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 7526–7550 of 10420 papers

TitleStatusHype
PRIME: Prioritizing Interpretability in Failure Mode Extraction—0
Constraining Pseudo-label in Self-training Unsupervised Domain Adaptation with Energy-based Model—0
ActiveDC: Distribution Calibration for Active Finetuning—0
Foothill: A Quasiconvex Regularization for Edge Computing of Deep Neural Networks—0
A Computational Account Of Self-Supervised Visual Learning From Egocentric Object Play—0
Fooling Neural Networks for Motion Forecasting via Adversarial Attacks—0
Constrained Low-Rank Learning Using Least Squares-Based Regularization—0
EXPLAINABLE AI-BASED DYNAMIC FILTER PRUNING OF CONVOLUTIONAL NEURAL NETWORKS—0
Privacy-Preserving Collaborative Deep Learning with Unreliable Participants—0
Provably efficient neural network representation for image classification—0
Privacy-preserving Decentralized Aggregation for Federated Learning—0
Explainable AI (XAI) in Image Segmentation in Medicine, Industry, and Beyond: A Survey—0
Privacy Preserving Federated Learning with Convolutional Variational Bottlenecks—0
Privacy-Preserving Federated Learning with Consistency via Knowledge Distillation Using Conditional Generator—0
Privacy-Preserving Image Classification in the Local Setting—0
Privacy-Preserving Image Classification Using Isotropic Network—0
Privacy-Preserving Image Classification Using Vision Transformer—0
Fooling a Real Car with Adversarial Traffic Signs—0
Privacy-preserving Machine Learning for Medical Image Classification—0
A Survey and Evaluation of Adversarial Attacks for Object Detection—0
Privacy-preserving Universal Adversarial Defense for Black-box Models—0
Privacy-Preserving Video Classification with Convolutional Neural Networks—0
Privacy Safe Representation Learning via Frequency Filtering Encoder—0
CNN Based Analysis of the Luria’s Alternating Series Test for Parkinson’s Disease Diagnostics—0
Trade-offs between membership privacy & adversarially robust learning—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