourced Deep Learning—0
Disrupting Model Merging: A Parameter-Level Defense Without Sacrificing Accuracy—0
Disrupting Model Training with Adversarial Shortcuts—0
Dissonance Between Human and Machine Understanding—0
Distance-based Composable Representations with Neural Networks—0
Distangling Biological Noise in Cellular Images with a focus on Explainability—0
Distant Domain Transfer Learning for Medical Imaging—0
DistilDoc: Knowledge Distillation for Visually-Rich Document Applications—0
StableMamba: Distillation-free Scaling of Large SSMs for Images and Videos—0
Distillation from heterogeneous unlabeled collections—0
Distilling High Diagnostic Value Patches for Whole Slide Image Classification Using Attention Mechanism—0
Distilling Knowledge into Quantum Vision Transformers for Biomedical Image Classification—0
Distilling Self-Supervised Vision Transformers for Weakly-Supervised Few-Shot Classification & Segmentation—0
Distilling Spikes: Knowledge Distillation in Spiking Neural Networks—0
Distortion Robust Image Classification using Deep Convolutional Neural Network with Discrete Cosine Transform—0
Distributed Deep Learning Using Volunteer Computing-Like Paradigm—0
Distributed Federated Learning-Based Deep Learning Model for Privacy MRI Brain Tumor Detection—0
GTAdam: Gradient Tracking with Adaptive Momentum for Distributed Online Optimization—0
Distributed stochastic optimization for deep learning (thesis)—0
Distributed Training of Deep Neural Networks with Theoretical Analysis: Under SSP Setting—0
Distributed Transfer Learning with 4th Gen Intel Xeon Processors—0
Distribution Adaptive INT8 Quantization for Training CNNs—0
Task-Robust Model-Agnostic Meta-Learning—0
Distributional loss for convolutional neural network regression and application to GNSS multi-path estimation—0
Robust Multi-instance Learning with Stable Instances—0
Distributionally Robust Optimization and Invariant Representation Learning for Addressing Subgroup Underrepresentation: Mechanisms and Limitations—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