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

Showing 94769500 of 10420 papers

TitleStatusHype
GFCN: A New Graph Convolutional Network Based on Parallel Flows0
Graph Neural Networks for Image Classification and Reinforcement Learning using Graph representations0
Graph Neural Networks for UnsupervisedDomain Adaptation of Histopathological ImageAnalytics0
Graph-propagation based Correlation Learning for Weakly Supervised Fine-grained Image Classification0
Graph-RISE: Graph-Regularized Image Semantic Embedding0
Graph Semi-Supervised Learning for Point Classification on Data Manifolds0
Graphs for deep learning representations0
Graph Structural Aggregation for Explainable Learning0
GraphViz2Vec: A Structure-aware Feature Generation Model to Improve Classification in GNNs0
GRASP: A Rehearsal Policy for Efficient Online Continual Learning0
Grassmann Pooling as Compact Homogeneous Bilinear Pooling for Fine-Grained Visual Classification0
GreedyNAS: Towards Fast One-Shot NAS with Greedy Supernet0
Greedy Policy Search: A Simple Baseline for Learnable Test-Time Augmentation0
G-RepsNet: A Fast and General Construction of Equivariant Networks for Arbitrary Matrix Groups0
flexgrid2vec: Learning Efficient Visual Representations Vectors0
How Out-of-Distribution Detection Learning Theory Enhances Transformer: Learnability and Reliability0
GROOD: Gradient-Aware Out-of-Distribution Detection0
Group Based Deep Shared Feature Learning for Fine-grained Image Classification0
Grouping-By-ID: Guarding Against Adversarial Domain Shifts0
Group Invariant Deep Representations for Image Instance Retrieval0
Group Sparse Coding0
GrowCLIP: Data-aware Automatic Model Growing for Large-scale Contrastive Language-Image Pre-training0
Growing Efficient Deep Networks by Structured Continuous Sparsification0
gSwin: Gated MLP Vision Model with Hierarchical Structure of Shifted Window0
GUIDED MCMC FOR SPARSE BAYESIAN MODELS TO DETECT RARE EVENTS IN IMAGES SANS LABELED DATA0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CoCa (finetuned)Top 1 Accuracy91Unverified
2Model soups (BASIC-L)Top 1 Accuracy90.98Unverified
3Model soups (ViT-G/14)Top 1 Accuracy90.94Unverified
4DaViT-GTop 1 Accuracy90.4Unverified
5DaViT-HTop 1 Accuracy90.2Unverified
6Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2Unverified
7SwinV2-GTop 1 Accuracy90.17Unverified
8MAWS (ViT-6.5B)Top 1 Accuracy90.1Unverified
9Florence-CoSwin-HTop 1 Accuracy90.05Unverified
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90Unverified