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 1101–1125 of 2042 papers

TitleStatusHype
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
Using Spatial Pooler of Hierarchical Temporal Memory to classify noisy videos with predefined complexity—0
Using Web Co-occurrence Statistics for Improving Image Categorization—0
Utility-Oriented Underwater Image Quality Assessment Based on Transfer Learning—0
UWN: A Large Multilingual Lexical Knowledge Base—0
UW-NET: AN INCEPTION-ATTENTION NETWORK FOR UNDERWATER IMAGE CLASSIFICATION—0
V1Net: A computational model of cortical horizontal connections—0
V^2R-Bench: Holistically Evaluating LVLM Robustness to Fundamental Visual Variations—0
V2X Cooperative Perception for Autonomous Driving: Recent Advances and Challenges—0
Variable-Viewpoint Representations for 3D Object Recognition—0
Variant Parallelism: Lightweight Deep Convolutional Models for Distributed Inference on IoT Devices—0
Variation of Gender Biases in Visual Recognition Models Before and After Finetuning—0
VDM-DA: Virtual Domain Modeling for Source Data-free Domain Adaptation—0
Ventral-Dorsal Neural Networks: Object Detection via Selective Attention—0
Show:102550
← PrevPage 45 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