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 1881–1890 of 2042 papers

Title	Date	Tasks	Status
A Multi-viewpoint Outdoor Dataset for Human Action Recognition	Oct 7, 2021	Action RecognitionFace Recognition	CodeCode Available
Investigating Negation in Pre-trained Vision-and-language Models	Nov 1, 2021	NegationObject Recognition	CodeCode Available
Investigating the Gestalt Principle of Closure in Deep Convolutional Neural Networks	Nov 1, 2024	Object Recognition	CodeCode Available
Real Classification by Description: Extending CLIP's Limits of Part Attributes Recognition	Dec 18, 2024	AttributeDescriptive	CodeCode Available
Investigating the Nature of 3D Generalization in Deep Neural Networks	Apr 19, 2023	Object Recognition	CodeCode Available
Target-Aware Generative Augmentations for Single-Shot Adaptation	May 22, 2023	AttributeObject Recognition	CodeCode Available
Targeted View-Invariant Adversarial Perturbations for 3D Object Recognition	Dec 17, 2024	3D Object RecognitionAdversarial Robustness	CodeCode Available
Real-Time Correlation Tracking via Joint Model Compression and Transfer	Jul 23, 2019	Computational EfficiencyCPU	CodeCode Available
Dense and Diverse Capsule Networks: Making the Capsules Learn Better	May 10, 2018	General Classificationimage-classification	CodeCode Available
Fast Feature Fool: A data independent approach to universal adversarial perturbations	Jul 18, 2017	Object Recognition	CodeCode Available

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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