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 931–940 of 2042 papers

Title	Date	Tasks	Status	Hype
When and how CNNs generalize to out-of-distribution category-viewpoint combinations	Jul 15, 2020	DiversityObject Recognition	CodeCode Available	1
Hardware Implementation of Hyperbolic Tangent Function using Catmull-Rom Spline Interpolation	Jul 13, 2020	Object Recognitionspeech-recognition	—Unverified	0
Nested Learning For Multi-Granular Tasks	Jul 13, 2020	Face IdentificationObject Recognition	—Unverified	0
Seeing eye-to-eye? A comparison of object recognition performance in humans and deep convolutional neural networks under image manipulation	Jul 13, 2020	AttributeImage Manipulation	—Unverified	0
Quaternion Capsule Networks	Jul 8, 2020	BenchmarkingObject Recognition	CodeCode Available	0
Single Shot MC Dropout Approximation	Jul 7, 2020	Autonomous DrivingObject Recognition	CodeCode Available	1
Are Labels Always Necessary for Classifier Accuracy Evaluation?	Jul 6, 2020	Object Recognitionregression	—Unverified	0
Progressive Tandem Learning for Pattern Recognition with Deep Spiking Neural Networks	Jul 2, 2020	Computational EfficiencyImage Reconstruction	—Unverified	0
Parkinson's Disease Detection Using Ensemble Architecture from MR Images	Jul 1, 2020	Decision MakingObject Recognition	—Unverified	0
Beyond accuracy: quantifying trial-by-trial behaviour of CNNs and humans by measuring error consistency	Jun 30, 2020	Decision MakingObject Recognition	CodeCode Available	0

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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