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 1551–1575 of 2042 papers

Title	Date	Tasks	Status
From Zero-shot Learning to Conventional Supervised Classification: Unseen Visual Data Synthesis	May 4, 2017	General ClassificationObject Recognition	—Unverified
Inception Recurrent Convolutional Neural Network for Object Recognition	Apr 25, 2017	ObjectObject Recognition	CodeCode Available
Towards Instance Segmentation with Object Priority: Prominent Object Detection and Recognition	Apr 24, 2017	Instance SegmentationObject	—Unverified
Residual Attention Network for Image Classification	Apr 23, 2017	General Classificationimage-classification	CodeCode Available
Track Everything: Limiting Prior Knowledge in Online Multi-Object Recognition	Apr 21, 2017	General ClassificationMulti-Object Tracking	—Unverified
Invariant recognition drives neural representations of action sequences	Apr 20, 2017	Action RecognitionObject Recognition	—Unverified
BranchConnect: Large-Scale Visual Recognition with Learned Branch Connections	Apr 20, 2017	Image CategorizationObject Recognition	—Unverified
Ranking to Learn: Feature Ranking and Selection via Eigenvector Centrality	Apr 18, 2017	feature selectionObject Recognition	—Unverified
Cutting the Error by Half: Investigation of Very Deep CNN and Advanced Training Strategies for Document Image Classification	Apr 11, 2017	document-image-classificationDocument Image Classification	CodeCode Available
Deep Affordance-grounded Sensorimotor Object Recognition	Apr 10, 2017	ObjectObject Recognition	—Unverified
Generate To Adapt: Aligning Domains using Generative Adversarial Networks	Apr 6, 2017	Domain AdaptationGenerative Adversarial Network	CodeCode Available
Pragmatic descriptions of perceptual stimuli	Apr 1, 2017	Image DescriptionObject Recognition	—Unverified
Learning with Privileged Information for Multi-Label Classification	Mar 29, 2017	Action Unit DetectionClassification	—Unverified
Object categorization in finer levels requires higher spatial frequencies, and therefore takes longer	Mar 29, 2017	Object CategorizationObject Recognition	—Unverified
Is Second-order Information Helpful for Large-scale Visual Recognition?	Mar 23, 2017	Object Recognition	CodeCode Available
Spatio-Temporal Facial Expression Recognition Using Convolutional Neural Networks and Conditional Random Fields	Mar 20, 2017	Facial Expression RecognitionFacial Expression Recognition (FER)	—Unverified
Multilevel Context Representation for Improving Object Recognition	Mar 19, 2017	Data AugmentationObject	—Unverified
Weakly-supervised DCNN for RGB-D Object Recognition in Real-World Applications Which Lack Large-scale Annotated Training Data	Mar 19, 2017	Object RecognitionWeakly-supervised Learning	CodeCode Available
Algorithms for Semantic Segmentation of Multispectral Remote Sensing Imagery using Deep Learning	Mar 19, 2017	object-detectionObject Detection	CodeCode Available
Using Human Brain Activity to Guide Machine Learning	Mar 16, 2017	BIG-bench Machine LearningHybrid Machine Learning	—Unverified
End-to-end Binary Representation Learning via Direct Binary Embedding	Mar 15, 2017	Image RetrievalObject Recognition	—Unverified
Discriminate-and-Rectify Encoders: Learning from Image Transformation Sets	Mar 14, 2017	Face VerificationObject Recognition	—Unverified
Geometry-Based Region Proposals for Real-Time Robot Detection of Tabletop Objects	Mar 14, 2017	Novel Object DetectionObject	CodeCode Available
Convolutional Spike Timing Dependent Plasticity based Feature Learning in Spiking Neural Networks	Mar 10, 2017	Object Recognition	—Unverified
Object classification in images of Neoclassical furniture using Deep Learning	Mar 7, 2017	ClassificationDeep 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