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 5501–5550 of 10420 papers

TitleStatusHype
Rectifying Open-set Object Detection: A Taxonomy, Practical Applications, and Proper Evaluation—0
Moment Centralization based Gradient Descent Optimizers for Convolutional Neural NetworksCode0
Few-shot Fine-grained Image Classification via Multi-Frequency Neighborhood and Double-cross ModulationCode0
Multi-manifold Attention for Vision Transformers—0
Fully trainable Gaussian derivative convolutional layerCode0
Residual and Attentional Architectures for Vector-Symbols—0
Consistent Polyhedral Surrogates for Top-k Classification and Variants—0
Improving Deep Neural Network Random Initialization Through Neuronal RewiringCode0
Generative Adversarial Networks Based on Transformer Encoder and Convolution Block for Hyperspectral Image Classification—0
Universal Adaptive Data Augmentation—0
Learning Discriminative Representation via Metric Learning for Imbalanced Medical Image Classification—0
Sound Randomized Smoothing in Floating-Point ArithmeticsCode0
T-RECX: Tiny-Resource Efficient Convolutional neural networks with early-eXit—0
QSAN: A Near-term Achievable Quantum Self-Attention Network—0
Provably Adversarially Robust Nearest Prototype ClassifiersCode0
Work In Progress: Safety and Robustness Verification of Autoencoder-Based Regression Models using the NNV Tool—0
Rich Feature Distillation with Feature Affinity Module for Efficient Image Dehazing—0
Is one annotation enough? A data-centric image classification benchmark for noisy and ambiguous label estimationCode0
Adversarial Robustness Assessment of NeuroEvolution Approaches—0
Image and Model Transformation with Secret Key for Vision Transformer—0
Look-ups are not (yet) all you need for deep learning inferenceCode0
RE-Tagger: A light-weight Real-Estate Image Classifier—0
Adversarial Style Augmentation for Domain Generalized Urban-Scene SegmentationCode0
PCCT: Progressive Class-Center Triplet Loss for Imbalanced Medical Image Classification—0
Adaptive Fine-Grained Predicates Learning for Scene Graph Generation—0
Facilitated machine learning for image-based fruit quality assessmentCode0
Rethinking Persistent Homology for Visual Recognition—0
SiaTrans: Siamese Transformer Network for RGB-D Salient Object Detection with Depth Image Classification—0
Combining Deep Learning with Good Old-Fashioned Machine Learning—0
Pixel-level Correspondence for Self-Supervised Learning from Video—0
On the Subspace Structure of Gradient-Based Meta-Learning—0
Guiding the retraining of convolutional neural networks against adversarial inputs—0
Calibrate to InterpretCode0
An Embedding-Dynamic Approach to Self-supervised Learning—0
Improving Few-Shot Image Classification Using Machine- and User-Generated Natural Language Descriptions—0
A Deep Model for Partial Multi-Label Image Classification with Curriculum Based Disambiguation—0
L_2BN: Enhancing Batch Normalization by Equalizing the L_2 Norms of Features—0
CNN-based Local Vision Transformer for COVID-19 Diagnosis—0
PatchZero: Defending against Adversarial Patch Attacks by Detecting and Zeroing the Patch—0
ST-CoNAL: Consistency-Based Acquisition Criterion Using Temporal Self-Ensemble for Active Learning—0
Vision-and-Language PretrainingCode0
Predicting Out-of-Domain Generalization with Neighborhood Invariance—0
Generalization to translation shifts: a study in architectures and augmentations—0
Understanding and Improving Group NormalizationCode0
Efficient Lung Cancer Image Classification and Segmentation Algorithm Based on Improved Swin Transformer—0
Distilling Ensemble of Explanations for Weakly-Supervised Pre-Training of Image Segmentation ModelsCode0
Portuguese Man-of-War Image Classification with Convolutional Neural Networks—0
DecisioNet: A Binary-Tree Structured Neural NetworkCode0
On Leave-One-Out Conditional Mutual Information For Generalization—0
Unsupervised Cross-Domain Feature Extraction for Single Blood Cell Image ClassificationCode0
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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