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 1351–1375 of 2042 papers

TitleStatusHype
Latent Bi-constraint SVM for Video-based Object Recognition—0
Latent Cognizance: What Machine Really Learns—0
Latent Constrained Correlation Filter—0
Latent Object Characteristics Recognition with Visual to Haptic-Audio Cross-modal Transfer Learning—0
Layerwise complexity-matched learning yields an improved model of cortical area V2—0
Learn and Search: An Elegant Technique for Object Lookup using Contrastive Learning—0
Learned-Norm Pooling for Deep Feedforward and Recurrent Neural Networks—0
Learning about Canonical Views from Internet Image Collections—0
Learning a discriminative hidden part model for human action recognition—0
Learning and Calibrating Per-Location Classifiers for Visual Place Recognition—0
Learning and Transferring Mid-Level Image Representations using Convolutional Neural Networks—0
Learning Attributes Equals Multi-Source Domain Generalization—0
Learning Beyond Human Expertise with Generative Models for Dental Restorations—0
Learning by Asking Questions for Knowledge-based Novel Object Recognition—0
Learning Canonical 3D Object Representation for Fine-Grained Recognition—0
Learning Collections of Part Models for Object Recognition—0
Learning Compact Binary Descriptors With Unsupervised Deep Neural Networks—0
Learning data association without data association: An EM approach to neural assignment prediction—0
Learning Deep Features for Scene Recognition using Places Database—0
Learning Descriptors for Object Recognition and 3D Pose Estimation—0
Learning Detailed Face Reconstruction from a Single Image—0
Learning for Semantic Knowledge Base-Guided Online Feature Transmission in Dynamic Channels—0
Learning from Extrinsic and Intrinsic Supervisions for Domain Generalization—0
Learning From Less Data: A Unified Data Subset Selection and Active Learning Framework for Computer Vision—0
Learning From Less Data: Diversified Subset Selection and Active Learning in Image Classification Tasks—0
Show:102550
← PrevPage 55 of 82Next →
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