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 401–410 of 2042 papers

Title	Date	Tasks	Status	Hype
Investigating the Role of Attribute Context in Vision-Language Models for Object Recognition and Detection	Mar 17, 2023	AttributeContrastive Learning	—Unverified	0
Explainable GeoAI: Can saliency maps help interpret artificial intelligence's learning process? An empirical study on natural feature detection	Mar 16, 2023	Deep LearningObject Recognition	CodeCode Available	1
Improving Out-of-Distribution Detection with Disentangled Foreground and Background Features	Mar 15, 2023	DisentanglementObject Recognition	CodeCode Available	0
Feature representations useful for predicting image memorability	Mar 14, 2023	Object RecognitionOpen-Ended Question Answering	—Unverified	0
Machine Learning Computer Vision Applications for Spatial AI Object Recognition in Orange County, California	Mar 14, 2023	ObjectObject Recognition	—Unverified	0
Variation of Gender Biases in Visual Recognition Models Before and After Finetuning	Mar 14, 2023	Object Recognition	—Unverified	0
EvConv: Fast CNN Inference on Event Camera Inputs For High-Speed Robot Perception	Mar 8, 2023	Depth EstimationObject Recognition	—Unverified	0
Toward a Geometric Theory of Manifold Untangling	Mar 7, 2023	ObjectObject Recognition	—Unverified	0
Learning Efficient Coding of Natural Images with Maximum Manifold Capacity Representations	Mar 6, 2023	Contrastive LearningObject Recognition	CodeCode Available	1
Domain-aware Triplet loss in Domain Generalization	Mar 1, 2023	ClusteringDomain Generalization	CodeCode Available	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