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Pedestrian Attribute Recognition

Pedestrian attribution recognition is the task of recognizing pedestrian features - such as whether they are talking on a phone, whether they have a backpack, and so on.

( Image credit: HydraPlus-Net: Attentive Deep Features for Pedestrian Analysis )

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 41–50 of 56 papers

Title	Date	Tasks	Status	Hype
Identity-Aware Attribute Recognition via Real-Time Distributed Inference in Mobile Edge Clouds	Aug 12, 2020	AttributePedestrian Attribute Recognition	—Unverified	0
Rethinking of Pedestrian Attribute Recognition: Realistic Datasets with Efficient Method	May 25, 2020	AttributePedestrian Attribute Recognition	CodeCode Available	1
Data Augmentation Imbalance For Imbalanced Attribute Classification	Apr 19, 2020	AttributeClassification	—Unverified	0
Multi-Task Learning via Co-Attentive Sharing for Pedestrian Attribute Recognition	Apr 7, 2020	AttributeMulti-Task Learning	—Unverified	0
Improving Pedestrian Attribute Recognition With Weakly-Supervised Multi-Scale Attribute-Specific Localization	Oct 10, 2019	AttributePedestrian Attribute Recognition	CodeCode Available	1
Unsupervised Domain-Adaptive Person Re-identification Based on Attributes	Aug 27, 2019	AttributeDomain Adaptation	—Unverified	0
Attribute Aware Pooling for Pedestrian Attribute Recognition	Jul 27, 2019	AttributePedestrian Attribute Recognition	—Unverified	0
Incremental Few-Shot Learning for Pedestrian Attribute Recognition	Jun 2, 2019	AttributeFew-Shot Learning	—Unverified	0
Pedestrian Attribute Recognition: A Survey	Jan 22, 2019	AttributeMulti-Label Learning	CodeCode Available	0
A Temporal Attentive Approach for Video-Based Pedestrian Attribute Recognition	Jan 17, 2019	AttributePedestrian Attribute Recognition	—Unverified	0

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All datasets PA-100K PETA RAPv2 RAP UAV-Human DukeMTMC-attribute Market1501-Attributes UPAR

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	Attivision	F1 score	93.1	—	Unverified
2	PATH (Partial FT)	Accuracy	90.8	—	Unverified
3	Attrivision	Accuracy	89.8	—	Unverified
4	Hulk(Finetune, ViT-L)	Accuracy	88.97	—	Unverified
5	Hulk(Finetune, ViT-B)	Accuracy	87.85	—	Unverified
6	C2T-Net	Accuracy	87.2	—	Unverified
7	SOLIDER	Accuracy	86.38	—	Unverified
8	UniHCP (finetune)	Accuracy	86.18	—	Unverified
9	APTM	Accuracy	80.17	—	Unverified
10	Label2Label	Accuracy	79.23	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	UniHCP (FT)	Accuracy	88.78	—	Unverified
2	C2T-Net	Accuracy	88.2	—	Unverified
3	ALM[tang2019Improving] (ICCV19)	Accuracy	79.52	—	Unverified
4	Attribute-Specific Localization	Accuracy	79.52	—	Unverified
5	strongbaseline	Accuracy	79.14	—	Unverified
6	HP-net	Accuracy	76.13	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Hulk(Finetune, ViT-L)	Accuracy	85.86	—	Unverified
2	Hulk(Finetune, ViT-B)	Accuracy	85.26	—	Unverified
3	Attrivision	Accuracy	84.2	—	Unverified
4	UniHCP (finetune)	Accuracy	82.34	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Attribute-Specific Localization	Accuracy	68.17	—	Unverified
2	HP-net	Accuracy	65.39	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	DenseNet	Backpack	63.9	—	Unverified
2	ResNet	Backpack	63.5	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MBNET	Accuracy	91.13	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Attrivision	Accuracy	83.8	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Attrivision	Accuracy	89.4	—	Unverified