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 1576–1600 of 2042 papers

Title	Date	Tasks	Status
Higher-order Pooling of CNN Features via Kernel Linearization for Action Recognition	Jan 19, 2017	Action RecognitionFine-grained Action Recognition	—Unverified
Synthetic to Real Adaptation with Generative Correlation Alignment Networks	Jan 19, 2017	Domain AdaptationObject Recognition	—Unverified
Incremental Learning for Robot Perception through HRI	Jan 17, 2017	Incremental LearningObject	—Unverified
Bandwidth limited object recognition in high resolution imagery	Jan 16, 2017	Objectobject-detection	—Unverified
What are the visual features underlying human versus machine vision?	Jan 10, 2017	DiagnosticObject	—Unverified
Signature of Geometric Centroids for 3D Local Shape Description and Partial Shape Matching	Dec 26, 2016	3D Object RecognitionDenoising	—Unverified
Few-Shot Object Recognition from Machine-Labeled Web Images	Dec 19, 2016	Few-Shot LearningObject	—Unverified
Deep Learning and Its Applications to Machine Health Monitoring: A Survey	Dec 16, 2016	Deep LearningImage Segmentation	CodeCode Available
Beyond Holistic Object Recognition: Enriching Image Understanding with Part States	Dec 15, 2016	Human-Object Interaction DetectionImage Captioning	—Unverified
Finding Tiny Faces	Dec 13, 2016	Face DetectionObject Recognition	CodeCode Available
Statistics of Visual Responses to Object Stimuli from Primate AIT Neurons to DNN Neurons	Dec 12, 2016	ObjectObject Recognition	—Unverified
AGA: Attribute Guided Augmentation	Dec 8, 2016	AttributeData Augmentation	CodeCode Available
Learning Localized Geometric Features Using 3D-CNN: An Application to Manufacturability Analysis of Drilled Holes	Dec 7, 2016	Decision MakingObject	—Unverified
Deep Pyramidal Residual Networks with Separated Stochastic Depth	Dec 5, 2016	Object Recognition	—Unverified
Food Image Recognition by Using Convolutional Neural Networks (CNNs)	Dec 3, 2016	Data AugmentationGeneral Classification	CodeCode Available
Deep Learning with Energy-efficient Binary Gradient Cameras	Dec 3, 2016	Deep LearningFace Detection	—Unverified
Hierarchical Object Representation for Open-Ended Object Category Learning and Recognition	Dec 1, 2016	3D Object RecognitionObject	—Unverified
Learning Transferrable Representations for Unsupervised Domain Adaptation	Dec 1, 2016	Domain AdaptationObject Recognition	—Unverified
Improved Deep Metric Learning with Multi-class N-pair Loss Objective	Dec 1, 2016	ClusteringFace Verification	—Unverified
'Part'ly first among equals: Semantic part-based benchmarking for state-of-the-art object recognition systems	Nov 23, 2016	BenchmarkingObject	—Unverified
Deep Learning for the Classification of Lung Nodules	Nov 21, 2016	BIG-bench Machine LearningClassification	—Unverified
Object Recognition with and without Objects	Nov 20, 2016	ObjectObject Recognition	CodeCode Available
Multi-Scale Saliency Detection using Dictionary Learning	Nov 19, 2016	Dictionary LearningGeneral Classification	—Unverified
Finding Mirror Symmetry via Registration	Nov 18, 2016	Line DetectionObject Recognition	—Unverified
The Freiburg Groceries Dataset	Nov 17, 2016	BenchmarkingBIG-bench Machine Learning	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