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 7951–8000 of 10420 papers

TitleStatusHype
Finding Optimal Kernel Size and Dimension in Convolutional Neural Networks An Architecture Optimization Approach—0
Finding Better Topologies for Deep Convolutional Neural Networks by Evolution—0
Generating and Detecting True Ambiguity: A Forgotten Danger in DNN Supervision Testing—0
Retaining Knowledge and Enhancing Long-Text Representations in CLIP through Dual-Teacher Distillation—0
The Power of Linear Combinations: Learning with Random Convolutions—0
Responsibility: An Example-based Explainable AI approach via Training Process Inspection—0
Representation Memorization for Fast Learning New Knowledge without Forgetting—0
Are Visual Recognition Models Robust to Image Compression?—0
Representation Synthesis by Probabilistic Many-Valued Logic Operation in Self-Supervised Learning—0
How Good is your Explanation? Algorithmic Stability Measures to Assess the Quality of Explanations for Deep Neural Networks—0
Filter Distribution Templates in Convolutional Networks for Image Classification Tasks—0
REP: Resource-Efficient Prompting for Rehearsal-Free Continual Learning—0
A Foreground Inference Network for Video Surveillance Using Multi-View Receptive Field—0
FILM: How can Few-Shot Image Classification Benefit from Pre-Trained Language Models?—0
Concurrent Neural Tree and Data Preprocessing AutoML for Image Classification—0
Fighting Fire with Fire: Avoiding DNN Shortcuts through Priming—0
Concurrent Classifier Error Detection (CCED) in Large Scale Machine Learning Systems—0
A Simple and Generic Framework for Feature Distillation via Channel-wise Transformation—0
Resource Efficient Neural Networks Using Hessian Based Pruning—0
Resampled Datasets Are Not Enough: Mitigating Societal Bias Beyond Single Attributes—0
Restricted Boltzmann Machines for galaxy morphology classification with a quantum annealer—0
Jointly Resampling and Reconstructing Corrupted Images for Image Classification using Frequency-Selective Mesh-to-Grid Resampling—0
ResBit: Residual Bit Vector for Categorical Values—0
ResBuilder: Automated Learning of Depth with Residual Structures—0
FickleNet: Weakly and Semi-supervised Semantic Image Segmentation using Stochastic Inference—0
Research on the Detection Method of Breast Cancer Deep Convolutional Neural Network Based on Computer Aid—0
Research on the pixel-based and object-oriented methods of urban feature extraction with GF-2 remote-sensing images—0
ReSet: Learning Recurrent Dynamic Routing in ResNet-like Neural Networks—0
A simple and effective postprocessing method for image classification—0
FHIST: A Benchmark for Few-shot Classification of Histological Images—0
Resolution-Based Distillation for Efficient Histology Image Classification—0
Residual and Attentional Architectures for Vector-Symbols—0
Concept Induction using LLMs: a user experiment for assessment—0
Few-shot Unsupervised Domain Adaptation with Image-to-class Sparse Similarity Encoding—0
Activation Atlas—0
Residual Error: a New Performance Measure for Adversarial Robustness—0
Residual Feature-Reutilization Inception Network for Image Classification—0
Residual Network based Aggregation Model for Skin Lesion Classification—0
Residual Networks of Residual Networks: Multilevel Residual Networks—0
Residual Squeeze VGG16—0
REFT: Resource-Efficient Federated Training Framework for Heterogeneous and Resource-Constrained Environments—0
Resisting Adversarial Attacks in Deep Neural Networks using Diverse Decision Boundaries—0
ResizeMix: Mixing Data with Preserved Object Information and True Labels—0
RE-Tagger: A light-weight Real-Estate Image Classifier—0
Few-Shot Non-Parametric Learning with Deep Latent Variable Model—0
Resnet18 Model With Sequential Layer For Computing Accuracy On Image Classification Dataset—0
Few-shot medical image classification with simple shape and texture text descriptors using vision-language models—0
Feature Augmentation for Self-supervised Contrastive Learning: A Closer Look—0
Concept Bottleneck with Visual Concept Filtering for Explainable Medical Image Classification—0
Few-Shot Learning of Compact Models via Task-Specific Meta Distillation—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