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 10051–10100 of 10420 papers

TitleStatusHype
On Complex Valued Convolutional Neural NetworksCode0
Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural NetworksCode0
On Study of the Binarized Deep Neural Network for Image Classification—0
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model sizeCode1
Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image AnnotationsCode1
Inception-v4, Inception-ResNet and the Impact of Residual Connections on LearningCode1
"Why Should I Trust You?": Explaining the Predictions of Any ClassifierCode1
Global Deconvolutional Networks for Semantic Segmentation—0
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning—0
Screen Content Image Segmentation Using Sparse Decomposition and Total Variation Minimization—0
Automatic and Quantitative evaluation of attribute discovery methods—0
Leveraging Mid-Level Deep Representations For Predicting Face Attributes in the Wild—0
Learning scale-variant and scale-invariant features for deep image classification—0
Unsupervised High-level Feature Learning by Ensemble Projection for Semi-supervised Image Classification and Image Clustering—0
What Can I Do Around Here? Deep Functional Scene Understanding for Cognitive Robots—0
Locally-Supervised Deep Hybrid Model for Scene Recognition—0
Compositional Model based Fisher Vector Coding for Image ClassificationCode0
Document image classification, with a specific view on applications of patent images—0
Learning Subclass Representations for Visually-varied Image Classification—0
Group Invariant Deep Representations for Image Instance Retrieval—0
Dense Bag-of-Temporal-SIFT-Words for Time Series Classification—0
Combined statistical and model based texture features for improved image classification—0
A Multiresolution Clinical Decision Support System Based on Fractal Model Design for Classification of Histological Brain Tumours—0
Convolutional Architecture Exploration for Action Recognition and Image Classification—0
Multi-Instance Visual-Semantic Embedding—0
Seeing through the Human Reporting Bias: Visual Classifiers from Noisy Human-Centric Labels—0
Sparse Coding with Fast Image Alignment via Large Displacement Optical Flow—0
Kernel principal component analysis network for image classification—0
Blockout: Dynamic Model Selection for Hierarchical Deep Networks—0
Semantic-enriched Visual Vocabulary Construction in a Weakly Supervised Context—0
Deep Learning Algorithms with Applications to Video Analytics for A Smart City: A Survey—0
Deep Residual Learning for Image RecognitionCode4
Distributed Training of Deep Neural Networks with Theoretical Analysis: Under SSP Setting—0
Embedding Label Structures for Fine-Grained Feature Representation—0
Explaining NonLinear Classification Decisions with Deep Taylor DecompositionCode0
Fine-grained Image Classification by Exploring Bipartite-Graph Labels—0
Rethinking the Inception Architecture for Computer VisionCode1
Structured Feature Selection—0
Task-Driven Feature Pooling for Image Classification—0
HD-CNN: Hierarchical Deep Convolutional Neural Networks for Large Scale Visual Recognition—0
Learning The Structure of Deep Convolutional Networks—0
MANTRA: Minimum Maximum Latent Structural SVM for Image Classification and Ranking—0
Aggregating Local Deep Features for Image Retrieval—0
Analyzing Classifiers: Fisher Vectors and Deep Neural Networks—0
Fine-Grained Classification via Mixture of Deep Convolutional Neural Networks—0
Design of Kernels in Convolutional Neural Networks for Image ClassificationCode0
Unsupervised Deep Feature Extraction for Remote Sensing Image Classification—0
Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)Code0
Recombinator Networks: Learning Coarse-to-Fine Feature AggregationCode0
Auxiliary Image Regularization for Deep CNNs with Noisy Labels—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