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 6826–6850 of 10420 papers

TitleStatusHype
Neural Networks Designing Neural Networks: Multi-Objective Hyper-Parameter Optimization—0
Efficient Computation of Quantized Neural Networks by −1, +1 Encoding Decomposition—0
CE-Dedup: Cost-Effective Convolutional Neural Nets Training based on Image Deduplication—0
Efficient Converted Spiking Neural Network for 3D and 2D Classification—0
Selective Synthetic Augmentation with Quality Assurance—0
A Survey of Neural Network Robustness Assessment in Image Recognition—0
Efficient Deep Learning Infrastructures for Embedded Computing Systems: A Comprehensive Survey and Future Envision—0
Agglomerative Token Clustering—0
Generalizable multi-task, multi-domain deep segmentation of sparse pediatric imaging datasets via multi-scale contrastive regularization and multi-joint anatomical priors—0
Generalizable Information Theoretic Causal Representation—0
Generalisation First, Memorisation Second? Memorisation Localisation for Natural Language Classification Tasks—0
A Survey of Malware Detection Using Deep Learning—0
NeurIPS'22 Cross-Domain MetaDL competition: Design and baseline results—0
NeuRN: Neuro-inspired Domain Generalization for Image Classification—0
Efficient Deep Representation Learning by Adaptive Latent Space Sampling—0
Active Learning for Multi-class Image Classification—0
NeuroMask: Explaining Predictions of Deep Neural Networks through Mask Learning—0
Contrastive Learning for Regression on Hyperspectral Data—0
General Adversarial Defense via Pixel Level and Feature Level Distribution Alignment—0
General Adversarial Defense Against Black-box Attacks via Pixel Level and Feature Level Distribution Alignments—0
New Benchmark for Household Garbage Image Recognition—0
A Survey of Graph and Attention Based Hyperspectral Image Classification Methods for Remote Sensing Data—0
On fine-tuning of Autoencoders for Fuzzy rule classifiers—0
On Focal Loss for Class-Posterior Probability Estimation: A Theoretical Perspective—0
Gemini Pro Defeated by GPT-4V: Evidence from Education—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