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 94269450 of 10420 papers

TitleStatusHype
ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object DetectorCode0
SparseNet: A Sparse DenseNet for Image Classification0
Deep Neural Networks Motivated by Partial Differential EquationsCode0
Adversarial Alignment of Class Prediction Uncertainties for Domain Adaptation0
Pooling is neither necessary nor sufficient for appropriate deformation stability in CNNs0
Deep Learning For Computer Vision Tasks: A review0
A Systematic DNN Weight Pruning Framework using Alternating Direction Method of MultipliersCode0
Unsupervised and semi-supervised learning with Categorical Generative Adversarial Networks assisted by Wasserstein distance for dermoscopy image Classification0
AMNet: Memorability Estimation with AttentionCode0
NetAdapt: Platform-Aware Neural Network Adaptation for Mobile ApplicationsCode0
Assessment of Breast Cancer Histology using Densely Connected Convolutional Networks0
Ordinal Pooling Networks: For Preserving Information over Shrinking Feature MapsCode0
Learn To Pay AttentionCode0
The Tsetlin Machine - A Game Theoretic Bandit Driven Approach to Optimal Pattern Recognition with Propositional LogicCode0
Self-supervised Learning of Geometrically Stable Features Through Probabilistic Introspection0
Multi-Scale Spatially-Asymmetric Recalibration for Image Classification0
The Structure Transfer Machine Theory and ApplicationsCode0
In-depth Question classification using Convolutional Neural Networks0
Compare and Contrast: Learning Prominent Visual Differences0
Joint Optimization Framework for Learning with Noisy LabelsCode0
Parallel Grid Pooling for Data AugmentationCode0
Hierarchical Transfer Convolutional Neural Networks for Image Classification0
Class Subset Selection for Transfer Learning using Submodularity0
Fast Parametric Learning with Activation Memorization0
Canonical Correlation Analysis of Datasets with a Common Source Graph0
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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
10RevCol-HTop 1 Accuracy90Unverified