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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 9451–9500 of 10420 papers

TitleStatusHype
Google street view and deep learning: a new ground truthing approach for crop mapping—0
GPC: Generative and General Pathology Image Classifier—0
GP-NAS: Gaussian Process Based Neural Architecture Search—0
GPT4MIA: Utilizing Generative Pre-trained Transformer (GPT-3) as A Plug-and-Play Transductive Model for Medical Image Analysis—0
GPT-4 Vision on Medical Image Classification -- A Case Study on COVID-19 Dataset—0
GQKVA: Efficient Pre-training of Transformers by Grouping Queries, Keys, and Values—0
Understanding Dropout as an Optimization Trick—0
Gradient-based Class Weighting for Unsupervised Domain Adaptation in Dense Prediction Visual Tasks—0
Gradient-based Wang-Landau Algorithm: A Novel Sampler for Output Distribution of Neural Networks over the Input Space—0
Gradient-EM Bayesian Meta-learning—0
ExPLoit: Extracting Private Labels in Split Learning—0
Gradient Normalization & Depth Based Decay For Deep Learning—0
Gradient Obfuscation Gives a False Sense of Security in Federated Learning—0
Gradient Scaling on Deep Spiking Neural Networks with Spike-Dependent Local Information—0
Gradient Weighted Superpixels for Interpretability in CNNs—0
GradPCA: Leveraging NTK Alignment for Reliable Out-of-Distribution Detection—0
Grafit: Learning fine-grained image representations with coarse labels—0
Grafting Vision Transformers—0
Graph-Based Classification of Omnidirectional Images—0
Graph Based Convolutional Neural Network—0
Graph based Label Enhancement for Multi-instance Multi-label learning—0
Graph-based Representation for Image based on Granular-ball—0
Graph Classification with Geometric Scattering—0
Graph Clustering With Missing Data: Convex Algorithms and Analysis—0
Graph Convolutional Networks based on Manifold Learning for Semi-Supervised Image Classification—0
GFCN: A New Graph Convolutional Network Based on Parallel Flows—0
Graph Neural Networks for Image Classification and Reinforcement Learning using Graph representations—0
Graph Neural Networks for UnsupervisedDomain Adaptation of Histopathological ImageAnalytics—0
Graph-propagation based Correlation Learning for Weakly Supervised Fine-grained Image Classification—0
Graph-RISE: Graph-Regularized Image Semantic Embedding—0
Graph Semi-Supervised Learning for Point Classification on Data Manifolds—0
Graphs for deep learning representations—0
Graph Structural Aggregation for Explainable Learning—0
GraphViz2Vec: A Structure-aware Feature Generation Model to Improve Classification in GNNs—0
GRASP: A Rehearsal Policy for Efficient Online Continual Learning—0
Grassmann Pooling as Compact Homogeneous Bilinear Pooling for Fine-Grained Visual Classification—0
GreedyNAS: Towards Fast One-Shot NAS with Greedy Supernet—0
Greedy Policy Search: A Simple Baseline for Learnable Test-Time Augmentation—0
G-RepsNet: A Fast and General Construction of Equivariant Networks for Arbitrary Matrix Groups—0
flexgrid2vec: Learning Efficient Visual Representations Vectors—0
How Out-of-Distribution Detection Learning Theory Enhances Transformer: Learnability and Reliability—0
GROOD: Gradient-Aware Out-of-Distribution Detection—0
Group Based Deep Shared Feature Learning for Fine-grained Image Classification—0
Grouping-By-ID: Guarding Against Adversarial Domain Shifts—0
Group Invariant Deep Representations for Image Instance Retrieval—0
Group Sparse Coding—0
GrowCLIP: Data-aware Automatic Model Growing for Large-scale Contrastive Language-Image Pre-training—0
Growing Efficient Deep Networks by Structured Continuous Sparsification—0
gSwin: Gated MLP Vision Model with Hierarchical Structure of Shifted Window—0
GUIDED MCMC FOR SPARSE BAYESIAN MODELS TO DETECT RARE EVENTS IN IMAGES SANS LABELED DATA—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