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 531–540 of 2042 papers

Title	Date	Tasks	Status	Hype
A Multi-purpose Realistic Haze Benchmark with Quantifiable Haze Levels and Ground Truth	Jun 13, 2022	Objectobject-detection	—Unverified	0
Sparse Mixture-of-Experts are Domain Generalizable Learners	Jun 8, 2022	Domain GeneralizationMixture-of-Experts	CodeCode Available	1
Improving Fairness in Large-Scale Object Recognition by CrowdSourced Demographic Information	Jun 2, 2022	Cultural Vocal Bursts Intensity PredictionFairness	—Unverified	0
ProxyMix: Proxy-based Mixup Training with Label Refinery for Source-Free Domain Adaptation	May 29, 2022	Domain AdaptationObject Recognition	CodeCode Available	1
Prune and distill: similar reformatting of image information along rat visual cortex and deep neural networks	May 27, 2022	ObjectObject Recognition	—Unverified	0
OTAdapt: Optimal Transport-based Approach For Unsupervised Domain Adaptation	May 22, 2022	Domain AdaptationObject Recognition	—Unverified	0
Robust Sensible Adversarial Learning of Deep Neural Networks for Image Classification	May 20, 2022	image-classificationImage Classification	CodeCode Available	0
Efficient visual object representation using a biologically plausible spike-latency code and winner-take-all inhibition	May 20, 2022	AllObject	—Unverified	0
The developmental trajectory of object recognition robustness: children are like small adults but unlike big deep neural networks	May 20, 2022	ObjectObject Recognition	CodeCode Available	0
Topologically Persistent Features-based Object Recognition in Cluttered Indoor Environments	May 16, 2022	Object RecognitionPoint Cloud Classification	—Unverified	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