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 10301–10325 of 10420 papers

TitleStatusHype
Comment on "Ensemble Projection for Semi-supervised Image Classification"—0
Sparse Graph-based Transduction for Image Classification—0
Label Consistent Fisher Vectors for Supervised Feature AggregationCode0
Hierarchical Adaptive Structural SVM for Domain Adaptation—0
Seeing through bag-of-visual-word glasses: towards understanding quantization effects in feature extraction methods—0
Discovering Discriminative Cell Attributes for HEp-2 Specimen Image Classification—0
An landcover fuzzy logic classification by maximumlikelihood—0
Learning Discriminative Stein Kernel for SPD Matrices and Its ApplicationsCode0
Face Identification with Second-Order Pooling—0
Face Image Classification by Pooling Raw Features—0
Committees of deep feedforward networks trained with few data—0
CNN: Single-label to Multi-label—0
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual RecognitionCode0
Convolutional Kernel Networks—0
Deep Epitomic Convolutional Neural Networks—0
Generalized Max Pooling—0
Additive Quantization for Extreme Vector Compression—0
Compact Representation for Image Classification: To Choose or to Compress?—0
Fantope Regularization in Metric Learning—0
Histograms of Pattern Sets for Image Classification and Object Recognition—0
Random Laplace Feature Maps for Semigroup Kernels on Histograms—0
Product Sparse Coding—0
Learning Receptive Fields for Pooling from Tensors of Feature Response—0
Modeling Image Patches with a Generic Dictionary of Mini-Epitomes—0
Learning and Transferring Mid-Level Image Representations using Convolutional Neural Networks—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