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

Showing 19762000 of 2042 papers

TitleStatusHype
Efficient Event Stream Super-Resolution with Recursive Multi-Branch FusionCode0
A Dataset and Framework for Learning State-invariant Object RepresentationsCode0
Learning the Precise Feature for Cluster AssignmentCode0
Sparse 3D convolutional neural networksCode0
On zero-shot recognition of generic objectsCode0
Learning to Detect Important People in Unlabelled Images for Semi-supervised Important People DetectionCode0
Learning to Find Common Objects Across Few Image CollectionsCode0
Comparative Study Between Distance Measures On Supervised Optimum-Path Forest ClassificationCode0
Opening Deep Neural Networks with Generative ModelsCode0
OpenLORIS-Object: A Robotic Vision Dataset and Benchmark for Lifelong Deep LearningCode0
OpenNav: Efficient Open Vocabulary 3D Object Detection for Smart Wheelchair NavigationCode0
Open-Set 3D Semantic Instance Maps for Vision Language Navigation -- O3D-SIMCode0
Are Vision Transformers More Data Hungry Than Newborn Visual Systems?Code0
Deep Learning with Nonparametric ClusteringCode0
Theano-based Large-Scale Visual Recognition with Multiple GPUsCode0
EBPC: Extended Bit-Plane Compression for Deep Neural Network Inference and Training AcceleratorsCode0
Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNsCode0
Two-Stream Convolutional Networks for Dynamic Texture SynthesisCode0
Big-Little Net: An Efficient Multi-Scale Feature Representation for Visual and Speech RecognitionCode0
Sparse Transfer Learning via Winning Lottery TicketsCode0
Deep Learning and Its Applications to Machine Health Monitoring: A SurveyCode0
Comparative evaluation of CNN architectures for Image Caption GenerationCode0
Learning Where to Edit Vision TransformersCode0
A Classification approach towards Unsupervised Learning of Visual RepresentationsCode0
Collaboratively Weighting Deep and Classic Representation via L2 Regularization for Image ClassificationCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Imagenshape bias98.7Unverified
2Stable Diffusionshape bias92.7Unverified
3Partishape bias91.7Unverified
4ViT-22B-384shape bias86.4Unverified
5ViT-22B-560shape bias83.8Unverified
6CLIP (ViT-B)shape bias79.9Unverified
7ViT-22B-224shape bias78Unverified
8ResNet-50 (L2 eps 5.0 adv trained)shape bias69.5Unverified
9ResNet-50 (with strong augmentations)shape bias62.2Unverified
10SWSL (ResNeXt-101)shape bias49.8Unverified
#ModelMetricClaimedVerifiedStatus
1Spike-VGG11Accuracy (% )85.55Unverified
2SSNNAccuracy (% )78.57Unverified
#ModelMetricClaimedVerifiedStatus
1Spike-VGG11Accuracy (% )85.62Unverified
2SSNNAccuracy (% )79.25Unverified
#ModelMetricClaimedVerifiedStatus
1ObjectNet-BaselineTop 5 Accuracy18.75Unverified
2yunTop 5 Accuracy14.75Unverified
#ModelMetricClaimedVerifiedStatus
1ObjectNet-BaselineTop 5 Accuracy52.24Unverified
2DYTop 5 Accuracy0.08Unverified
#ModelMetricClaimedVerifiedStatus
1ObjectNet-BaselineTop 5 Accuracy52.24Unverified
2AJ2021Top 5 Accuracy27.68Unverified
#ModelMetricClaimedVerifiedStatus
1SSNNAccuracy (% )94.91Unverified
#ModelMetricClaimedVerifiedStatus
1Faster-RCNNmAP30.39Unverified
#ModelMetricClaimedVerifiedStatus
1Spike-VGG11Accuracy (% )96Unverified