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 1101–1110 of 2042 papers

Title	Date	Tasks	Status
Unsupervised Network Pretraining via Encoding Human Design	Feb 19, 2015	Object Recognition	—Unverified
Unsupervised Object Discovery: A Comprehensive Survey and Unified Taxonomy	Oct 30, 2024	NavigateObject	—Unverified
Unsupervised Part Discovery via Feature Alignment	Dec 1, 2020	ObjectObject Recognition	—Unverified
Unsupervised Regenerative Learning of Hierarchical Features in Spiking Deep Networks for Object Recognition	Feb 3, 2016	DecoderGeneral Classification	—Unverified
Unsupervised Spiking Instance Segmentation on Event Data using STDP	Nov 9, 2021	Event-based visionFace Detection	—Unverified
Unsupervised Template Learning for Fine-Grained Object Recognition	Dec 1, 2012	ObjectObject Recognition	—Unverified
Unsupervised Transductive Domain Adaptation	Feb 10, 2016	Domain AdaptationObject Recognition	—Unverified
Unveiling the Potential of iMarkers: Invisible Fiducial Markers for Advanced Robotics	Jan 26, 2025	Object RecognitionScene Understanding	—Unverified
Using body-anchored priors for identifying actions in single images	Dec 1, 2010	Object Recognition	—Unverified
Using Human Brain Activity to Guide Machine Learning	Mar 16, 2017	BIG-bench Machine LearningHybrid Machine Learning	—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