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 1976–2000 of 2042 papers

Title	Date	Tasks	Status
Weakly Supervised Learning of Mid-Level Features with Beta-Bernoulli Process Restricted Boltzmann Machines	Jun 1, 2013	Object RecognitionWeakly-supervised Learning	—Unverified
Mesh Based Semantic Modelling for Indoor and Outdoor Scenes	Jun 1, 2013	ObjectObject Recognition	—Unverified
Perceptual Organization and Recognition of Indoor Scenes from RGB-D Images	Jun 1, 2013	Boundary DetectionContour Detection	—Unverified
PISA: Pixelwise Image Saliency by Aggregating Complementary Appearance Contrast Measures with Spatial Priors	Jun 1, 2013	Image SegmentationObject Recognition	—Unverified
Subspace Interpolation via Dictionary Learning for Unsupervised Domain Adaptation	Jun 1, 2013	Dictionary LearningDomain Adaptation	—Unverified
Sparse Output Coding for Large-Scale Visual Recognition	Jun 1, 2013	ClassificationGeneral Classification	—Unverified
Scene Parsing by Integrating Function, Geometry and Appearance Models	Jun 1, 2013	ObjectObject Recognition	—Unverified
Kernel Null Space Methods for Novelty Detection	Jun 1, 2013	Density EstimationNovelty Detection	—Unverified
BFO Meets HOG: Feature Extraction Based on Histograms of Oriented p.d.f. Gradients for Image Classification	Jun 1, 2013	ClassificationGeneral Classification	—Unverified
Learning Collections of Part Models for Object Recognition	Jun 1, 2013	ObjectObject Recognition	—Unverified
Light Field Distortion Feature for Transparent Object Recognition	Jun 1, 2013	ObjectObject Recognition	—Unverified
Cartesian K-Means	Jun 1, 2013	ClusteringObject Recognition	CodeCode Available
SLAM++: Simultaneous Localisation and Mapping at the Level of Objects	Jun 1, 2013	3D Object RecognitionDescriptive	—Unverified
Efficient 2D-to-3D Correspondence Filtering for Scalable 3D Object Recognition	Jun 1, 2013	3D Object RecognitionCPU	—Unverified
Fully-Connected CRFs with Non-Parametric Pairwise Potential	Jun 1, 2013	DenoisingDensity Estimation	—Unverified
Adaptive Active Learning for Image Classification	Jun 1, 2013	Active LearningClassification	—Unverified
Image Optimization and Prediction	May 10, 2013	Object RecognitionPrediction	—Unverified
An Adaptive Descriptor Design for Object Recognition in the Wild	May 1, 2013	Domain AdaptationObject	—Unverified
Reading Ancient Coin Legends: Object Recognition vs. OCR	Apr 26, 2013	ObjectObject Recognition	—Unverified
Image Retrieval based on Bag-of-Words model	Apr 18, 2013	General Classificationimage-classification	—Unverified
Rotational Projection Statistics for 3D Local Surface Description and Object Recognition	Apr 11, 2013	3D Object RecognitionObject	—Unverified
Amplitude-Based Approach to Evidence Accumulation	Mar 27, 2013	ObjectObject Recognition	—Unverified
Multiple Kernel Sparse Representations for Supervised and Unsupervised Learning	Mar 3, 2013	ClusteringDictionary Learning	—Unverified
ImageNet Classification with Deep Convolutional Neural Networks	Dec 1, 2012	General ClassificationGPU	CodeCode Available
Large Scale Distributed Deep Networks	Dec 1, 2012	CPUObject 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