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
CloudFort: Enhancing Robustness of 3D Point Cloud Classification Against Backdoor Attacks via Spatial Partitioning and Ensemble Prediction	Apr 22, 2024	3D Point Cloud ClassificationAutonomous Vehicles	—Unverified
Deep Learning for Material recognition: most recent advances and open challenges	Dec 14, 2020	Deep LearningMaterial Recognition	—Unverified
Deep Learning for the Classification of Lung Nodules	Nov 21, 2016	BIG-bench Machine LearningClassification	—Unverified
Deep Learning from Parametrically Generated Virtual Buildings for Real-World Object Recognition	Jan 3, 2023	3D ReconstructionObject Recognition	—Unverified
A Poodle or a Dog? Evaluating Automatic Image Annotation Using Human Descriptions at Different Levels of Granularity	Aug 1, 2014	Object Recognition	—Unverified
Cloud based Scalable Object Recognition from Video Streams using Orientation Fusion and Convolutional Neural Networks	Jun 19, 2021	ObjectObject Recognition	—Unverified
Deep Learning Techniques for Geospatial Data Analysis	Aug 30, 2020	Deep Learningimage-classification	—Unverified
Deep Learning with Energy-efficient Binary Gradient Cameras	Dec 3, 2016	Deep LearningFace Detection	—Unverified
Deep Learning with Logged Bandit Feedback	Jan 1, 2018	counterfactualDeep Learning	—Unverified
CLIP-Nav: Using CLIP for Zero-Shot Vision-and-Language Navigation	Nov 30, 2022	DiversityInstruction Following	—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