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 4251–4300 of 10420 papers

TitleStatusHype
Continual Learning with Deep Generative ReplayCode0
Hyperparameter Ensembles for Robustness and Uncertainty QuantificationCode0
Hyper-Process Model: A Zero-Shot Learning algorithm for Regression Problems based on Shape AnalysisCode0
Continual Learning of Unsupervised Monocular Depth from VideosCode0
Continual Learning in Open-vocabulary Classification with Complementary Memory SystemsCode0
HyperDID: Hyperspectral Intrinsic Image Decomposition with Deep Feature EmbeddingCode0
Hyperbolic Sliced-Wasserstein via Geodesic and Horospherical ProjectionsCode0
Hyperspectral Image Classification: Artifacts of Dimension Reduction on Hybrid CNNCode0
HyenaPixel: Global Image Context with ConvolutionsCode0
HybridSN: Exploring 3D-2D CNN Feature Hierarchy for Hyperspectral Image ClassificationCode0
Human-in-the-Loop Visual Re-ID for Population Size EstimationCode0
Drop Clause: Enhancing Performance, Interpretability and Robustness of the Tsetlin MachineCode0
Continual Contrastive Learning for Image ClassificationCode0
Active Generation for Image ClassificationCode0
Hybrid Macro/Micro Level Backpropagation for Training Deep Spiking Neural NetworksCode0
Continual and Multi-Task Architecture SearchCode0
HSI-CNN: A Novel Convolution Neural Network for Hyperspectral ImageCode0
DeMansia: Mamba Never Forgets Any TokensCode0
Human Activity Recognition Based on Wearable Sensor Data: A Standardization of the State-of-the-ArtCode0
A Stochastic Proximal Polyak Step SizeCode0
How to Use Dropout Correctly on Residual Networks with Batch NormalizationCode0
Contextual Learning in Fourier Complex Field for VHR Remote Sensing ImagesCode0
Contextualizing Meta-Learning via Learning to DecomposeCode0
How transfer learning is used in generative models for image classification: improved accuracyCode0
Human-imperceptible, Machine-recognizable ImagesCode0
HyperbolicLR: Epoch insensitive learning rate schedulerCode0
IBCL: Zero-shot Model Generation for Task Trade-offs in Continual LearningCode0
Contextual Explanation NetworksCode0
FrImCla: A Framework for Image Classification Using Traditional and Transfer Learning TechniquesCode0
Contextual Encoder-Decoder Network for Visual Saliency PredictionCode0
FrequentNet: A Novel Interpretable Deep Learning Model for Image ClassificationCode0
Frequency-Temporal Attention Network for Remote Sensing Imagery Change DetectionCode0
HOLMES: HOLonym-MEronym based Semantic inspection for Convolutional Image ClassifiersCode0
Frequency-Guided Masking for Enhanced Vision Self-Supervised LearningCode0
Contextual Dropout: An Efficient Sample-Dependent Dropout ModuleCode0
Homogeneous Learning: Self-Attention Decentralized Deep LearningCode0
Histopathological Image Classification based on Self-Supervised Vision Transformer and Weak LabelsCode0
Histopathological Image Classification using Discriminative Feature-oriented Dictionary LearningCode0
Contextual Classification Using Self-Supervised Auxiliary Models for Deep Neural NetworksCode0
Associative TransformerCode0
Contextual Checkerboard Denoise -- A Novel Neural Network-Based Approach for Classification-Aware OCT Image DenoisingCode0
Use HiResCAM instead of Grad-CAM for faithful explanations of convolutional neural networksCode0
FReLU: Flexible Rectified Linear Units for Improving Convolutional Neural NetworksCode0
ISLE: An Intelligent Streaming Framework for High-Throughput AI Inference in Medical ImagingCode0
Histogram Layers for Neural Engineered FeaturesCode0
How Do Training Methods Influence the Utilization of Vision Models?Code0
Associative Alignment for Few-shot Image ClassificationCode0
Context-Gated ConvolutionCode0
High Definition image classification in Geoscience using Machine LearningCode0
High-fidelity Pseudo-labels for Boosting Weakly-Supervised SegmentationCode0
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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