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 801–825 of 2042 papers

TitleStatusHype
Compositional Convolutional Neural Networks: A Robust and Interpretable Model for Object Recognition under Occlusion—0
Are Deep Neural Networks Adequate Behavioural Models of Human Visual Perception?—0
Fashioning with Networks: Neural Style Transfer to Design Clothes—0
Farm land weed detection with region-based deep convolutional neural networks—0
Complex-valued Iris Recognition Network—0
Complete End-To-End Low Cost Solution To a 3D Scanning System with Integrated Turntable—0
Forecasting Hands and Objects in Future Frames—0
Factorization of View-Object Manifolds for Joint Object Recognition and Pose Estimation—0
Face-space Action Recognition by Face-Object Interactions—0
Fourier descriptors based on the structure of the human primary visual cortex with applications to object recognition—0
Foveated Downsampling Techniques—0
Face processing emerges from object-trained convolutional neural networks—0
Comparing Photorealism in Game Engines for Synthetic Maritime Computer Vision Datasets—0
A recurrent multi-scale approach to RBG-D Object Recognition—0
A Convolutional Neural Network based Live Object Recognition System as Blind Aid—0
Face Identification with Second-Order Pooling—0
From the Virtual to the RealWorld: Referring to Objects in Real-World Spatial Scenes—0
From Virtual to Real: A Framework for Verbal Interaction with Robots—0
Fabric Surface Characterization: Assessment of Deep Learning-based Texture Representations Using a Challenging Dataset—0
From Zero-shot Learning to Conventional Supervised Classification: Unseen Visual Data Synthesis—0
Comparing object recognition in humans and deep convolutional neural networks -- An eye tracking study—0
EZSR: Event-based Zero-Shot Recognition—0
Extreme Image Transformations Facilitate Robust Latent Object Representations—0
A Real-time Junk Food Recognition System based on Machine Learning—0
Extreme Image Transformations Affect Humans and Machines Differently—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