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 411–420 of 2042 papers

Title	Date	Tasks	Status	Hype
DuEqNet: Dual-Equivariance Network in Outdoor 3D Object Detection for Autonomous Driving	Feb 27, 2023	3D Object DetectionAutonomous Driving	—Unverified	0
InOR-Net: Incremental 3D Object Recognition Network for Point Cloud Representation	Feb 20, 2023	3D Object RecognitionFairness	—Unverified	0
Deep Learning for Event-based Vision: A Comprehensive Survey and Benchmarks	Feb 17, 2023	DeblurringDeep Learning	CodeCode Available	1
Towards Local Visual Modeling for Image Captioning	Feb 13, 2023	Image CaptioningObject Recognition	CodeCode Available	1
Scaling Vision Transformers to 22 Billion Parameters	Feb 10, 2023	Action ClassificationFairness	CodeCode Available	0
Zero-Knowledge Zero-Shot Learning for Novel Visual Category Discovery	Feb 9, 2023	Generalized Zero-Shot LearningObject Recognition	—Unverified	0
Convolutional Neural Networks Trained to Identify Words Provide a Surprisingly Good Account of Visual Form Priming Effects	Feb 8, 2023	FormObject Recognition	—Unverified	0
Dynamic Atomic Column Detection in Transmission Electron Microscopy Videos via Ridge Estimation	Feb 2, 2023	Object Recognition	—Unverified	0
Diverse, Difficult, and Odd Instances (D2O): A New Test Set for Object Classification	Jan 29, 2023	MiscellaneousObject Recognition	CodeCode Available	0
Connecting metrics for shape-texture knowledge in computer vision	Jan 25, 2023	image-classificationImage 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