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 1771–1780 of 2042 papers

Title	Date	Tasks	Status
AGA: Attribute Guided Augmentation	Dec 8, 2016	AttributeData Augmentation	CodeCode Available
Continual Learning through Human-Robot Interaction: Human Perceptions of a Continual Learning Robot in Repeated Interactions	May 22, 2023	Continual LearningObject Recognition	CodeCode Available
How much human-like visual experience do current self-supervised learning algorithms need in order to achieve human-level object recognition?	Sep 23, 2021	Object RecognitionRepresentation Learning	CodeCode Available
Scene Recognition by Combining Local and Global Image Descriptors	Feb 21, 2017	Object RecognitionScene Recognition	CodeCode Available
Bayesian and Neural Inference on LSTM-based Object Recognition from Tactile and Kinesthetic Information	Jun 10, 2023	ObjectObject Recognition	CodeCode Available
Sublinear Partition Estimation	Aug 7, 2015	Language ModelingLanguage Modelling	CodeCode Available
UnSegGNet: Unsupervised Image Segmentation using Graph Neural Networks	May 9, 2024	Image SegmentationObject Recognition	CodeCode Available
Places205-VGGNet Models for Scene Recognition	Aug 7, 2015	Computational EfficiencyGPU	CodeCode Available
Multi-area Target Individual Detection with Free Drawing on Video	Jul 6, 2022	Object Recognition	CodeCode Available
A Framework of Transfer Learning in Object Detection for Embedded Systems	Nov 12, 2018	Hyperparameter OptimizationObject	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