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 9551–9600 of 10420 papers

TitleStatusHype
Highly Efficient Forward and Backward Propagation of Convolutional Neural Networks for Pixelwise Classification—0
Highly Efficient Representation and Active Learning Framework and Its Application to Imbalanced Medical Image Classification—0
High Performance Human Face Recognition using Independent High Intensity Gabor Wavelet Responses: A Statistical Approach—0
High Performance Hyperspectral Image Classification using Graphics Processing Units—0
High Quality Remote Sensing Image Super-Resolution Using Deep Memory Connected Network—0
Hilbert Curve Based Molecular Sequence Analysis—0
Hilbert Sinkhorn Divergence for Optimal Transport—0
HindSight: A Graph-Based Vision Model Architecture For Representing Part-Whole Hierarchies—0
Hippocampus Temporal Lobe Epilepsy Detection using a Combination of Shape-based Features and Spherical Harmonics Representation—0
HistoFS: Non-IID Histopathologic Whole Slide Image Classification via Federated Style Transfer with RoI-Preserving—0
Histograms of Pattern Sets for Image Classification and Object Recognition—0
Histopathological Image Classification and Vulnerability Analysis using Federated Learning—0
Histopathology Image Classification using Deep Manifold Contrastive Learning—0
Historical Test-time Prompt Tuning for Vision Foundation Models—0
HMIC: Hierarchical Medical Image Classification, A Deep Learning Approach—0
HOG feature extraction from encrypted images for privacy-preserving machine learning—0
Homogenizing Non-IID datasets via In-Distribution Knowledge Distillation for Decentralized Learning—0
How adversarial attacks can disrupt seemingly stable accurate classifiers—0
How Compact?: Assessing Compactness of Representations through Layer-Wise Pruning—0
How do Convolutional Neural Networks Learn Design?—0
How Does Diverse Interpretability of Textual Prompts Impact Medical Vision-Language Zero-Shot Tasks?—0
How does promoting the minority fraction affect generalization? A theoretical study of the one-hidden-layer neural network on group imbalance—0
How does self-supervised pretraining improve robustness against noisy labels across various medical image classification datasets?—0
How do Hyenas deal with Human Speech? Speech Recognition and Translation with ConfHyena—0
How good is my GAN?—0
How many classifiers do we need?—0
How much data is needed to train a medical image deep learning system to achieve necessary high accuracy?—0
How Much Is Hidden in the NAS Benchmarks? Few-Shot Adaptation of a NAS Predictor—0
How Much Off-The-Shelf Knowledge Is Transferable From Natural Images To Pathology Images?—0
How Quality Affects Deep Neural Networks in Fine-Grained Image Classification—0
How stable are Transferability Metrics evaluations?—0
How to Adapt Your Large-Scale Vision-and-Language Model—0
How to augment your ViTs? Consistency loss and StyleAug, a random style transfer augmentation—0
How to distribute data across tasks for meta-learning?—0
How To Overcome Confirmation Bias in Semi-Supervised Image Classification By Active Learning—0
How Training Data Affect the Accuracy and Robustness of Neural Networks for Image Classification—0
How Transferable Are Self-supervised Features in Medical Image Classification Tasks?—0
HQViT: Hybrid Quantum Vision Transformer for Image Classification—0
HSI-BERT: Hyperspectral Image Classification Using the Bidirectional Encoder Representation From Transformers—0
HSVLT: Hierarchical Scale-Aware Vision-Language Transformer for Multi-Label Image Classification—0
Hu-Fu: Hardware and Software Collaborative Attack Framework against Neural Networks—0
Human Action Recognition in Still Images Using ConViT—0
Human Action Recognition using Factorized Spatio-Temporal Convolutional Networks—0
Human-aligned Deep Learning: Explainability, Causality, and Biological Inspiration—0
Human Attention-Guided Explainable Artificial Intelligence for Computer Vision Models—0
Human-Centered Evaluation of XAI Methods—0
Human Face Recognition using Gabor based Kernel Entropy Component Analysis—0
Human Imperceptible Attacks and Applications to Improve Fairness—0
Human-interpretable model explainability on high-dimensional data—0
Understanding More about Human and Machine Attention in Deep Neural Networks—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