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 Added Most Hyped Most Active Needs Verification Most Verified

Showing 551–560 of 2042 papers

Title	Date	Tasks	Status	Score
Deep Predictive Coding Network with Local Recurrent Processing for Object Recognition	May 19, 2018	image-classificationImage Classification	CodeCode Available	5
Discriminative Unsupervised Feature Learning with Convolutional Neural Networks	Dec 1, 2014	General ClassificationImage Classification	CodeCode Available	5
Detecting semantic anomalies	Aug 13, 2019	Anomaly DetectionMulti-Task Learning	CodeCode Available	5
Fast Feature Fool: A data independent approach to universal adversarial perturbations	Jul 18, 2017	Object Recognition	CodeCode Available	5
Distinctive Image Features from Scale-Invariant Keypoints	Jan 5, 2004	Image RegistrationObject	CodeCode Available	5
Temporal-Coded Deep Spiking Neural Network with Easy Training and Robust Performance	Sep 24, 2019	Data AugmentationObject Recognition	CodeCode Available	5
Different Spectral Representations in Optimized Artificial Neural Networks and Brains	Aug 22, 2022	Adversarial AttackAdversarial Robustness	CodeCode Available	5
Finding Tiny Faces	Dec 13, 2016	Face DetectionObject Recognition	CodeCode Available	5
SharpNet: Fast and Accurate Recovery of Occluding Contours in Monocular Depth Estimation	May 21, 2019	Depth EstimationDepth Prediction	CodeCode Available	5
Directional Regularized Tensor Modeling for Video Rain Streaks Removal	Feb 19, 2019	Object RecognitionRain Removal	CodeCode Available	5

Show:10 25 50

← PrevPage 56 of 205Next →

All datasets shape bias CIFAR10-DVS N-Caltech 101 ObjectNet (All classes)ObjectNet (ImageNet classes)ObjectNet (ImageNet classes, trained on ImageNet)DVS128 Gesture MECCANO N-CARS

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	Imagen	shape bias	98.7	—	Unverified
2	Stable Diffusion	shape bias	92.7	—	Unverified
3	Parti	shape bias	91.7	—	Unverified
4	ViT-22B-384	shape bias	86.4	—	Unverified
5	ViT-22B-560	shape bias	83.8	—	Unverified
6	CLIP (ViT-B)	shape bias	79.9	—	Unverified
7	ViT-22B-224	shape bias	78	—	Unverified
8	ResNet-50 (L2 eps 5.0 adv trained)	shape bias	69.5	—	Unverified
9	ResNet-50 (with strong augmentations)	shape bias	62.2	—	Unverified
10	SWSL (ResNeXt-101)	shape bias	49.8	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Spike-VGG11	Accuracy (% )	85.55	—	Unverified
2	SSNN	Accuracy (% )	78.57	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Spike-VGG11	Accuracy (% )	85.62	—	Unverified
2	SSNN	Accuracy (% )	79.25	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ObjectNet-Baseline	Top 5 Accuracy	18.75	—	Unverified
2	yun	Top 5 Accuracy	14.75	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ObjectNet-Baseline	Top 5 Accuracy	52.24	—	Unverified
2	DY	Top 5 Accuracy	0.08	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ObjectNet-Baseline	Top 5 Accuracy	52.24	—	Unverified
2	AJ2021	Top 5 Accuracy	27.68	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	SSNN	Accuracy (% )	94.91	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Faster-RCNN	mAP	30.39	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Spike-VGG11	Accuracy (% )	96	—	Unverified