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 9026–9050 of 10420 papers

TitleStatusHype
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
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
10RevCol-HTop 1 Accuracy90—Unverified