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 771–780 of 2042 papers

Title	Date	Tasks	Status
Compositional Clustering: Applications to Multi-Label Object Recognition and Speaker Identification	Sep 9, 2021	ClusteringFew-Shot Learning	CodeCode Available
Object recognition for robotics from tactile time series data utilising different neural network architectures	Sep 9, 2021	Object RecognitionTime Series	—Unverified
Temporal RoI Align for Video Object Recognition	Sep 8, 2021	Instance SegmentationObject	—Unverified
Capturing the objects of vision with neural networks	Sep 7, 2021	ObjectObject Recognition	—Unverified
Recognition Awareness: An Application of Latent Cognizance to Open-Set Recognition	Aug 27, 2021	ObjectObject Recognition	—Unverified
An Underwater Image Semantic Segmentation Method Focusing on Boundaries and a Real Underwater Scene Semantic Segmentation Dataset	Aug 26, 2021	Boundary DetectionInstance Segmentation	CodeCode Available
Airplane Type Identification Based on Mask RCNN; An Approach to Reduce Airport Traffic Congestion	Aug 25, 2021	ObjectObject Recognition	—Unverified
Reducing Label Effort: Self-Supervised meets Active Learning	Aug 25, 2021	Active LearningObject Recognition	—Unverified
OOWL500: Overcoming Dataset Collection Bias in the Wild	Aug 24, 2021	Adversarial AttackData Augmentation	—Unverified
Deep Bayesian Image Set Classification: A Defence Approach against Adversarial Attacks	Aug 23, 2021	Face RecognitionObject Recognition	—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