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 3301–3350 of 10419 papers

TitleStatusHype
Dynamic Convolution: Attention over Convolution KernelsCode0
Dynamic Convolutions: Exploiting Spatial Sparsity for Faster InferenceCode0
Understanding and Robustifying Differentiable Architecture SearchCode0
Improving Neural Architecture Search Image Classifiers via Ensemble LearningCode0
Improving Shift Invariance in Convolutional Neural Networks with Translation Invariant Polyphase SamplingCode0
Multilingual Vision-Language Pre-training for the Remote Sensing DomainCode0
Deep Manifold Embedding for Hyperspectral Image ClassificationCode0
Improving Generalization of Batch Whitening by Convolutional Unit OptimizationCode0
SynerMix: Synergistic Mixup Solution for Enhanced Intra-Class Cohesion and Inter-Class Separability in Image ClassificationCode0
Deeply-Supervised NetsCode0
A Certified Radius-Guided Attack Framework to Image Segmentation ModelsCode0
Improving Intervention Efficacy via Concept Realignment in Concept Bottleneck ModelsCode0
Improving Generalizability of Kolmogorov-Arnold Networks via Error-Correcting Output CodesCode0
Deep Low-Shot Learning for Biological Image Classification and Visualization from Limited Training SamplesCode0
Adaptively Connected Neural NetworksCode0
Improving Fairness in Image Classification via SketchingCode0
Dynamic Loss For Robust LearningCode0
Multimodal Structure-Aware Quantum Data ProcessingCode0
Dynamic Mini-batch SGD for Elastic Distributed Training: Learning in the Limbo of ResourcesCode0
Improving Generalization and Convergence by Enhancing Implicit RegularizationCode0
Improving k-Means Clustering Performance with Disentangled Internal RepresentationsCode0
Dynamic Mode Decomposition based feature for Image ClassificationCode0
Deep Learning with Gaussian Differential PrivacyCode0
Multi-path Convolutional Neural Networks for Complex Image ClassificationCode0
Deep learning with Elastic Averaging SGDCode0
Deep Learning with Eigenvalue Decay RegularizerCode0
Deep Learning using Linear Support Vector MachinesCode0
Balanced Mixture of SuperNets for Learning the CNN Pooling ArchitectureCode0
AmsterTime: A Visual Place Recognition Benchmark Dataset for Severe Domain ShiftCode0
Deep Learning under Privileged Information Using Heteroscedastic DropoutCode0
Balanced joint maximum mean discrepancy for deep transfer learningCode0
Improving Deep Neural Network Random Initialization Through Neuronal RewiringCode0
A Mosquito is Worth 16x16 Larvae: Evaluation of Deep Learning Architectures for Mosquito Larvae ClassificationCode0
Multiscale Principle of Relevant Information for Hyperspectral Image ClassificationCode0
Targeted Deep Learning System Boundary TestingCode0
MaxVA: Fast Adaptation of Step Sizes by Maximizing Observed Variance of GradientsCode0
Improving Ensemble Distillation With Weight Averaging and Diversifying PerturbationCode0
Improving Long-tailed Object Detection with Image-Level Supervision by Multi-Task Collaborative LearningCode0
Improving singing voice separation with the Wave-U-Net using Minimum Hyperspherical EnergyCode0
Successive Embedding and Classification Loss for Aerial Image ClassificationCode0
Early Diagnosis of Retinal Blood Vessel Damage via Deep Learning-Powered Collective Intelligence ModelsCode0
Early-exit deep neural networks for distorted images: providing an efficient edge offloadingCode0
Towards Difficulty-Agnostic Efficient Transfer Learning for Vision-Language ModelsCode0
Adaptive Learning Rate and Momentum for Training Deep Neural NetworksCode0
Improving (α, f)-Byzantine Resilience in Federated Learning via layerwise aggregation and cosine distanceCode0
Balanced Binary Neural Networks with Gated ResidualCode0
BR-NPA: A Non-Parametric High-Resolution Attention Model to improve the Interpretability of AttentionCode0
Improved Training Speed, Accuracy, and Data Utilization Through Loss Function OptimizationCode0
Improving Calibration by Relating Focal Loss, Temperature Scaling, and PropernessCode0
Bag of Tricks for Retail Product Image ClassificationCode0
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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