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 1851–1875 of 2042 papers

TitleStatusHype
Deep Learning from Parametrically Generated Virtual Buildings for Real-World Object Recognition—0
Deep Learning Object Detection Methods for Ecological Camera Trap Data—0
Deep learning systems as complex networks—0
Deep Learning Techniques for Geospatial Data Analysis—0
Deep Learning with Energy-efficient Binary Gradient Cameras—0
Deep Learning with Logged Bandit Feedback—0
DeepLogo: Hitting Logo Recognition with the Deep Neural Network Hammer—0
Deep Machine Learning Based Egyptian Vehicle License Plate Recognition Systems—0
Deep Mixture of Diverse Experts for Large-Scale Visual Recognition—0
Deep Models for Multi-View 3D Object Recognition: A Review—0
Deep Multi-View Learning using Neuron-Wise Correlation-Maximizing Regularizers—0
Deep Network Guided Proof Search—0
Deep Networks Can Resemble Human Feed-forward Vision in Invariant Object Recognition—0
Deep Neural Networks Can Learn Generalizable Same-Different Visual Relations—0
Deep Neural Networks predict Hierarchical Spatio-temporal Cortical Dynamics of Human Visual Object Recognition—0
Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition—0
Deep Optical Coding Design in Computational Imaging—0
Deep Predictive Coding Network for Object Recognition—0
Deep Pyramidal Residual Networks with Separated Stochastic Depth—0
Deep RBFNet: Point Cloud Feature Learning using Radial Basis Functions—0
Deep Reinforcement Learning Models Predict Visual Responses in the Brain: A Preliminary Result—0
Deep Scene Image Classification With the MFAFVNet—0
DeepSIC: Deep Semantic Image Compression—0
Deep Sliding Shapes for Amodal 3D Object Detection in RGB-D Images—0
Deep Symmetry Networks—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