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 926–950 of 2042 papers

TitleStatusHype
MIRAGE: A Multi-modal Benchmark for Spatial Perception, Reasoning, and Intelligence—0
An Efficient Semi-Automated Scheme for Infrastructure LiDAR Annotation—0
Human peripheral blur is optimal for object recognition—0
Deep Active Object Recognition by Joint Label and Action Prediction—0
Humans and deep networks largely agree on which kinds of variation make object recognition harder—0
Deep Affordance-grounded Sensorimotor Object Recognition—0
Human vs. Computer in Scene and Object Recognition—0
Hybrid Optimized Deep Convolution Neural Network based Learning Model for Object Detection—0
Hybrid Predictive Coding: Inferring, Fast and Slow—0
Hybrid SNN-ANN: Energy-Efficient Classification and Object Detection for Event-Based Vision—0
Hyper-Class Augmented and Regularized Deep Learning for Fine-Grained Image Classification—0
Improving Adversarial Robustness via Unlabeled Out-of-Domain Data—0
Hyper-parameter optimization of Deep Convolutional Networks for object recognition—0
Improving Multi-label Learning with Missing Labels by Structured Semantic Correlations—0
Incorporating Geo-Diverse Knowledge into Prompting for Increased Geographical Robustness in Object Recognition—0
Discovering Novel Actions from Open World Egocentric Videos with Object-Grounded Visual Commonsense Reasoning—0
iCub World: Friendly Robots Help Building Good Vision Data-Sets—0
Identifying Auxiliary or Adversarial Tasks Using Necessary Condition Analysis for Adversarial Multi-task Video Understanding—0
Identifying Object States in Cooking-Related Images—0
Identity documents recognition and detection using semantic segmentation with convolutional neural network—0
iLab-20M: A Large-Scale Controlled Object Dataset to Investigate Deep Learning—0
Illumination-Invariant Image from 4-Channel Images: The Effect of Near-Infrared Data in Shadow Removal—0
Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review—0
Image Captioning with Unseen Objects—0
BPGrad: Towards Global Optimality in Deep Learning via Branch and Pruning—0
Show:102550
← PrevPage 38 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