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
Deep Reinforcement Learning Models Predict Visual Responses in the Brain: A Preliminary Result	Jun 18, 2021	Deep Reinforcement LearningObject Recognition	—Unverified
Deep Scene Image Classification With the MFAFVNet	Oct 1, 2017	ClassificationGeneral Classification	—Unverified
DeepSIC: Deep Semantic Image Compression	Jan 29, 2018	BenchmarkingImage Compression	—Unverified
Deep Sliding Shapes for Amodal 3D Object Detection in RGB-D Images	Nov 7, 2015	3D Object DetectionObject	—Unverified
Deep Symmetry Networks	Dec 1, 2014	Object RecognitionTranslation	—Unverified
Deep Transfer Learning: A new deep learning glitch classification method for advanced LIGO	Jun 22, 2017	ClusteringGeneral Classification	—Unverified
Deep Trans-layer Unsupervised Networks for Representation Learning	Sep 27, 2015	Face VerificationObject Recognition	—Unverified
Deep Versus Wide Convolutional Neural Networks for Object Recognition on Neuromorphic System	Feb 7, 2018	ClassificationGeneral Classification	—Unverified
Deep Watershed Detector for Music Object Recognition	May 26, 2018	Information RetrievalMusic Information Retrieval	—Unverified
Deflating Dataset Bias Using Synthetic Data Augmentation	Apr 28, 2020	Autonomous VehiclesData Augmentation	—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