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 491–500 of 2042 papers

Title	Date	Tasks	Status	Hype
Continual Learning for Class- and Domain-Incremental Semantic Segmentation	Sep 16, 2022	class-incremental learningClass Incremental Learning	—Unverified	0
Visual Recognition with Deep Nearest Centroids	Sep 15, 2022	Decision Makingimage-classification	CodeCode Available	1
Data Augmentation by Selecting Mixed Classes Considering Distance Between Classes	Sep 12, 2022	Data AugmentationDiversity	—Unverified	0
Continual Learning for Pose-Agnostic Object Recognition in 3D Point Clouds	Sep 11, 2022	Continual LearningObject Recognition	—Unverified	0
Measuring Human Perception to Improve Open Set Recognition	Sep 8, 2022	Object RecognitionOpen Set Learning	—Unverified	0
Low-Energy Convolutional Neural Networks (CNNs) using Hadamard Method	Sep 6, 2022	Object Recognition	—Unverified	0
Improving the Accuracy and Robustness of CNNs Using a Deep CCA Neural Data Regularizer	Sep 6, 2022	Object Recognition	—Unverified	0
Chosen methods of improving small object recognition with weak recognizable features	Aug 29, 2022	DiversityObject	—Unverified	0
A Simulation Method for MMW Radar Sensing in Traffic Intersection Based on BART Algorithm	Aug 23, 2022	Object Recognition	—Unverified	0
How good are deep models in understanding the generated images?	Aug 23, 2022	ObjectObject Recognition	—Unverified	0

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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