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 4951–4975 of 10420 papers

TitleStatusHype
Adversarial Robustness Across Representation Spaces—0
Explanations of Classifiers Enhance Medical Image Segmentation via End-to-end Pre-training—0
Explanations for Automatic Speech Recognition—0
Explanation of Unintended Radiated Emission Classification via LIME—0
A Privacy Preserving Method with a Random Orthogonal Matrix for ConvMixer Models—0
Do Explanations Reflect Decisions? A Machine-centric Strategy to Quantify the Performance of Explainability Algorithms—0
CO2: Consistent Contrast for Unsupervised Visual Representation Learning—0
A privacy-preserving method using secret key for convolutional neural network-based speech classification—0
Explaining the Unexplained: Revealing Hidden Correlations for Better Interpretability—0
Explaining the Black-box Smoothly- A Counterfactual Approach—0
CNN with large memory layers—0
Explaining Representation by Mutual Information—0
A Priori Generalizability Estimate for a CNN—0
Explaining non-linear Classifier Decisions within Kernel-based Deep Architectures—0
CNNs with Multi-Level Attention for Domain Generalization—0
CNN: Single-label to Multi-label—0
A priori compression of convolutional neural networks for wave simulators—0
Adversarial Perturbations Against Deep Neural Networks for Malware Classification—0
CNNs Avoid Curse of Dimensionality by Learning on Patches—0
Explaining Deep Convolutional Neural Networks for Image Classification by Evolving Local Interpretable Model-agnostic Explanations—0
A Principled Hierarchical Deep Learning Approach to Joint Image Compression and Classification—0
Explaining Convolutional Neural Networks by Tagging Filters—0
Explaining Clinical Decision Support Systems in Medical Imaging using Cycle-Consistent Activation Maximization—0
A Preliminary Study on Data Augmentation of Deep Learning for Image Classification—0
Adversarial Masked Image Inpainting for Robust Detection of Mpox and Non-Mpox—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