# SSPNet: Scale and Spatial Priors Guided Generalizable and Interpretable Pedestrian Attribute Recognition arXiv: https://arxiv.org/abs/2312.06049 ยท HTML: https://mlsanity.com/paper/2312.06049/ ## Key facts - Cited by 7 papers in the map (data through 14 Oct 2026). - Builds on 24 of its 41 references that are in the map. - First posted on arXiv 11 Dec 2023. ## Builds on (most cited first, 24 of 24) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021) - [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016) - [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016) - [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015) - [Deformable Convolutional Networks](https://mlsanity.com/paper/1703.06211/) (2017) - [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) - [Pose-driven Deep Convolutional Model for Person Re-identification](https://mlsanity.com/paper/1709.08325/) (2017) - [SimCC: a Simple Coordinate Classification Perspective for Human Pose Estimation](https://mlsanity.com/paper/2107.03332/) (2021) - [Deep Imbalanced Attribute Classification using Visual Attention Aggregation](https://mlsanity.com/paper/1807.03903/) (2018) - [Learning Deep Context-aware Features over Body and Latent Parts for Person Re-identification](https://mlsanity.com/paper/1710.06555/) (2017) - [Pedestrian Attribute Recognition: A Survey](https://mlsanity.com/paper/1901.07474/) (2019) - [Improving Pedestrian Attribute Recognition With Weakly-Supervised Multi-Scale Attribute-Specific Localization](https://mlsanity.com/paper/1910.04562/) (2019) - [Spatial and Semantic Consistency Regularizations for Pedestrian Attribute Recognition](https://mlsanity.com/paper/2109.05686/) (2021) - [Rethinking of Pedestrian Attribute Recognition: A Reliable Evaluation under Zero-Shot Pedestrian Identity Setting](https://mlsanity.com/paper/2107.03576/) (2021) - [Attribute Recognition by Joint Recurrent Learning of Context and Correlation](https://mlsanity.com/paper/1709.08553/) (2017) - [Localization Guided Learning for Pedestrian Attribute Recognition](https://mlsanity.com/paper/1808.09102/) (2018) - [Clothing Co-Parsing by Joint Image Segmentation and Labeling](https://mlsanity.com/paper/1502.00739/) (2015) - [PARFormer: Transformer-based Multi-Task Network for Pedestrian Attribute Recognition](https://mlsanity.com/paper/2304.07230/) (2023) - [Distraction-Aware Feature Learning for Human Attribute Recognition via Coarse-to-Fine Attention Mechanism](https://mlsanity.com/paper/1911.11351/) (2019) - [UPAR: Unified Pedestrian Attribute Recognition and Person Retrieval](https://mlsanity.com/paper/2209.02522/) (2022) - [Weakly-supervised Learning of Mid-level Features for Pedestrian Attribute Recognition and Localization](https://mlsanity.com/paper/1611.05603/) (2016) - [Attribute Recognition from Adaptive Parts](https://mlsanity.com/paper/1607.01437/) (2016) ## Built on by (most cited first, 7 of 7) - [An Empirical Study of Mamba-based Pedestrian Attribute Recognition](https://mlsanity.com/paper/2407.10374/) (2024) - [Pedestrian Attribute Recognition: A New Benchmark Dataset and A Large Language Model Augmented Framework](https://mlsanity.com/paper/2408.09720/) (2024) - [RGB-Event based Pedestrian Attribute Recognition: A Benchmark Dataset and An Asymmetric RWKV Fusion Framework](https://mlsanity.com/paper/2504.10018/) (2025) - [Pedestrian Attribute Recognition via Hierarchical Cross-Modality HyperGraph Learning](https://mlsanity.com/paper/2509.22331/) (2025) - [A Data-Centric Approach to Pedestrian Attribute Recognition: Synthetic Augmentation via Prompt-driven Diffusion Models](https://mlsanity.com/paper/2509.02099/) (2025) - [Uncertainty-Aware Pedestrian Attribute Recognition via Evidential Deep Learning](https://mlsanity.com/paper/2604.26873/) (2026) - [VLM-PAR: A Vision Language Model for Pedestrian Attribute Recognition](https://mlsanity.com/paper/2512.22217/) (2025) ## Abstract (arXiv) Global feature based Pedestrian Attribute Recognition (PAR) models are often poorly localized when using Grad-CAM for attribute response analysis, which has a significant impact on the interpretability, generalizability and performance. Previous researches have attempted to improve generalization and interpretation through meticulous model design, yet they often have neglected or underutilized effective prior information crucial for PAR. To this end, a novel Scale and Spatial Priors Guided Network (SSPNet) is proposed for PAR, which is mainly composed of the Adaptive Feature Scale Selection (AFSS) and Prior Location Extraction (PLE) modules. The AFSS module learns to provide reasonable scale prior information for different attribute groups, allowing the model to focus on different levels of feature maps with varying semantic granularity. The PLE module reveals potential attribute spatial prior information, which avoids unnecessary attention on irrelevant areas and lowers the risk of model over-fitting. More specifically, the scale prior in AFSS is adaptively learned from different layers of feature pyramid with maximum accuracy, while the spatial priors in PLE can be revealed from part feature with different granularity (such as image blocks, human pose keypoint and sparse sampling points). Besides, a novel IoU based attribute localization metric is proposed for Weakly-supervised Pedestrian Attribute Localization (WPAL) based on the improved Grad-CAM for attribute response mask. The experimental results on the intra-dataset and cross-dataset evaluations demonstrate the effectiveness of our proposed method in terms of mean accuracy (mA). Furthermore, it also achieves superior performance on the PCS dataset for attribute localization in terms of IoU. Code will be released at https://github.com/guotengg/SSPNet.