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 3051–3100 of 10419 papers

TitleStatusHype
Benchmarking Large Language Models for Image Classification of Marine MammalsCode0
Improving Shift Invariance in Convolutional Neural Networks with Translation Invariant Polyphase SamplingCode0
Defending Against Physically Realizable Attacks on Image ClassificationCode0
Improving Pre-Trained Weights Through Meta-Heuristics Fine-TuningCode0
Improving Prototypical Visual Explanations with Reward Reweighing, Reselection, and RetrainingCode0
Improving Pairwise Ranking for Multi-label Image ClassificationCode0
Improving Random-Sampling Neural Architecture Search by Evolving the Proxy Search SpaceCode0
Boosting Deep Ensemble Performance with Hierarchical PruningCode0
An Empirical Investigation of Randomized Defenses against Adversarial AttacksCode0
Discriminative Feature Learning through Feature Distance LossCode0
Improving singing voice separation with the Wave-U-Net using Minimum Hyperspherical EnergyCode0
Influence of Image Classification Accuracy on Saliency Map EstimationCode0
Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)Code0
Knowledge Swapping via Learning and UnlearningCode0
Boosting Ensemble Accuracy by Revisiting Ensemble Diversity MetricsCode0
Learning Sparse & Ternary Neural Networks with Entropy-Constrained Trained Ternarization (EC2T)Code0
Benchmarking Deep Learning Models on NVIDIA Jetson Nano for Real-Time Systems: An Empirical InvestigationCode0
Improving Long-tailed Object Detection with Image-Level Supervision by Multi-Task Collaborative LearningCode0
Acne Severity Grading on Face Images via Extraction and Guidance of Prior KnowledgeCode0
Discriminative Unsupervised Feature Learning with Convolutional Neural NetworksCode0
Deep Visual City Recognition VisualizationCode0
Adaptive Sample Selection for Robust Learning under Label NoiseCode0
Deep Variation-structured Reinforcement Learning for Visual Relationship and Attribute DetectionCode0
Analysing Training-Data Leakage from Gradients through Linear Systems and Gradient MatchingCode0
Improving k-Means Clustering Performance with Disentangled Internal RepresentationsCode0
Improving Memory Efficiency for Training KANs via Meta LearningCode0
Improving Generalization of Batch Whitening by Convolutional Unit OptimizationCode0
Benchmark Generation Framework with Customizable Distortions for Image Classifier RobustnessCode0
Improving Generalization and Convergence by Enhancing Implicit RegularizationCode0
SynerMix: Synergistic Mixup Solution for Enhanced Intra-Class Cohesion and Inter-Class Separability in Image ClassificationCode0
Improving Fairness in Image Classification via SketchingCode0
Deep transfer learning method based on automatic domain alignment and moment matchingCode0
Fourier Transform Approximation as an Auxiliary Task for Image ClassificationCode0
DisplaceNet: Recognising Displaced People from Images by Exploiting Dominance LevelCode0
Improving Ensemble Distillation With Weight Averaging and Diversifying PerturbationCode0
Improving Generalizability of Kolmogorov-Arnold Networks via Error-Correcting Output CodesCode0
Improving Intervention Efficacy via Concept Realignment in Concept Bottleneck ModelsCode0
Improving model calibration with accuracy versus uncertainty optimizationCode0
Improving Confident-Classifiers For Out-of-distribution DetectionCode0
Distance Based Image Classification: A solution to generative classification's conundrum?Code0
An All-digital 8.6-nJ/Frame 65-nm Tsetlin Machine Image Classification AcceleratorCode0
Improving Calibration by Relating Focal Loss, Temperature Scaling, and PropernessCode0
BEiT v2: Masked Image Modeling with Vector-Quantized Visual TokenizersCode0
Improving Classification Neural Networks by using Absolute activation function (MNIST/LeNET-5 example)Code0
Towards Difficulty-Agnostic Efficient Transfer Learning for Vision-Language ModelsCode0
Deep Sub-Ensembles for Fast Uncertainty Estimation in Image ClassificationCode0
An Algorithm for Out-Of-Distribution Attack to Neural Network EncoderCode0
Anytime Inference with Distilled Hierarchical Neural EnsemblesCode0
Improving (α, f)-Byzantine Resilience in Federated Learning via layerwise aggregation and cosine distanceCode0
Improving Deep Neural Network Random Initialization Through Neuronal RewiringCode0
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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