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	Hype
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	0
The ObjectFolder Benchmark: Multisensory Learning with Neural and Real Objects	Jun 1, 2023	BenchmarkingObject	—Unverified	0
CVSNet: A Computer Implementation for Central Visual System of The Brain	May 31, 2023	Object Recognition	—Unverified	0
A Framework For Refining Text Classification and Object Recognition from Academic Articles	May 27, 2023	ArticlesObject Recognition	—Unverified	0
Discovering Novel Actions from Open World Egocentric Videos with Object-Grounded Visual Commonsense Reasoning	May 26, 2023	Object RecognitionVisual Commonsense Reasoning	—Unverified	0
Are Deep Neural Networks Adequate Behavioural Models of Human Visual Perception?	May 26, 2023	ObjectObject Recognition	—Unverified	0
Continual Learning through Human-Robot Interaction: Human Perceptions of a Continual Learning Robot in Repeated Interactions	May 22, 2023	Continual LearningObject Recognition	CodeCode Available	0
Target-Aware Generative Augmentations for Single-Shot Adaptation	May 22, 2023	AttributeObject Recognition	CodeCode Available	0
CNN-based Methods for Object Recognition with High-Resolution Tactile Sensors	May 21, 2023	Object RecognitionTransfer Learning	CodeCode Available	0
Paxion: Patching Action Knowledge in Video-Language Foundation Models	May 18, 2023	Action UnderstandingDiagnostic	CodeCode Available	1

Show:10 25 50

← PrevPage 38 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