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

Showing 23512400 of 10419 papers

TitleStatusHype
I-SplitEE: Image classification in Split Computing DNNs with Early ExitsCode0
Is it Time to Swish? Comparing Deep Learning Activation Functions Across NLP tasksCode0
PCNN: Probable-Class Nearest-Neighbor Explanations Improve Fine-Grained Image Classification Accuracy for AIs and HumansCode0
An Iteratively Optimized Patch Label Inference Network for Automatic Pavement Distress DetectionCode0
IPCL: Iterative Pseudo-Supervised Contrastive Learning to Improve Self-Supervised Feature RepresentationCode0
Investigating Weight-Perturbed Deep Neural Networks With Application in Iris Presentation Attack DetectionCode0
ADVISE: ADaptive Feature Relevance and VISual Explanations for Convolutional Neural NetworksCode0
Investigation of Federated Learning Algorithms for Retinal Optical Coherence Tomography Image Classification with Statistical HeterogeneityCode0
Spurious Feature Eraser: Stabilizing Test-Time Adaptation for Vision-Language Foundation ModelCode0
Enhancing Self-Supervised Learning for Remote Sensing with Elevation Data: A Case Study with Scarce And High Level Semantic LabelsCode0
Invariant Shape Representation Learning For Image ClassificationCode0
Investigating the Corruption Robustness of Image Classifiers with Random Lp-norm CorruptionsCode0
Is it enough to optimize CNN architectures on ImageNet?Code0
Kernel Normalized Convolutional NetworksCode0
Are LSTMs Good Few-Shot Learners?Code0
Invariance encoding in sliced-Wasserstein space for image classification with limited training dataCode0
A Group-Theoretic Framework for Data AugmentationCode0
Invariant backpropagation: how to train a transformation-invariant neural networkCode0
Interpret Your Decision: Logical Reasoning Regularization for Generalization in Visual ClassificationCode0
Interpretable Network Visualizations: A Human-in-the-Loop Approach for Post-hoc Explainability of CNN-based Image ClassificationCode0
Intra-class Patch Swap for Self-DistillationCode0
Interpretable and Interactive Deep Multiple Instance Learning for Dental Caries Classification in Bitewing X-raysCode0
Combined Depth Space based Architecture Search For Person Re-identificationCode0
Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)Code0
A Contrastive Knowledge Transfer Framework for Model Compression and Transfer LearningCode0
InterpNET: Neural Introspection for Interpretable Deep LearningCode0
Adversarial Style Augmentation for Domain Generalized Urban-Scene SegmentationCode0
Adversarial Teacher-Student Representation Learning for Domain GeneralizationCode0
An Intelligent Remote Sensing Image Quality Inspection SystemCode0
ColorNet: Investigating the importance of color spaces for image classificationCode0
ColorNet -- Estimating Colorfulness in Natural ImagesCode0
ColorMAE: Exploring data-independent masking strategies in Masked AutoEncodersCode0
Architectural Vision for Quantum Computing in the Edge-Cloud ContinuumCode0
Integrating kNN with Foundation Models for Adaptable and Privacy-Aware Image ClassificationCode0
Color Channel Perturbation Attacks for Fooling Convolutional Neural Networks and A Defense Against Such AttacksCode0
ARC: Anchored Representation Clouds for High-Resolution INR ClassificationCode0
Intelligent Multi-View Test Time AugmentationCode0
Interferometric Neural NetworksCode0
Instance-dependent Label Distribution Estimation for Learning with Label NoiseCode0
A Rate-Distortion Framework for Explaining Neural Network DecisionsCode0
Instance Temperature Knowledge DistillationCode0
Adversarial Structure Matching for Structured Prediction TasksCode0
Instilling Inductive Biases with SubnetworksCode0
Collaboratively Weighting Deep and Classic Representation via L2 Regularization for Image ClassificationCode0
Multi-head Spatial-Spectral Mamba for Hyperspectral Image ClassificationCode0
Instance Enhancement Batch Normalization: an Adaptive Regulator of Batch NoiseCode0
Interlocking Backpropagation: Improving depthwise model-parallelismCode0
Learning Convolutional Neural Networks using Hybrid Orthogonal Projection and EstimationCode0
A Quantization-Friendly Separable Convolution for MobileNetsCode0
Cold Case: The Lost MNIST DigitsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CoCa (finetuned)Top 1 Accuracy91Unverified
2Model soups (BASIC-L)Top 1 Accuracy90.98Unverified
3Model soups (ViT-G/14)Top 1 Accuracy90.94Unverified
4DaViT-GTop 1 Accuracy90.4Unverified
5Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2Unverified
6DaViT-HTop 1 Accuracy90.2Unverified
7SwinV2-GTop 1 Accuracy90.17Unverified
8MAWS (ViT-6.5B)Top 1 Accuracy90.1Unverified
9Florence-CoSwin-HTop 1 Accuracy90.05Unverified
10RevCol-HTop 1 Accuracy90Unverified