SOTAVerified

Object Recognition

Object recognition is a computer vision technique for detecting + classifying objects in images or videos. Since this is a combined task of object detection plus image classification, the state-of-the-art tables are recorded for each component task here and here.

( Image credit: Tensorflow Object Detection API )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 676–700 of 2042 papers

TitleStatusHype
MetaSketch: Wireless Semantic Segmentation by Metamaterial Surfaces—0
Patchwork: Concentric Zone-based Region-wise Ground Segmentation with Ground Likelihood Estimation Using a 3D LiDAR SensorCode1
Learning Canonical 3D Object Representation for Fine-Grained Recognition—0
An optical biomimetic eyes with interested object imaging—0
A Framework for Multi-View Classification of Features—0
Detectron2 Object Detection & Manipulating Images using CartoonizationCode4
Comparing object recognition in humans and deep convolutional neural networks -- An eye tracking study—0
To Boost or not to Boost: On the Limits of Boosted Neural Networks—0
Language Models as Zero-shot Visual Semantic Learners—0
A 51.3 TOPS/W, 134.4 GOPS In-memory Binary Image Filtering in 65nm CMOS—0
Deep Machine Learning Based Egyptian Vehicle License Plate Recognition Systems—0
Saliency for free: Saliency prediction as a side-effect of object recognition—0
Spectral Processing and Optimization of Static and Dynamic 3D Geometries—0
The Foes of Neural Network's Data Efficiency Among Unnecessary Input Dimensions—0
Applications of knowledge graphs for food science and industry—0
Prediction Surface Uncertainty Quantification in Object Detection Models for Autonomous DrivingCode0
On the Challenges of Open World Recognitionunder Shifting Visual DomainsCode1
Mind Your Outliers! Investigating the Negative Impact of Outliers on Active Learning for Visual Question AnsweringCode1
MixStyle Neural Networks for Domain Generalization and Adaptation—0
Hebbian learning with gradients: Hebbian convolutional neural networks with modern deep learning frameworksCode1
Parasitic Egg Detection and Classification in Low-cost Microscopic Images using Transfer Learning—0
Understanding Adversarial Examples Through Deep Neural Network's Response Surface and Uncertainty Regions—0
In-distribution adversarial attacks on object recognition models using gradient-free searchCode0
PatentNet: A Large-Scale Incomplete Multiview, Multimodal, Multilabel Industrial Goods Image Database—0
SISA: Securing Images by Selective Alteration—0
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All datasetsshape biasCIFAR10-DVSN-Caltech 101ObjectNet (All classes)ObjectNet (ImageNet classes)ObjectNet (ImageNet classes, trained on ImageNet)DVS128 GestureMECCANON-CARS

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Imagenshape bias98.7—Unverified
2Stable Diffusionshape bias92.7—Unverified
3Partishape bias91.7—Unverified
4ViT-22B-384shape bias86.4—Unverified
5ViT-22B-560shape bias83.8—Unverified
6CLIP (ViT-B)shape bias79.9—Unverified
7ViT-22B-224shape bias78—Unverified
8ResNet-50 (L2 eps 5.0 adv trained)shape bias69.5—Unverified
9ResNet-50 (with strong augmentations)shape bias62.2—Unverified
10SWSL (ResNeXt-101)shape bias49.8—Unverified
#ModelMetricClaimedVerifiedStatus
1Spike-VGG11Accuracy (% )85.55—Unverified
2SSNNAccuracy (% )78.57—Unverified
#ModelMetricClaimedVerifiedStatus
1Spike-VGG11Accuracy (% )85.62—Unverified
2SSNNAccuracy (% )79.25—Unverified
#ModelMetricClaimedVerifiedStatus
1ObjectNet-BaselineTop 5 Accuracy18.75—Unverified
2yunTop 5 Accuracy14.75—Unverified
#ModelMetricClaimedVerifiedStatus
1ObjectNet-BaselineTop 5 Accuracy52.24—Unverified
2DYTop 5 Accuracy0.08—Unverified
#ModelMetricClaimedVerifiedStatus
1ObjectNet-BaselineTop 5 Accuracy52.24—Unverified
2AJ2021Top 5 Accuracy27.68—Unverified
#ModelMetricClaimedVerifiedStatus
1SSNNAccuracy (% )94.91—Unverified
#ModelMetricClaimedVerifiedStatus
1Faster-RCNNmAP30.39—Unverified
#ModelMetricClaimedVerifiedStatus
1Spike-VGG11Accuracy (% )96—Unverified