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 1371–1380 of 2042 papers

Title	Date	Tasks	Status
Learning Detailed Face Reconstruction from a Single Image	Nov 15, 2016	Face ReconstructionObject Recognition	—Unverified
Learning for Semantic Knowledge Base-Guided Online Feature Transmission in Dynamic Channels	Nov 30, 2023	Autonomous VehiclesDecision Making	—Unverified
Learning from Extrinsic and Intrinsic Supervisions for Domain Generalization	Jul 18, 2020	Anomaly DetectionDomain Generalization	—Unverified
Learning From Less Data: A Unified Data Subset Selection and Active Learning Framework for Computer Vision	Jan 3, 2019	Active LearningBIG-bench Machine Learning	—Unverified
Learning From Less Data: Diversified Subset Selection and Active Learning in Image Classification Tasks	May 28, 2018	Active LearningBIG-bench Machine Learning	—Unverified
Learning Good Representation via Continuous Attention	Mar 29, 2019	ObjectObject Recognition	—Unverified
Learning Hard Alignments with Variational Inference	May 16, 2017	Hard AttentionImage Captioning	—Unverified
Learning Hierarchical Sparse Representations using Iterative Dictionary Learning and Dimension Reduction	Jun 2, 2011	Dictionary LearningDimensionality Reduction	—Unverified
Learning Illuminant Estimation from Object Recognition	May 23, 2018	Color ConstancyDeep Learning	—Unverified
Learning Image Embeddings using Convolutional Neural Networks for Improved Multi-Modal Semantics	Oct 1, 2014	Object RecognitionTransfer Learning	—Unverified

Show:10 25 50

← PrevPage 138 of 205Next →

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