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 1851–1875 of 2042 papers

Title	Date	Tasks	Status
Learning Descriptors for Object Recognition and 3D Pose Estimation	Feb 20, 2015	3D Pose EstimationObject	—Unverified
Scalable Bayesian Optimization Using Deep Neural Networks	Feb 19, 2015	Bayesian OptimizationCaption Generation	CodeCode Available
Unsupervised Network Pretraining via Encoding Human Design	Feb 19, 2015	Object Recognition	—Unverified
Measuring and Understanding Sensory Representations within Deep Networks Using a Numerical Optimization Framework	Feb 17, 2015	Object Recognition	—Unverified
DeepID3: Face Recognition with Very Deep Neural Networks	Feb 3, 2015	Face IdentificationFace Recognition	CodeCode Available
Incorporating Structural Alternatives and Sharing into Hierarchy for Multiclass Object Recognition and Detection	Feb 3, 2015	Object Recognitionvalid	—Unverified
Hyper-parameter optimization of Deep Convolutional Networks for object recognition	Jan 30, 2015	BIG-bench Machine LearningObject	—Unverified
Deep Learning with Nonparametric Clustering	Jan 13, 2015	ClusteringDeep Learning	CodeCode Available
A Survey on Recent Advances of Computer Vision Algorithms for Egocentric Video	Jan 12, 2015	Action DetectionActivity Detection	—Unverified
From Visual Attributes to Adjectives through Decompositional Distributional Semantics	Jan 12, 2015	AttributeObject	—Unverified
Joint Deep Learning for Car Detection	Dec 25, 2014	Deep Learningobject-detection	—Unverified
Multiple Object Recognition with Visual Attention	Dec 24, 2014	ObjectObject Recognition	CodeCode Available
Unsupervised Feature Learning with C-SVDDNet	Dec 23, 2014	Image ClassificationObject Recognition	—Unverified
Striving for Simplicity: The All Convolutional Net	Dec 21, 2014	AllImage Classification	CodeCode Available
Self-informed neural network structure learning	Dec 20, 2014	Object Recognition	—Unverified
An Analysis of Unsupervised Pre-training in Light of Recent Advances	Dec 20, 2014	Data AugmentationImage Classification	CodeCode Available
Compressing Deep Convolutional Networks using Vector Quantization	Dec 18, 2014	ClassificationClustering	—Unverified
Towards Open World Recognition	Dec 18, 2014	Object Recognition	—Unverified
Object Recognition Using Deep Neural Networks: A Survey	Dec 10, 2014	ObjectObject Recognition	—Unverified
Theano-based Large-Scale Visual Recognition with Multiple GPUs	Dec 7, 2014	GPUObject Recognition	CodeCode Available
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

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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