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 6851–6900 of 10420 papers

TitleStatusHype
LANCE: Efficient Low-Precision Quantized Winograd Convolution for Neural Networks Based on Graphics Processing Units—0
Towards Non-I.I.D. Image Classification: A Dataset and Baselines—0
Ladder Networks for Semi-Supervised Hyperspectral Image Classification—0
Deep Scene Image Classification With the MFAFVNet—0
Bayesian Test-Time Adaptation for Vision-Language Models—0
LaCoOT: Layer Collapse through Optimal Transport—0
NN2CAM: Automated Neural Network Mapping for Multi-Precision Edge Processing on FPGA-Based Cameras—0
Laconic Image Classification: Human vs. Machine Performance—0
Laconic Deep Learning Computing—0
LABO: Towards Learning Optimal Label Regularization via Bi-level Optimization—0
Revisiting the Role of Label Smoothing in Enhanced Text Sentiment Classification—0
Noise-Contrastive Variational Information Bottleneck Networks—0
Label-similarity Curriculum Learning—0
Noise-reducing attention cross fusion learning transformer for histological image classification of osteosarcoma—0
Deep Residual Network based food recognition for enhanced Augmented Reality application—0
Label-noise-tolerant medical image classification via self-attention and self-supervised learning—0
Label Geometry Aware Discriminator for Conditional Generative Networks—0
NoisyAG-News: A Benchmark for Addressing Instance-Dependent Noise in Text Classification—0
Label Embedding with Partial Heterogeneous Contexts—0
Deep Residual Axial Networks—0
Label-Embedding for Attribute-Based Classification—0
Noisy Label Processing for Classification: A Survey—0
Deep Repulsive Clustering of Ordered Data Based on Order-Identity Decomposition—0
No more meta-parameter tuning in unsupervised sparse feature learning—0
Bayesian Optimization for Hyperparameters Tuning in Neural Networks—0
An Adaptive Sampling and Edge Detection Approach for Encoding Static Images for Spiking Neural Networks—0
Adaptive Region Pooling for Fine-Grained Representation Learning—0
Nonconvex Continual Learning with Episodic Memory—0
Label-efficient Single Photon Images Classification via Active Learning—0
DeepRepair: Style-Guided Repairing for DNNs in the Real-world Operational Environment—0
Non-Convex Optimization in Federated Learning via Variance Reduction and Adaptive Learning—0
Label-Efficient Group Robustness via Out-of-Distribution Concept Curation—0
Label Denoising through Cross-Model Agreement—0
Deep-Relative-Trust-Based Diffusion for Decentralized Deep Learning—0
Non-Determinism in TensorFlow ResNets—0
Non-Federated Multi-Task Split Learning for Heterogeneous Sources—0
Label Consistent Transform Learning for Hyperspectral Image Classification—0
Non-imaging single-pixel sensing with optimized binary modulation—0
Non-linear Convolution Filters for CNN-based Learning—0
Nonlinear functional regression by functional deep neural network with kernel embedding—0
Deep reinforcement learning with automated label extraction from clinical reports accurately classifies 3D MRI brain volumes—0
Nonlocal Kernel Network (NKN): a Stable and Resolution-Independent Deep Neural Network—0
Deep Reinforcement Learning using Capsules in Advanced Game Environments—0
Nonnegative/Binary Matrix Factorization for Image Classification using Quantum Annealing—0
KTAN: Knowledge Transfer Adversarial Network—0
Deep Reinforcement Learning for Task Offloading in UAV-Aided Smart Farm Networks—0
Deep reinforcement learning-based image classification achieves perfect testing set accuracy for MRI brain tumors with a training set of only 30 images—0
Kolmogorov-Arnold networks for metal surface defect classification—0
Deep Reinforcement Learning and its Neuroscientific Implications—0
Bayesian Learning to Optimize: Quantifying the Optimizer Uncertainty—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