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 6701–6750 of 10420 papers

TitleStatusHype
LDCA: Local Descriptors with Contextual Augmentation for Few-Shot Learning—0
LC-TTFS: Towards Lossless Network Conversion for Spiking Neural Networks with TTFS Coding—0
Multi-task Image Classification via Collaborative, Hierarchical Spike-and-Slab Priors—0
Multi-task Learning on MNIST Image Datasets—0
LCReg: Long-Tailed Image Classification with Latent Categories based Recognition—0
Multi-Task Multicriteria Hyperparameter Optimization—0
L-CNN: A Lattice cross-fusion strategy for multistream convolutional neural networks—0
Deep View-Sensitive Pedestrian Attribute Inference in an end-to-end Model—0
Benchmarking Adversarial Robustness—0
Multi-Teacher Multi-Objective Meta-Learning for Zero-Shot Hyperspectral Band Selection—0
LCDet: Low-Complexity Fully-Convolutional Neural Networks for Object Detection in Embedded Systems—0
LayoutMask: Enhance Text-Layout Interaction in Multi-modal Pre-training for Document Understanding—0
DeepTraverse: A Depth-First Search Inspired Network for Algorithmic Visual Understanding—0
Multi-View Matrix Completion for Multi-Label Image Classification—0
Benchmarking a Benchmark: How Reliable is MS-COCO?—0
LayoutLLM: Large Language Model Instruction Tuning for Visually Rich Document Understanding—0
A Generic Shared Attention Mechanism for Various Backbone Neural Networks—0
Multi-view Vector-valued Manifold Regularization for Multi-label Image Classification—0
Layer-Wise Adaptive Updating for Few-Shot Image Classification—0
Multi-way Encoding for Robustness—0
Layer-Specific Adaptive Learning Rates for Deep Networks—0
MuMIC -- Multimodal Embedding for Multi-label Image Classification with Tempered Sigmoid—0
Deep Transfer Learning: Model Framework and Error Analysis—0
An Alternative Practice of Tropical Convolution to Traditional Convolutional Neural Networks—0
MUPAX: Multidimensional Problem Agnostic eXplainable AI—0
MUSE: Feature Self-Distillation with Mutual Information and Self-Information—0
Deep Transfer Learning Methods for Colon Cancer Classification in Confocal Laser Microscopy Images—0
LayerCollapse: Adaptive compression of neural networks—0
Benchmark data to study the influence of pre-training on explanation performance in MR image classification—0
Mutual Attention-based Hybrid Dimensional Network for Multimodal Imaging Computer-aided Diagnosis—0
Lateralized Learning for Multi-Class Visual Classification Tasks—0
Latent Time Neural Ordinary Differential Equations—0
Deep Transfer Learning for Person Re-identification—0
Benchmark Analysis of Various Pre-trained Deep Learning Models on ASSIRA Cats and Dogs Dataset—0
Adaptive Risk Minimization: A Meta-Learning Approach for Tackling Group Shift—0
Achieving Rotation Invariance in Convolution Operations: Shifting from Data-Driven to Mechanism-Assured—0
Latent Model Ensemble with Auto-localization—0
MVFormer: Diversifying Feature Normalization and Token Mixing for Efficient Vision Transformers—0
LatentGAN Autoencoder: Learning Disentangled Latent Distribution—0
MVP: Meta Visual Prompt Tuning for Few-Shot Remote Sensing Image Scene Classification—0
Deep transfer learning for image classification: a survey—0
Latent Enhancing AutoEncoder for Occluded Image Classification—0
Latent Domain Learning with Dynamic Residual Adapters—0
Naive Gabor Networks for Hyperspectral Image Classification—0
Nanorobot queue: Cooperative treatment of cancer based on team member communication and image processing—0
Narrowing the Focus: Learned Optimizers for Pretrained Models—0
Deep Transfer Learning for Few-Shot SAR Image Classification—0
Deep transfer learning for detecting Covid-19, Pneumonia and Tuberculosis using CXR images -- A Review—0
deepTerra -- AI Land Classification Made Easy—0
BEL: A Bag Embedding Loss for Transformer enhances Multiple Instance Whole Slide Image Classification—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