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 6601–6650 of 10420 papers

TitleStatusHype
Multi-layer Representation Learning for Robust OOD Image Classification—0
Deferred Poisoning: Making the Model More Vulnerable via Hessian Singularization—0
Learning Binary Codes and Binary Weights for Efficient Classification—0
Defensive Tensorization: Randomized Tensor Parametrization for Robust Neural Networks—0
Learning-Based Data Storage [Vision] (Technical Report)—0
Learning Augmentation Network via Influence Functions—0
Defensive Tensorization—0
Multi-level Residual Networks from Dynamical Systems View—0
Benchmarking PathCLIP for Pathology Image Analysis—0
Multilingual Image Corpus: Annotation Protocol—0
Multilingual Image Corpus – Towards a Multimodal and Multilingual Dataset—0
Learning and Transferring Mid-Level Image Representations using Convolutional Neural Networks—0
Multi-loss ensemble deep learning for chest X-ray classification—0
Learning and Interpreting Multi-Multi-Instance Learning Networks—0
Learning and Exploiting Interclass Visual Correlations for Medical Image Classification—0
Learning a Fourier Transform for Linear Relative Positional Encodings in Transformers—0
Benchmarking Multi-Domain Active Learning on Image Classification—0
Analysis of Catastrophic Forgetting for Random Orthogonal Transformation Tasks in the Overparameterized Regime—0
Adaptive Selection of Deep Learning Models on Embedded Systems—0
Multimodal Approaches to Fair Image Classification: An Ethical Perspective—0
Learning a Deep ConvNet for Multi-label Classification with Partial Labels—0
Multimodal brain tumor classification—0
Defending Malware Classification Networks Against Adversarial Perturbations with Non-Negative Weight Restrictions—0
Learning Across Decentralized Multi-Modal Remote Sensing Archives with Federated Learning—0
Post-train Black-box Defense via Bayesian Boundary Correction—0
Defending Against Universal Perturbations With Shared Adversarial Training—0
Benchmarking MedMNIST dataset on real quantum hardware—0
Learned Image resizing with efficient training (LRET) facilitates improved performance of large-scale digital histopathology image classification models—0
Learned Gradient Compression for Distributed Deep Learning—0
Defending Against Image Corruptions Through Adversarial Augmentations—0
Benchmarking Inference Performance of Deep Learning Models on Analog Devices—0
Learnable Pooling Regions for Image Classification—0
Defending Adversaries Using Unsupervised Feature Clustering VAE—0
Benchmarking FedAvg and FedCurv for Image Classification Tasks—0
Analysis Dictionary Learning: An Efficient and Discriminative Solution—0
Multimodal Semantic Transfer from Text to Image. Fine-Grained Image Classification by Distributional Semantics—0
Adaptive sampling for scanning pixel cameras—0
ACIL: Active Class Incremental Learning for Image Classification—0
SNR and Resource Adaptive Deep JSCC for Distributed IoT Image Classification—0
Tag-based Semantic Features for Scene Image Classification—0
What Do Single-view 3D Reconstruction Networks Learn?—0
Learnable Companding Quantization for Accurate Low-bit Neural Networks—0
Learnable Bernoulli Dropout for Bayesian Deep Learning—0
DeepVO: A Deep Learning approach for Monocular Visual Odometry—0
LEAP: Learning Embeddings for Adaptive Pace—0
LeanResNet: A Low-cost Yet Effective Convolutional Residual Networks—0
LeanConvNets: Low-cost Yet Effective Convolutional Neural Networks—0
Multipath Sparse Coding Using Hierarchical Matching Pursuit—0
Multi-perspective Contrastive Logit Distillation—0
Deep Visual Domain Adaptation: A Survey—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