# Pedestrian Attribute Recognition in Video Surveillance Scenarios Based on View-attribute Attention Localization arXiv: https://arxiv.org/abs/2106.06485 ยท HTML: https://mlsanity.com/paper/2106.06485/ ## Key facts - Cited by 8 papers in the map (data through 14 Oct 2026). - Builds on 31 of its 55 references that are in the map. - First posted on arXiv 11 Jun 2021. ## Builds on (most cited first, 30 of 31) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015) - [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015) - [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017) - [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014) - [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018) - [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019) - [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016) - [Once-for-All: Train One Network and Specialize it for Efficient Deployment](https://mlsanity.com/paper/1908.09791/) (2019) - [Joint Discriminative and Generative Learning for Person Re-identification](https://mlsanity.com/paper/1904.07223/) (2019) - [Improving Person Re-identification by Attribute and Identity Learning](https://mlsanity.com/paper/1703.07220/) (2017) - [HydraPlus-Net: Attentive Deep Features for Pedestrian Analysis](https://mlsanity.com/paper/1709.09930/) (2017) - [Learning Spatial Regularization with Image-level Supervisions for Multi-label Image Classification](https://mlsanity.com/paper/1702.05891/) (2017) - [Deep Imbalanced Attribute Classification using Visual Attention Aggregation](https://mlsanity.com/paper/1807.03903/) (2018) - [Improving Pedestrian Attribute Recognition With Weakly-Supervised Multi-Scale Attribute-Specific Localization](https://mlsanity.com/paper/1910.04562/) (2019) - [SCT: Set Constrained Temporal Transformer for Set Supervised Action Segmentation](https://mlsanity.com/paper/2003.14266/) (2020) - [Set-Constrained Viterbi for Set-Supervised Action Segmentation](https://mlsanity.com/paper/2002.11925/) (2020) - [A Richly Annotated Dataset for Pedestrian Attribute Recognition](https://mlsanity.com/paper/1603.07054/) (2016) - [Attribute Recognition by Joint Recurrent Learning of Context and Correlation](https://mlsanity.com/paper/1709.08553/) (2017) - [Deep Attributes Driven Multi-Camera Person Re-identification](https://mlsanity.com/paper/1605.03259/) (2016) - [Localization Guided Learning for Pedestrian Attribute Recognition](https://mlsanity.com/paper/1808.09102/) (2018) - [Rethinking of Pedestrian Attribute Recognition: Realistic Datasets with Efficient Method](https://mlsanity.com/paper/2005.11909/) (2020) - [Pedestrian Detection aided by Deep Learning Semantic Tasks](https://mlsanity.com/paper/1412.0069/) (2014) - [Multi-view Face Detection Using Deep Convolutional Neural Networks](https://mlsanity.com/paper/1502.02766/) (2015) - [Distraction-Aware Feature Learning for Human Attribute Recognition via Coarse-to-Fine Attention Mechanism](https://mlsanity.com/paper/1911.11351/) (2019) - [Deep View-Sensitive Pedestrian Attribute Inference in an end-to-end Model](https://mlsanity.com/paper/1707.06089/) (2017) - [Learning View-Specific Deep Networks for Person Re-Identification](https://mlsanity.com/paper/1803.11333/) (2018) - [An Attention-Based Deep Learning Model for Multiple Pedestrian Attributes Recognition](https://mlsanity.com/paper/2004.01110/) (2020) - [Multi-Task Learning via Co-Attentive Sharing for Pedestrian Attribute Recognition](https://mlsanity.com/paper/2004.03164/) (2020) - [Deep Template Matching for Pedestrian Attribute Recognition with the Auxiliary Supervision of Attribute-wise Keypoints](https://mlsanity.com/paper/2011.06798/) (2020) ## Built on by (most cited first, 8 of 8) - [Deep Gradient Learning for Efficient Camouflaged Object Detection](https://mlsanity.com/paper/2205.12853/) (2022) - [Disambiguated Attention Embedding for Multi-Instance Partial-Label Learning](https://mlsanity.com/paper/2305.16912/) (2023) - [PrObeD: Proactive Object Detection Wrapper](https://mlsanity.com/paper/2310.18788/) (2023) - [Exploiting Conjugate Label Information for Multi-Instance Partial-Label Learning](https://mlsanity.com/paper/2408.14369/) (2024) - [Parsing Objects at a Finer Granularity: A Survey](https://mlsanity.com/paper/2212.13693/) (2022) - [ViTA-PAR: Visual and Textual Attribute Alignment with Attribute Prompting for Pedestrian Attribute Recognition](https://mlsanity.com/paper/2506.01411/) (2025) - [Reinforced Pedestrian Attribute Recognition with Group Optimization Reward](https://mlsanity.com/paper/2205.14042/) (2022) - [Shifting Spotlight for Co-supervision: A Simple yet Efficient Single-branch Network to See Through Camouflage](https://mlsanity.com/paper/2404.08936/) (2024) ## Abstract (arXiv) Pedestrian attribute recognition in surveillance scenarios is still a challenging task due to the inaccurate localization of specific attributes. In this paper, we propose a novel view-attribute localization method based on attention (VALA), which utilizes view information to guide the recognition process to focus on specific attributes and attention mechanism to localize specific attribute-corresponding areas. Concretely, view information is leveraged by the view prediction branch to generate four view weights that represent the confidences for attributes from different views. View weights are then delivered back to compose specific view-attributes, which will participate and supervise deep feature extraction. In order to explore the spatial location of a view-attribute, regional attention is introduced to aggregate spatial information and encode inter-channel dependencies of the view feature. Subsequently, a fine attentive attribute-specific region is localized, and regional weights for the view-attribute from different spatial locations are gained by the regional attention. The final view-attribute recognition outcome is obtained by combining the view weights with the regional weights. Experiments on three wide datasets (RAP, RAPv2, and PA-100K) demonstrate the effectiveness of our approach compared with state-of-the-art methods.