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 1481–1490 of 2042 papers

Title	Date	Tasks	Status
MotionRec: A Unified Deep Framework for Moving Object Recognition	Apr 14, 2020	Moving Object Detectionobject-detection	—Unverified
Motion Segmentation from a Moving Monocular Camera	Sep 24, 2023	ClusteringMotion Segmentation	—Unverified
Multi-3D-Models Registration-Based Augmented Reality (AR) Instructions for Assembly	Nov 27, 2023	3D AssemblyObject Recognition	—Unverified
MultiCrossViT: Multimodal Vision Transformer for Schizophrenia Prediction using Structural MRI and Functional Network Connectivity Data	Nov 12, 2022	Deep Learningimage-classification	—Unverified
Multi-Evidence Filtering and Fusion for Multi-Label Classification, Object Detection and Semantic Segmentation Based on Weakly Supervised Learning	Feb 26, 2018	ClusteringGeneral Classification	—Unverified
Multi-label Multiple Kernel Learning by Stochastic Approximation: Application to Visual Object Recognition	Dec 1, 2010	ObjectObject Recognition	—Unverified
Multi-label Object Attribute Classification using a Convolutional Neural Network	Nov 10, 2018	AttributeGeneral Classification	—Unverified
Multilevel Context Representation for Improving Object Recognition	Mar 19, 2017	Data AugmentationObject	—Unverified
Multimodal Skip-gram Using Convolutional Pseudowords	Nov 12, 2015	Object RecognitionRetrieval	—Unverified
Multiple Class Novelty Detection Under Data Distribution Shift	Aug 1, 2020	Domain AdaptationNovelty Detection	—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