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 1501–1525 of 2042 papers

TitleStatusHype
Broad Learning System: An Effective and Efficient Incremental Learning System Without the Need for Deep Architecture—0
Improving Gibbs Sampler Scan Quality with DoGS—0
Fast Feature Fool: A data independent approach to universal adversarial perturbationsCode0
Robust Visual Tracking via Hierarchical Convolutional FeaturesCode0
Embedding Visual Hierarchy with Deep Networks for Large-Scale Visual Recognition—0
Deep Discrete Hashing with Self-supervised Pairwise LabelsCode0
Model compression as constrained optimization, with application to neural nets. Part I: general framework—0
Exploration of object recognition from 3D point cloud—0
A Fast Method For Computing Principal Curvatures From Range ImagesCode0
BIND: Binary Integrated Net Descriptors for Texture-Less Object Recognition—0
AGA: Attribute-Guided AugmentationCode0
Locality-Sensitive Deconvolution Networks With Gated Fusion for RGB-D Indoor Semantic Segmentation—0
Obtaining referential word meanings from visual and distributional information: Experiments on object naming—0
Missing Modalities Imputation via Cascaded Residual Autoencoder—0
Deep Co-Occurrence Feature Learning for Visual Object RecognitionCode0
Image classification using local tensor singular value decompositions—0
A New Urban Objects Detection Framework Using Weakly Annotated Sets—0
Controlled Tactile Exploration and Haptic Object Recognition—0
Do Deep Neural Networks Suffer from Crowding?Code0
Deep Mixture of Diverse Experts for Large-Scale Visual Recognition—0
Deep Transfer Learning: A new deep learning glitch classification method for advanced LIGO—0
Comparing deep neural networks against humans: object recognition when the signal gets weakerCode0
Two-Stream Convolutional Networks for Dynamic Texture SynthesisCode0
Analysis of dropout learning regarded as ensemble learning—0
Human-like Clustering with Deep Convolutional Neural NetworksCode0
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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