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 541–550 of 2042 papers

Title	Date	Tasks	Status	Score
Adding Knowledge to Unsupervised Algorithms for the Recognition of Intent	Nov 12, 2020	3D Object ReconstructionObject Recognition	CodeCode Available	5
Dense and Diverse Capsule Networks: Making the Capsules Learn Better	May 10, 2018	General Classificationimage-classification	CodeCode Available	5
Discriminative Unsupervised Feature Learning with Convolutional Neural Networks	Dec 1, 2014	General ClassificationImage Classification	CodeCode Available	5
Task-generalizable Adversarial Attack based on Perceptual Metric	Nov 22, 2018	Adversarial Attackobject-detection	CodeCode Available	5
Do deep nets really need weight decay and dropout?	Feb 20, 2018	Data AugmentationObject Recognition	CodeCode Available	5
Deep Reconstruction-Classification Networks for Unsupervised Domain Adaptation	Jul 12, 2016	ClassificationDomain Adaptation	CodeCode Available	5
Beyond accuracy: quantifying trial-by-trial behaviour of CNNs and humans by measuring error consistency	Jun 30, 2020	Decision MakingObject Recognition	CodeCode Available	5
Temporal-Coded Deep Spiking Neural Network with Easy Training and Robust Performance	Sep 24, 2019	Data AugmentationObject Recognition	CodeCode Available	5
Analysis and Optimization of Convolutional Neural Network Architectures	Jul 31, 2017	Data AugmentationObject Recognition	CodeCode Available	5
Deep Predictive Coding Network with Local Recurrent Processing for Object Recognition	May 19, 2018	image-classificationImage Classification	CodeCode Available	5

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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