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 531–540 of 2042 papers

Title	Date	Tasks	Status
Adversarial alignment: Breaking the trade-off between the strength of an attack and its relevance to human perception	Jun 5, 2023	Adversarial AttackAdversarial Robustness	—Unverified
The ObjectFolder Benchmark: Multisensory Learning with Neural and Real Objects	Jun 1, 2023	BenchmarkingObject	—Unverified
CVSNet: A Computer Implementation for Central Visual System of The Brain	May 31, 2023	Object Recognition	—Unverified
A Framework For Refining Text Classification and Object Recognition from Academic Articles	May 27, 2023	ArticlesObject Recognition	—Unverified
Are Deep Neural Networks Adequate Behavioural Models of Human Visual Perception?	May 26, 2023	ObjectObject Recognition	—Unverified
Discovering Novel Actions from Open World Egocentric Videos with Object-Grounded Visual Commonsense Reasoning	May 26, 2023	Object RecognitionVisual Commonsense Reasoning	—Unverified
Target-Aware Generative Augmentations for Single-Shot Adaptation	May 22, 2023	AttributeObject Recognition	CodeCode Available
Continual Learning through Human-Robot Interaction: Human Perceptions of a Continual Learning Robot in Repeated Interactions	May 22, 2023	Continual LearningObject Recognition	CodeCode Available
CNN-based Methods for Object Recognition with High-Resolution Tactile Sensors	May 21, 2023	Object RecognitionTransfer Learning	CodeCode Available
How Deep Learning Sees the World: A Survey on Adversarial Attacks & Defenses	May 18, 2023	Face RecognitionMalware Detection	—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