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 7901–7950 of 10420 papers

TitleStatusHype
Reinforcement Explanation Learning—0
Reinforcement Learning Approach to Active Learning for Image Classification—0
Fine-Grained Neural Architecture Search—0
Conditional Networks—0
Reinforcement Learning Platform for Adversarial Black-box Attacks with Custom Distortion Filters—0
Reinforcing Generated Images via Meta-learning for One-Shot Fine-Grained Visual Recognition—0
Reinforcing Medical Image Classifier to Improve Generalization on Small Datasets—0
Conditionally Deep Hybrid Neural Networks Across Edge and Cloud—0
Faster and Accurate Classification for JPEG2000 Compressed Images in Networked Applications—0
Relating Regularization and Generalization through the Intrinsic Dimension of Activations—0
Fine-Grained Image Classification via Combining Vision and Language—0
Fine-grained Image Classification by Exploring Bipartite-Graph Labels—0
Faster Inference of Integer SWIN Transformer by Removing the GELU Activation—0
WaveMamba: Spatial-Spectral Wavelet Mamba for Hyperspectral Image Classification—0
Conditional Consistency Regularization for Semi-Supervised Multi-label Image Classification—0
Fine-Grained Few Shot Learning with Foreground Object Transformation—0
Faster Training by Selecting Samples Using Embeddings—0
Relaxed Attention for Transformer Models—0
Fine-grained Discriminative Localization via Saliency-guided Faster R-CNN—0
Fine-Grained Classification via Mixture of Deep Convolutional Neural Networks—0
Relearning Forgotten Knowledge: on Forgetting, Overfit and Training-Free Ensembles of DNNs—0
Relevance Prediction from Eye-movements Using Semi-interpretable Convolutional Neural Networks—0
Conditional Graphical Lasso for Multi-Label Image Classification—0
Relevant-features based Auxiliary Cells for Energy Efficient Detection of Natural Errors—0
Fast Fourier Convolution Based Remote Sensor Image Object Detection for Earth Observation—0
Fine-grained Classification of Solder Joints with α-skew Jensen-Shannon Divergence—0
Conditional Autoregressors are Interpretable Classifiers—0
Fine-graind Image Classification via Combining Vision and Language—0
Activation by Interval-wise Dropout: A Simple Way to Prevent Neural Networks from Plasticity Loss—0
Fast and reliable uncertainty quantification with neural network ensembles for industrial image classification—0
Finding Original Image Of A Sub Image Using CNNs—0
Finding Optimal Kernel Size and Dimension in Convolutional Neural Networks An Architecture Optimization Approach—0
Finding Better Topologies for Deep Convolutional Neural Networks by Evolution—0
Generating and Detecting True Ambiguity: A Forgotten Danger in DNN Supervision Testing—0
Resampled Datasets Are Not Enough: Mitigating Societal Bias Beyond Single Attributes—0
ResFeats: Residual Network Based Features for Image Classification—0
Representation Memorization for Fast Learning New Knowledge without Forgetting—0
Remote Sensing Image Classification Using Convolutional Neural Network (CNN) and Transfer Learning Techniques—0
Remote Sensing Image Classification with Large Scale Gaussian Processes—0
Remote Sensing Image Classification with Decoupled Knowledge Distillation—0
Representation Synthesis by Probabilistic Many-Valued Logic Operation in Self-Supervised Learning—0
Remote Sensing Vision-Language Foundation Models without Annotations via Ground Remote Alignment—0
Filter Distribution Templates in Convolutional Networks for Image Classification Tasks—0
A Foreground Inference Network for Video Surveillance Using Multi-View Receptive Field—0
How Good is your Explanation? Algorithmic Stability Measures to Assess the Quality of Explanations for Deep Neural Networks—0
Visual Tree Convolutional Neural Network in Image Classification—0
FILM: How can Few-Shot Image Classification Benefit from Pre-Trained Language Models?—0
FAST OBJECT LOCALIZATION VIA SENSITIVITY ANALYSIS—0
Concurrent Neural Tree and Data Preprocessing AutoML for Image Classification—0
Fighting Fire with Fire: Avoiding DNN Shortcuts through Priming—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
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90—Unverified