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 1376–1400 of 2042 papers

TitleStatusHype
Unpaired Image Captioning by Image-level Weakly-Supervised Visual Concept Recognition—0
Unsupervised AER Object Recognition Based on Multiscale Spatio-Temporal Features and Spiking Neurons—0
Unsupervised Cross-Domain Recognition by Identifying Compact Joint Subspaces—0
Unsupervised Domain Adaptation using Regularized Hyper-graph Matching—0
Unsupervised Domain Adaptation using Graph Transduction Games—0
Unsupervised feature learning by augmenting single images—0
Unsupervised Feature Learning by Deep Sparse Coding—0
Unsupervised Feature Learning for Event Data: Direct vs Inverse Problem Formulation—0
Unsupervised Feature Learning with C-SVDDNet—0
Unsupervised Foveal Vision Neural Networks with Top-Down Attention—0
Unsupervised Generation of a Viewpoint Annotated Car Dataset From Videos—0
Unsupervised Learning of Invariant Representations in Hierarchical Architectures—0
Unsupervised Learning using Pretrained CNN and Associative Memory Bank—0
Unsupervised Network Pretraining via Encoding Human Design—0
Unsupervised Object Discovery: A Comprehensive Survey and Unified Taxonomy—0
Unsupervised Part Discovery via Feature Alignment—0
Unsupervised Regenerative Learning of Hierarchical Features in Spiking Deep Networks for Object Recognition—0
Unsupervised Spiking Instance Segmentation on Event Data using STDP—0
Unsupervised Template Learning for Fine-Grained Object Recognition—0
Unsupervised Transductive Domain Adaptation—0
Unveiling the Potential of iMarkers: Invisible Fiducial Markers for Advanced Robotics—0
Using body-anchored priors for identifying actions in single images—0
Using Human Brain Activity to Guide Machine Learning—0
Using Motion and Internal Supervision in Object Recognition—0
Using Satellite Imagery for Good: Detecting Communities in Desert and Mapping Vaccination Activities—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