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 8651–8700 of 10420 papers

TitleStatusHype
Cycle-Consistent Adversarial GAN: the integration of adversarial attack and defense—0
Unrestricted Adversarial Examples via Semantic ManipulationCode0
Generating Minimal Adversarial Perturbations with Integrated Adaptive Gradients—0
A New Loss Function for CNN Classifier Based on Pre-defined Evenly-Distributed Class CentroidsCode0
An Analysis of Pre-Training on Object Detection—0
Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave ConvolutionCode0
Active Multi-Kernel Domain Adaptation for Hyperspectral Image Classification—0
CondConv: Conditionally Parameterized Convolutions for Efficient InferenceCode0
Weakly Supervised Learning of Instance Segmentation with Inter-pixel RelationsCode1
Towards Analyzing Semantic Robustness of Deep Neural NetworksCode0
SWNet: Small-World Neural Networks and Rapid Convergence—0
PUNCH: Positive UNlabelled Classification based information retrieval in Hyperspectral imagesCode0
Automated Search for Configurations of Deep Neural Network ArchitecturesCode0
A Target-Agnostic Attack on Deep Models: Exploiting Security Vulnerabilities of Transfer LearningCode0
SoDeep: a Sorting Deep net to learn ranking loss surrogatesCode0
WeNet: Weighted Networks for Recurrent Network Architecture Search—0
Simultaneous Spectral-Spatial Feature Selection and Extraction for Hyperspectral Images—0
Large Margin Multi-modal Multi-task Feature Extraction for Image Classification—0
Filter Pruning by Switching to Neighboring CNNs with Good Attributes—0
Decomposition-Based Transfer Distance Metric Learning for Image Classification—0
Multi-view Vector-valued Manifold Regularization for Multi-label Image Classification—0
Improving Image Classification Robustness through Selective CNN-Filters Fine-Tuning—0
Multi-View Matrix Completion for Multi-Label Image Classification—0
Learning Metrics from Teachers: Compact Networks for Image EmbeddingCode0
ANTNets: Mobile Convolutional Neural Networks for Resource Efficient Image Classification—0
Adaptively Connected Neural NetworksCode0
C2S2: Cost-aware Channel Sparse Selection for Progressive Network Pruning—0
Split Batch Normalization: Improving Semi-Supervised Learning under Domain Shift—0
Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4 HoursCode0
Relation-Aware Global Attention for Person Re-identificationCode0
A Hybrid Approach with Optimization and Metric-based Meta-Learner for Few-Shot Learning—0
On Direct Distribution Matching for Adapting Segmentation Networks—0
Active Transfer Learning Network: A Unified Deep Joint Spectral-Spatial Feature Learning Model For Hyperspectral Image Classification—0
Video Classification with Channel-Separated Convolutional NetworksCode0
Understanding the efficacy, reliability and resiliency of computer vision techniques for malware detection and future research directions—0
Hyperbolic Image EmbeddingsCode1
Correlation Congruence for Knowledge DistillationCode0
A Comprehensive Overhaul of Feature DistillationCode0
Exploring Randomly Wired Neural Networks for Image RecognitionCode0
Looking back at Labels: A Class based Domain Adaptation TechniqueCode0
Augmented Neural ODEsCode1
C2AE: Class Conditioned Auto-Encoder for Open-set Recognition—0
Res2Net: A New Multi-scale Backbone ArchitectureCode1
Significance-aware Information Bottleneck for Domain Adaptive Semantic Segmentation—0
Weakly Supervised Object Detection with Segmentation Collaboration—0
Variational Adversarial Active LearningCode1
IMAE for Noise-Robust Learning: Mean Absolute Error Does Not Treat Examples Equally and Gradient Magnitude's Variance MattersCode1
Deep Convolutional Spiking Neural Networks for Image Classification—0
Revisiting Local Descriptor based Image-to-Class Measure for Few-shot LearningCode0
Zero-shot Image Recognition Using Relational Matching, Adaptation and Calibration—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