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

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Showing 9301–9350 of 10420 papers

TitleStatusHype
Two Methods for Domain Adaptation of Bilingual Tasks: Delightfully Simple and Broadly ApplicableCode0
Fractional Wavelet Scattering Network and Applications—0
A Benchmark for Interpretability Methods in Deep Neural NetworksCode0
Understanding Dropout as an Optimization Trick—0
Generating Counterfactual Explanations with Natural Language—0
Constructing Deep Neural Networks by Bayesian Network Structure Learning—0
Defending Malware Classification Networks Against Adversarial Perturbations with Non-Negative Weight Restrictions—0
Considerations for a PAP Smear Image Analysis System with CNN Features—0
Pixel-level Reconstruction and Classification for Noisy Handwritten Bangla Characters—0
Finding Original Image Of A Sub Image Using CNNs—0
Inference of Quantized Neural Networks on Heterogeneous All-Programmable Devices—0
A convex method for classification of groups of examples—0
DPP-Net: Device-aware Progressive Search for Pareto-optimal Neural Architectures—0
Maximally Invariant Data Perturbation as ExplanationCode0
Classification of remote sensing images using attribute profiles and feature profiles from different trees: a comparative study—0
Non-Negative Networks Against Adversarial AttacksCode0
Recurrent Multiresolution Convolutional Networks for VHR Image Classification—0
Image classification and retrieval with random depthwise signed convolutional neural networksCode0
Three dimensional Deep Learning approach for remote sensing image classification—0
NetScore: Towards Universal Metrics for Large-scale Performance Analysis of Deep Neural Networks for Practical On-Device Edge Usage—0
GLoMo: Unsupervisedly Learned Relational Graphs as Transferable RepresentationsCode0
Efficient Active Learning for Image Classification and Segmentation using a Sample Selection and Conditional Generative Adversarial Network—0
Crowd-Powered Data Mining—0
Benchmarks for Image Classification and Other High-dimensional Pattern Recognition Problems—0
Human Activity Recognition Based on Wearable Sensor Data: A Standardization of the State-of-the-ArtCode0
Knowledge Distillation by On-the-Fly Native EnsembleCode0
Dual Pattern Learning Networks by Empirical Dual Prediction Risk Minimization—0
Improving Whole Slide Segmentation Through Visual Context - A Systematic Study—0
A Systematic Evaluation of Recent Deep Learning Architectures for Fine-Grained Vehicle ClassificationCode0
Revisiting Adversarial Risk—0
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
Exploring Feature Reuse in DenseNet Architectures—0
Deep Gaussian Processes with Convolutional Kernels—0
Factorized Adversarial Networks for Unsupervised Domain Adaptation—0
Meta-Learner with Linear Nulling—0
Between Progress and Potential Impact of AI: the Neglected Dimensions—0
A Novel Framework for Recurrent Neural Networks with Enhancing Information Processing and Transmission between Units—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
Tandem Blocks in Deep Convolutional Neural Networks—0
A Network Architecture for Point Cloud Classification via Automatic Depth Images Generation—0
Adapted and Oversegmenting Graphs: Application to Geometric Deep Learning—0
Analyzing Filters Toward Efficient ConvNet—0
Analytic Expressions for Probabilistic Moments of PL-DNN With Gaussian Input—0
Classification-Driven Dynamic Image Enhancement—0
CLIP-Q: Deep Network Compression Learning by In-Parallel Pruning-Quantization—0
Coupled End-to-End Transfer Learning With Generalized Fisher Information—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