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 576–600 of 2042 papers

TitleStatusHype
Deep Multi-View Learning using Neuron-Wise Correlation-Maximizing Regularizers—0
Deep Models for Multi-View 3D Object Recognition: A Review—0
Deep Mixture of Diverse Experts for Large-Scale Visual Recognition—0
Basic Level Categorization Facilitates Visual Object Recognition—0
An Adaptive Descriptor Design for Object Recognition in the Wild—0
Deep Machine Learning Based Egyptian Vehicle License Plate Recognition Systems—0
DeepLogo: Hitting Logo Recognition with the Deep Neural Network Hammer—0
Deep Learning with Logged Bandit Feedback—0
Bandwidth limited object recognition in high resolution imagery—0
Deep Learning with Energy-efficient Binary Gradient Cameras—0
Deep Learning Techniques for Geospatial Data Analysis—0
Background Invariance Testing According to Semantic Proximity—0
Deep learning systems as complex networks—0
Deep Learning Object Detection Methods for Ecological Camera Trap Data—0
A Variational Feature Encoding Method of 3D Object for Probabilistic Semantic SLAM—0
A Multisensory Learning Architecture for Rotation-invariant Object Recognition—0
A biologically plausible network for the computation of orientation dominance—0
Deep Learning from Parametrically Generated Virtual Buildings for Real-World Object Recognition—0
Deep Learning for the Classification of Lung Nodules—0
Autonomous Manipulation Learning for Similar Deformable Objects via Only One Demonstration—0
Deep Learning for Material recognition: most recent advances and open challenges—0
Automatic Ultrasound Image Segmentation of Supraclavicular Nerve Using Dilated U-Net Deep Learning Architecture—0
A Multi-purpose Realistic Haze Benchmark with Quantifiable Haze Levels and Ground Truth—0
Deep-Learning Convolutional Neural Networks for scattered shrub detection with Google Earth Imagery—0
Deep learning based infrared small object segmentation: Challenges and future directions—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