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 371–380 of 2042 papers

Title	Date	Tasks	Status	Score
Exploring Novel Object Recognition and Spontaneous Location Recognition Machine Learning Analysis Techniques in Alzheimer's Mice	Dec 12, 2023	Object RecognitionPose Estimation	CodeCode Available	5
A Framework of Transfer Learning in Object Detection for Embedded Systems	Nov 12, 2018	Hyperparameter OptimizationObject	CodeCode Available	5
Comparing deep neural networks against humans: object recognition when the signal gets weaker	Jun 21, 2017	General ClassificationObject	CodeCode Available	5
Incremental Robot Learning of New Objects with Fixed Update Time	May 17, 2016	Active LearningGeneral Classification	CodeCode Available	5
Causal importance of orientation selectivity for generalization in image recognition	May 1, 2019	image-classificationImage Classification	CodeCode Available	5
Experiments with mmWave Automotive Radar Test-bed	Dec 29, 2019	Object Recognition	CodeCode Available	5
AGA: Attribute Guided Augmentation	Dec 8, 2016	AttributeData Augmentation	CodeCode Available	5
A Comparative Analysis on Bangla Handwritten Digit Recognition with Data Augmentation and Non-Augmentation Process	Jun 26, 2020	Data AugmentationHandwritten Digit Recognition	CodeCode Available	5
Compositional Clustering: Applications to Multi-Label Object Recognition and Speaker Identification	Sep 9, 2021	ClusteringFew-Shot Learning	CodeCode Available	5
Faster gaze prediction with dense networks and Fisher pruning	Jan 17, 2018	Gaze EstimationGaze Prediction	CodeCode Available	5

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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