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 9251–9300 of 10420 papers

TitleStatusHype
Dimensionality-Driven Learning with Noisy LabelsCode0
Path-Level Network Transformation for Efficient Architecture SearchCode0
Training Faster by Separating Modes of Variation in Batch-normalized ModelsCode0
Revisiting Adversarial Risk—0
Deep Gaussian Processes with Convolutional Kernels—0
Exploring Feature Reuse in DenseNet Architectures—0
Meta-Learner with Linear Nulling—0
Factorized Adversarial Networks for Unsupervised Domain Adaptation—0
A Novel Framework for Recurrent Neural Networks with Enhancing Information Processing and Transmission between Units—0
Between Progress and Potential Impact of AI: the Neglected Dimensions—0
Sufficient Conditions for Idealised Models to Have No Adversarial Examples: a Theoretical and Empirical Study with Bayesian Neural Networks—0
Webly Supervised Learning Meets Zero-Shot Learning: A Hybrid Approach for Fine-Grained Classification—0
The Power of Ensembles for Active Learning in Image Classification—0
Classification-Driven Dynamic Image Enhancement—0
Analyzing Filters Toward Efficient ConvNet—0
Analytic Expressions for Probabilistic Moments of PL-DNN With Gaussian Input—0
Coupled End-to-End Transfer Learning With Generalized Fisher Information—0
A Network Architecture for Point Cloud Classification via Automatic Depth Images Generation—0
HydraNets: Specialized Dynamic Architectures for Efficient Inference—0
CLIP-Q: Deep Network Compression Learning by In-Parallel Pruning-Quantization—0
OLÉ: Orthogonal Low-Rank Embedding - A Plug and Play Geometric Loss for Deep LearningCode0
Generating Image Captions in Arabic using Root-Word Based Recurrent Neural Networks and Deep Neural Networks—0
IGCV3: Interleaved Low-Rank Group Convolutions for Efficient Deep Neural NetworksCode0
Accurate and Efficient Similarity Search for Large Scale Face Recognition—0
Adapted and Oversegmenting Graphs: Application to Geometric Deep Learning—0
Tandem Blocks in Deep Convolutional Neural Networks—0
Rotation Equivariance and Invariance in Convolutional Neural NetworksCode0
Multiaccuracy: Black-Box Post-Processing for Fairness in ClassificationCode0
Adversarial Attacks on Face Detectors using Neural Net based Constrained Optimization—0
Learning multiple non-mutually-exclusive tasks for improved classification of inherently ordered labels—0
Multi-function Convolutional Neural Networks for Improving Image Classification Performance—0
Deep Learning under Privileged Information Using Heteroscedastic DropoutCode0
Learning From Less Data: Diversified Subset Selection and Active Learning in Image Classification Tasks—0
CapsNet comparative performance evaluation for image classification—0
Improving the Resolution of CNN Feature Maps Efficiently with MultisamplingCode1
Fast Dynamic Routing Based on Weighted Kernel Density EstimationCode0
Adversarial Examples in Remote Sensing—0
Object-Level Representation Learning for Few-Shot Image Classification—0
Accelerating CNN inference on FPGAs: A Survey—0
Calibrating Deep Convolutional Gaussian ProcessesCode0
Transductive Label Augmentation for Improved Deep Network Learning—0
AutoAugment: Learning Augmentation Policies from DataCode3
Do Better ImageNet Models Transfer Better?—0
Non-convex non-local flows for saliency detection—0
Transfer Learning for Illustration ClassificationCode0
Hyperspectral image classification via a random patches networkCode0
Unsupervised Domain Adaptation using Regularized Hyper-graph Matching—0
"Why Should I Trust Interactive Learners?" Explaining Interactive Queries of Classifiers to Users—0
Masking: A New Perspective of Noisy SupervisionCode0
Improving CNN classifiers by estimating test-time priors—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