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 1581–1590 of 2042 papers

Title	Date	Tasks	Status
Analyzing the Dependency of ConvNets on Spatial Information	Feb 5, 2020	image-classificationImage Classification	—Unverified
Analyzing the Performance of Multilayer Neural Networks for Object Recognition	Jul 7, 2014	Object Recognition	—Unverified
An Application-Specific VLIW Processor with Vector Instruction Set for CNN Acceleration	Apr 10, 2019	Object Recognition	—Unverified
An Approach for Noise Removal on Depth Images	Feb 16, 2016	3D ReconstructionObject Recognition	—Unverified
An ecologically motivated image dataset for deep learning yields better models of human vision	Feb 15, 2021	Object Recognition	—Unverified
An Effective Training Method For Deep Convolutional Neural Network	Jul 31, 2017	Object Recognition	—Unverified
An Efficient Accelerator Design Methodology for Deformable Convolutional Networks	Jun 9, 2020	Object Recognition	—Unverified
An Efficient Edge Detection Approach to Provide Better Edge Connectivity for Image Analysis	Mar 13, 2019	Edge DetectionFace Detection	—Unverified
An Efficient Semi-Automated Scheme for Infrastructure LiDAR Annotation	Jan 25, 2023	Autonomous DrivingMulti-Object Tracking	—Unverified
A Neural Spiking Approach Compared to Deep Feedforward Networks on Stepwise Pixel Erasement	Dec 6, 2019	ClassificationGeneral Classification	—Unverified

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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