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 9001–9050 of 10420 papers

TitleStatusHype
Examining the Proximity of Adversarial Examples to Class Manifolds in Deep Networks—0
Example-Based Explainable AI and its Application for Remote Sensing Image Classification—0
Exemplar Based Deep Discriminative and Shareable Feature Learning for Scene Image Classification—0
Exemplar-free Online Continual Learning—0
Exemplar SVMs as Visual Feature Encoders—0
ExMobileViT: Lightweight Classifier Extension for Mobile Vision Transformer—0
Expanding Training Data for Endoscopic Phenotyping of Eosinophilic Esophagitis—0
ExpandNets: Linear Over-parameterization to Train Compact Convolutional Networks—0
Experimental Observations of the Topology of Convolutional Neural Network Activations—0
Expert-Like Reparameterization of Heterogeneous Pyramid Receptive Fields in Efficient CNNs for Fair Medical Image Classification—0
Experts Don't Cheat: Learning What You Don't Know By Predicting Pairs—0
Explainability-aided Domain Generalization for Image Classification—0
Explainability and Robustness of Deep Visual Classification Models—0
Explainable 3D Convolutional Neural Networks by Learning Temporal Transformations—0
EXPLAINABLE AI-BASED DYNAMIC FILTER PRUNING OF CONVOLUTIONAL NEURAL NETWORKS—0
Explainable AI for medical imaging: Explaining pneumothorax diagnoses with Bayesian Teaching—0
Explainable AI (XAI) in Image Segmentation in Medicine, Industry, and Beyond: A Survey—0
Explainable Analysis of Deep Learning Methods for SAR Image Classification—0
Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models—0
Explainable Disease Classification via weakly-supervised segmentation—0
Explainable Image Classification with Evidence Counterfactual—0
Explainable Knowledge Distillation for On-device Chest X-Ray Classification—0
Explainable Metric Learning for Deflating Data Bias—0
Explainable unsupervised multi-modal image registration using deep networks—0
Explainers in the Wild: Making Surrogate Explainers Robust to Distortions through Perception—0
Explaining Black-box Model Predictions via Two-level Nested Feature Attributions with Consistency Property—0
Explaining Clinical Decision Support Systems in Medical Imaging using Cycle-Consistent Activation Maximization—0
Explaining Convolutional Neural Networks by Tagging Filters—0
Explaining Deep Convolutional Neural Networks for Image Classification by Evolving Local Interpretable Model-agnostic Explanations—0
Explaining non-linear Classifier Decisions within Kernel-based Deep Architectures—0
Explaining Representation by Mutual Information—0
Explaining the Black-box Smoothly- A Counterfactual Approach—0
Explaining the Unexplained: Revealing Hidden Correlations for Better Interpretability—0
Do Explanations Reflect Decisions? A Machine-centric Strategy to Quantify the Performance of Explainability Algorithms—0
Explanation of Unintended Radiated Emission Classification via LIME—0
Explanations for Automatic Speech Recognition—0
Explanations of Classifiers Enhance Medical Image Segmentation via End-to-end Pre-training—0
Explanatory Masks for Neural Network Interpretability—0
Explicit Connection Distillation—0
Explicit Domain Adaptation with Loosely Coupled Samples—0
Explicitly Modeled Attention Maps for Image Classification—0
Exploiting Activation based Gradient Output Sparsity to Accelerate Backpropagation in CNNs—0
Exploiting Category Names for Few-Shot Classification with Vision-Language Models—0
Exploiting Contextual Uncertainty of Visual Data for Efficient Training of Deep Models—0
Exploiting Image-trained CNN Architectures for Unconstrained Video Classification—0
Exploiting Kernel Compression on BNNs—0
Exploiting LMM-based knowledge for image classification tasks—0
Exploiting Local Features from Deep Networks for Image Retrieval—0
Exploiting Nontrivial Connectivity for Automatic Speech Recognition—0
Exploiting Patch Sizes and Resolutions for Multi-Scale Deep Learning in Mammogram 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