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 1871–1880 of 2042 papers

Title	Date	Tasks	Status
Self-Adaptable Templates for Feature Coding	Dec 1, 2014	image-classificationImage Classification	—Unverified
Fast Sublinear Sparse Representation using Shallow Tree Matching Pursuit	Dec 1, 2014	Compressive SensingDenoising	—Unverified
Zero-shot recognition with unreliable attributes	Dec 1, 2014	AttributeObject Recognition	—Unverified
Learning Deep Features for Scene Recognition using Places Database	Dec 1, 2014	DiversityObject	—Unverified
Discriminative Unsupervised Feature Learning with Convolutional Neural Networks	Dec 1, 2014	General ClassificationImage Classification	CodeCode Available
Deep Symmetry Networks	Dec 1, 2014	Object RecognitionTranslation	—Unverified
Fisher Vectors Derived from Hybrid Gaussian-Laplacian Mixture Models for Image Annotation	Nov 26, 2014	Image RetrievalObject Recognition	—Unverified
Encoding High Dimensional Local Features by Sparse Coding Based Fisher Vectors	Nov 24, 2014	Fine-Grained Image ClassificationGeneral Classification	—Unverified
Visual Sentiment Prediction with Deep Convolutional Neural Networks	Nov 21, 2014	Object RecognitionPrediction	—Unverified
Maximum Likelihood Directed Enumeration Method in Piecewise-Regular Object Recognition	Nov 20, 2014	Face RecognitionObject Recognition	—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