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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

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Showing 1–50 of 56 papers

TitleStatusHype
Beyond Appearance: a Semantic Controllable Self-Supervised Learning Framework for Human-Centric Visual TasksCode3
HumanBench: Towards General Human-centric Perception with Projector Assisted PretrainingCode2
Hulk: A Universal Knowledge Translator for Human-Centric TasksCode2
Pedestrian Attribute Recognition as Label-balanced Multi-label LearningCode1
PLIP: Language-Image Pre-training for Person Representation LearningCode1
C2T-Net: Channel-Aware Cross-Fused Transformer-Style Networks for Pedestrian Attribute RecognitionCode1
PARFormer: Transformer-based Multi-Task Network for Pedestrian Attribute RecognitionCode1
HAP: Structure-Aware Masked Image Modeling for Human-Centric PerceptionCode1
Spatio-Temporal Side Tuning Pre-trained Foundation Models for Video-based Pedestrian Attribute RecognitionCode1
Towards Unified Text-based Person Retrieval: A Large-scale Multi-Attribute and Language Search BenchmarkCode1
UAV-Human: A Large Benchmark for Human Behavior Understanding with Unmanned Aerial VehiclesCode1
UniHCP: A Unified Model for Human-Centric PerceptionsCode1
Densely Connected Convolutional NetworksCode1
UPAR: Unified Pedestrian Attribute Recognition and Person RetrievalCode1
Rethinking of Pedestrian Attribute Recognition: Realistic Datasets with Efficient MethodCode1
Rethinking of Pedestrian Attribute Recognition: A Reliable Evaluation under Zero-Shot Pedestrian Identity SettingCode1
Image-Based and Partially Categorical Annotating Approach for Pedestrian Attribute RecognitionCode1
Improving Pedestrian Attribute Recognition With Weakly-Supervised Multi-Scale Attribute-Specific LocalizationCode1
A Richly Annotated Dataset for Pedestrian Attribute RecognitionCode1
Label2Label: A Language Modeling Framework for Multi-Attribute LearningCode1
Learning CLIP Guided Visual-Text Fusion Transformer for Video-based Pedestrian Attribute RecognitionCode1
POAR: Towards Open Vocabulary Pedestrian Attribute RecognitionCode0
A Solution to Co-occurrence Bias: Attributes Disentanglement via Mutual Information Minimization for Pedestrian Attribute RecognitionCode0
SSPNet: Scale and Spatial Priors Guided Generalizable and Interpretable Pedestrian Attribute RecognitionCode0
ViTA-PAR: Visual and Textual Attribute Alignment with Attribute Prompting for Pedestrian Attribute RecognitionCode0
YinYang-Net: Complementing Face and Body Information for Wild Gender RecognitionCode0
HydraPlus-Net: Attentive Deep Features for Pedestrian AnalysisCode0
A Temporal Attentive Approach for Video-Based Pedestrian Attribute Recognition—0
Incremental Few-Shot Learning for Pedestrian Attribute Recognition—0
Weakly-supervised Learning of Mid-level Features for Pedestrian Attribute Recognition and Localization—0
Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition—0
An Empirical Study of Mamba-based Pedestrian Attribute Recognition—0
Attribute Aware Pooling for Pedestrian Attribute Recognition—0
Attribute Recognition by Joint Recurrent Learning of Context and Correlation—0
AttriVision: Advancing Generalization in Pedestrian Attribute Recognition using CLIP—0
Data Augmentation Imbalance For Imbalanced Attribute Classification—0
Deep Template Matching for Pedestrian Attribute Recognition with the Auxiliary Supervision of Attribute-wise Keypoints—0
Enhanced Visual-Semantic Interaction with Tailored Prompts for Pedestrian Attribute Recognition—0
FOCUS: Fine-grained Optimization with Semantic Guided Understanding for Pedestrian Attributes Recognition—0
Identity-Aware Attribute Recognition via Real-Time Distributed Inference in Mobile Edge Clouds—0
Knowledge Assembly: Semi-Supervised Multi-Task Learning from Multiple Datasets with Disjoint Labels—0
Learning Disentangled Label Representations for Multi-label Classification—0
Lightweight Attribute Localizing Models for Pedestrian Attribute Recognition—0
Localization Guided Learning for Pedestrian Attribute Recognition—0
Multi-Task Learning via Co-Attentive Sharing for Pedestrian Attribute Recognition—0
Pedestrian Attribute Recognition: A New Benchmark Dataset and A Large Language Model Augmented Framework—0
Pedestrian Attribute Recognition: A Survey—0
Pedestrian Attribute Recognition in Video Surveillance Scenarios Based on View-attribute Attention Localization—0
Pedestrian Attribute Recognition via CLIP based Prompt Vision-Language Fusion—0
Reinforced Pedestrian Attribute Recognition with Group Optimization Reward—0
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All datasetsPA-100KPETARAPv2RAPUAV-HumanDukeMTMC-attributeMarket1501-AttributesUPAR

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AttivisionF1 score93.1—Unverified
2PATH (Partial FT)Accuracy90.8—Unverified
3AttrivisionAccuracy 89.8—Unverified
4Hulk(Finetune, ViT-L)Accuracy88.97—Unverified
5Hulk(Finetune, ViT-B)Accuracy87.85—Unverified
6C2T-NetAccuracy87.2—Unverified
7SOLIDERAccuracy86.38—Unverified
8UniHCP (finetune)Accuracy86.18—Unverified
9APTMAccuracy80.17—Unverified
10Label2LabelAccuracy79.23—Unverified
#ModelMetricClaimedVerifiedStatus
1UniHCP (FT)Accuracy88.78—Unverified
2C2T-NetAccuracy88.2—Unverified
3ALM[tang2019Improving] (ICCV19)Accuracy79.52—Unverified
4Attribute-Specific LocalizationAccuracy79.52—Unverified
5strongbaselineAccuracy79.14—Unverified
6HP-netAccuracy76.13—Unverified
#ModelMetricClaimedVerifiedStatus
1Hulk(Finetune, ViT-L)Accuracy85.86—Unverified
2Hulk(Finetune, ViT-B)Accuracy85.26—Unverified
3AttrivisionAccuracy 84.2—Unverified
4UniHCP (finetune)Accuracy82.34—Unverified
#ModelMetricClaimedVerifiedStatus
1Attribute-Specific LocalizationAccuracy68.17—Unverified
2HP-netAccuracy65.39—Unverified
#ModelMetricClaimedVerifiedStatus
1DenseNetBackpack63.9—Unverified
2ResNetBackpack63.5—Unverified
#ModelMetricClaimedVerifiedStatus
1MBNETAccuracy91.13—Unverified
#ModelMetricClaimedVerifiedStatus
1AttrivisionAccuracy 83.8—Unverified
#ModelMetricClaimedVerifiedStatus
1AttrivisionAccuracy 89.4—Unverified