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 1451–1475 of 2042 papers

TitleStatusHype
Meta-neural-network for Realtime and Passive Deep-learning-based Object Recognition—0
Metric Learning as a Service with Covariance Embedding—0
MIML-FCN+: Multi-instance Multi-label Learning via Fully Convolutional Networks with Privileged Information—0
Minimal Adversarial Examples for Deep Learning on 3D Point Clouds—0
Minimal Images in Deep Neural Networks: Fragile Object Recognition in Natural Images—0
MirBot: A collaborative object recognition system for smartphones using convolutional neural networks—0
Misalignment Resilient Diffractive Optical Networks—0
Missing Modalities Imputation via Cascaded Residual Autoencoder—0
Mitigating the Hubness Problem for Zero-Shot Learning of 3D Objects—0
MixStyle Neural Networks for Domain Generalization and Adaptation—0
MMSS: Multi-Modal Sharable and Specific Feature Learning for RGB-D Object Recognition—0
Modality and Component Aware Feature Fusion For RGB-D Scene Classification—0
Model alignment using inter-modal bridges—0
Model-based active learning to detect isometric deformable objects in the wild with deep architectures—0
Model compression as constrained optimization, with application to neural nets. Part I: general framework—0
Modeling biological face recognition with deep convolutional neural networks—0
Modeling Human Development: Effects of Blurred Vision on Category Learning in CNNs—0
Modeling infant object perception as program induction—0
Modeling Object Recognition in Newborn Chicks using Deep Neural Networks—0
Modeling the Contribution of Central Versus Peripheral Vision in Scene, Object, and Face Recognition—0
Modeling the Sequence of Brain Volumes by Local Mesh Models for Brain Decoding—0
Modelling Local Deep Convolutional Neural Network Features to Improve Fine-Grained Image Classification—0
Modelling of LIDAR sensor disturbances by solid airborne particles—0
Monocular SLAM Supported Object Recognition—0
MonoStream: A Minimal-Hardware High Accuracy Device-free WLAN Localization System—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