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 8701–8750 of 10420 papers

TitleStatusHype
Conditional Variance Penalties and Domain Shift RobustnessCode0
ConDiSR: Contrastive Disentanglement and Style Regularization for Single Domain GeneralizationCode0
Variational Resampling Based Assessment of Deep Neural Networks under Distribution ShiftCode0
Accurate Dictionary Learning with Direct Sparsity ControlCode0
Accuracy of TextFooler black box adversarial attacks on 01 loss sign activation neural network ensembleCode0
MetaKernel: Learning Variational Random Features with Limited LabelsCode0
E2GC: Energy-efficient Group Convolution in Deep Neural NetworksCode0
DAGNet: A Dual-View Attention-Guided Network for Efficient X-ray Security InspectionCode0
High Definition image classification in Geoscience using Machine LearningCode0
Meta Label Correction for Noisy Label LearningCode0
CondenseNeXt: An Ultra-Efficient Deep Neural Network for Embedded SystemsCode0
High-fidelity Pseudo-labels for Boosting Weakly-Supervised SegmentationCode0
Meta-Learners for Few-Shot Weakly-Supervised Medical Image SegmentationCode0
A multiple-instance densely-connected ConvNet for aerial scene classificationCode0
Meta-learning algorithms for Few-Shot Computer VisionCode0
Meta-Learning Initializations for Image SegmentationCode0
Rescaling Large Datasets Based on Validation Outcomes of a Pre-trained NetworkCode0
Conceptual Learning via Embedding Approximations for Reinforcing Interpretability and TransparencyCode0
Dynamic Mode Decomposition based feature for Image ClassificationCode0
High Performance Offline Handwritten Chinese Character Recognition Using GoogLeNet and Directional Feature MapsCode0
Meta-Learning without MemorizationCode0
ISLE: An Intelligent Streaming Framework for High-Throughput AI Inference in Medical ImagingCode0
A Multimodal Approach For Endoscopic VCE Image Classification Using BiomedCLIP-PubMedBERTCode0
Concept Graph Embedding Models for Enhanced Accuracy and InterpretabilityCode0
QSGD: Communication-Efficient SGD via Gradient Quantization and EncodingCode0
Optimizing Neural Networks with Gradient Lexicase SelectionCode0
Dynamic Mobile-Former: Strengthening Dynamic Convolution with Attention and Residual Connection in Kernel SpaceCode0
Use HiResCAM instead of Grad-CAM for faithful explanations of convolutional neural networksCode0
Augmented Balanced Image Dataset Generator Using AugStatic LibraryCode0
Histogram Layers for Neural Engineered FeaturesCode0
AmsterTime: A Visual Place Recognition Benchmark Dataset for Severe Domain ShiftCode0
Concept-based explainability for an EEG transformer modelCode0
Histopathological Image Classification based on Self-Supervised Vision Transformer and Weak LabelsCode0
Histopathological Image Classification using Discriminative Feature-oriented Dictionary LearningCode0
Dynamic Mini-batch SGD for Elastic Distributed Training: Learning in the Limbo of ResourcesCode0
Meta-Reinforced Synthetic Data for One-Shot Fine-Grained Visual RecognitionCode0
Computer-aided Interpretable Features for Leaf Image ClassificationCode0
Dynamic Loss For Robust LearningCode0
Quality-Agnostic Image Recognition via Invertible DecoderCode0
Dynamic Convolutions: Exploiting Spatial Sparsity for Faster InferenceCode0
MetH: A family of high-resolution and variable-shape image challengesCode0
HOLMES: HOLonym-MEronym based Semantic inspection for Convolutional Image ClassifiersCode0
Homogeneous Learning: Self-Attention Decentralized Deep LearningCode0
Orchid2024: A cultivar-level dataset and methodology for fine-grained classification of Chinese Cymbidium OrchidsCode0
Dynamic Convolution: Attention over Convolution KernelsCode0
Methods for the frugal labeler: Multi-class semantic segmentation on heterogeneous labelsCode0
Quality Assessment of In-the-Wild VideosCode0
Deep and interpretable regression models for ordinal outcomesCode0
Metrics and methods for robustness evaluation of neural networks with generative modelsCode0
Compressing Vision Transformers for Low-Resource Visual LearningCode0
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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