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 671–680 of 2042 papers

Title	Date	Tasks	Status
Robustness of Humans and Machines on Object Recognition with Extreme Image Transformations	May 9, 2022	Object Recognition	—Unverified
Towards Robust 3D Object Recognition with Dense-to-Sparse Deep Domain Adaptation	May 7, 2022	3D Object RecognitionAutonomous Vehicles	—Unverified
Utility-Oriented Underwater Image Quality Assessment Based on Transfer Learning	May 7, 2022	Fish DetectionImage Quality Assessment	—Unverified
Lifelong Ensemble Learning based on Multiple Representations for Few-Shot Object Recognition	May 4, 2022	3D Object RecognitionEnsemble Learning	—Unverified
Multitask Network for Joint Object Detection, Semantic Segmentation and Human Pose Estimation in Vehicle Occupancy Monitoring	May 3, 2022	Autonomous Drivingobject-detection	—Unverified
Can domain adaptation make object recognition work for everyone?	Apr 23, 2022	Domain AdaptationObject	—Unverified
Label Efficient Regularization and Propagation for Graph Node Classification	Apr 19, 2022	AttributeBenchmarking	—Unverified
Sensitivity of sparse codes to image distortions	Apr 15, 2022	Object RecognitionSensitivity	—Unverified
Hybrid Predictive Coding: Inferring, Fast and Slow	Apr 5, 2022	Bayesian InferenceObject Recognition	—Unverified
Exploiting Temporal Relations on Radar Perception for Autonomous Driving	Apr 3, 2022	2D Object DetectionAutonomous Driving	—Unverified

Show:10 25 50

← PrevPage 68 of 205Next →

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