# GSAlign: Geometric and Semantic Alignment Network for Aerial-Ground Person Re-Identification arXiv: https://arxiv.org/abs/2510.22268 ยท HTML: https://mlsanity.com/paper/2510.22268/ ## Key facts - Cited by 5 papers in the map (data through 14 Oct 2026). - Builds on 39 of its 53 references that are in the map. - First posted on arXiv 25 Oct 2025. ## Builds on (most cited first, 30 of 39) - [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020) - [Deep Learning for Person Re-identification: A Survey and Outlook](https://mlsanity.com/paper/2001.04193/) (2020) - [TransReID: Transformer-based Object Re-Identification](https://mlsanity.com/paper/2102.04378/) (2021) - [Bag of Tricks and A Strong Baseline for Deep Person Re-identification](https://mlsanity.com/paper/1903.07071/) (2019) - [Learning Discriminative Features with Multiple Granularities for Person Re-Identification](https://mlsanity.com/paper/1804.01438/) (2018) - [CLIP-ReID: Exploiting Vision-Language Model for Image Re-Identification without Concrete Text Labels](https://mlsanity.com/paper/2211.13977/) (2022) - [FastReID: A Pytorch Toolbox for General Instance Re-identification](https://mlsanity.com/paper/2006.02631/) (2020) - [ABD-Net: Attentive but Diverse Person Re-Identification](https://mlsanity.com/paper/1908.01114/) (2019) - [Diverse Part Discovery: Occluded Person Re-identification with Part-Aware Transformer](https://mlsanity.com/paper/2106.04095/) (2021) - [High-Order Information Matters: Learning Relation and Topology for Occluded Person Re-Identification](https://mlsanity.com/paper/2003.08177/) (2020) - [UAV-Human: A Large Benchmark for Human Behavior Understanding with Unmanned Aerial Vehicles](https://mlsanity.com/paper/2104.00946/) (2021) - [Diverse Embedding Expansion Network and Low-Light Cross-Modality Benchmark for Visible-Infrared Person Re-identification](https://mlsanity.com/paper/2303.14481/) (2023) - [Learning Generalisable Omni-Scale Representations for Person Re-Identification](https://mlsanity.com/paper/1910.06827/) (2019) - [Noisy-Correspondence Learning for Text-to-Image Person Re-identification](https://mlsanity.com/paper/2308.09911/) (2023) - [Person Re-identification in Aerial Imagery](https://mlsanity.com/paper/1908.05024/) (2019) - [Part-Aware Transformer for Generalizable Person Re-identification](https://mlsanity.com/paper/2308.03322/) (2023) - [Magic Tokens: Select Diverse Tokens for Multi-modal Object Re-Identification](https://mlsanity.com/paper/2403.10254/) (2024) - [Dynamic Prototype Mask for Occluded Person Re-Identification](https://mlsanity.com/paper/2207.09046/) (2022) - [DeMo: Decoupled Feature-Based Mixture of Experts for Multi-Modal Object Re-Identification](https://mlsanity.com/paper/2412.10650/) (2024) - [Implicit Discriminative Knowledge Learning for Visible-Infrared Person Re-Identification](https://mlsanity.com/paper/2403.11708/) (2024) - [View-decoupled Transformer for Person Re-identification under Aerial-ground Camera Network](https://mlsanity.com/paper/2403.14513/) (2024) - [Vehicle Re-Identification: an Efficient Baseline Using Triplet Embedding](https://mlsanity.com/paper/1901.01015/) (2019) - [AG-ReID.v2: Bridging Aerial and Ground Views for Person Re-identification](https://mlsanity.com/paper/2401.02634/) (2024) - [Aerial-Ground Person Re-ID](https://mlsanity.com/paper/2303.08597/) (2023) - [Unified Pre-training with Pseudo Texts for Text-To-Image Person Re-identification](https://mlsanity.com/paper/2309.01420/) (2023) - [DC-Former: Diverse and Compact Transformer for Person Re-Identification](https://mlsanity.com/paper/2302.14335/) (2023) - [RLE: A Unified Perspective of Data Augmentation for Cross-Spectral Re-identification](https://mlsanity.com/paper/2411.01225/) (2024) - [SeCap: Self-Calibrating and Adaptive Prompts for Cross-view Person Re-Identification in Aerial-Ground Networks](https://mlsanity.com/paper/2503.06965/) (2025) - [PartFormer: Awakening Latent Diverse Representation from Vision Transformer for Object Re-Identification](https://mlsanity.com/paper/2408.16684/) (2024) - [Class-level Structural Relation Modelling and Smoothing for Visual Representation Learning](https://mlsanity.com/paper/2308.04142/) (2023) ## Built on by (most cited first, 5 of 5) - [3D-LENS: A 3D Lifting-based Elevated Novel-view Synthesis method for Single-View Aerial-Ground Re-Identification](https://mlsanity.com/paper/2604.26520/) (2026) - [Rectifying Geometry-Induced Similarity Distortions for Real-World Aerial-Ground Person Re-Identification](https://mlsanity.com/paper/2601.21405/) (2026) - [FUSE: Frequency-domain Unification and Spectral Energy Alignment for Multi-modal Object Re-Identification](https://mlsanity.com/paper/2606.20044/) (2026) - [HiHR: Hierarchical Hyperbolic Representation for Aerial-Ground Person Re-Identification](https://mlsanity.com/paper/2607.09186/) (2026) - [Reliability-Aware 3D Geometric Injection for Universal Person Re-identification](https://mlsanity.com/paper/2607.18863/) (2026) ## Abstract (arXiv) Aerial-Ground person re-identification (AG-ReID) is an emerging yet challenging task that aims to match pedestrian images captured from drastically different viewpoints, typically from unmanned aerial vehicles (UAVs) and ground-based surveillance cameras. The task poses significant challenges due to extreme viewpoint discrepancies, occlusions, and domain gaps between aerial and ground imagery. While prior works have made progress by learning cross-view representations, they remain limited in handling severe pose variations and spatial misalignment. To address these issues, we propose a Geometric and Semantic Alignment Network (GSAlign) tailored for AG-ReID. GSAlign introduces two key components to jointly tackle geometric distortion and semantic misalignment in aerial-ground matching: a Learnable Thin Plate Spline (LTPS) Module and a Dynamic Alignment Module (DAM). The LTPS module adaptively warps pedestrian features based on a set of learned keypoints, effectively compensating for geometric variations caused by extreme viewpoint changes. In parallel, the DAM estimates visibility-aware representation masks that highlight visible body regions at the semantic level, thereby alleviating the negative impact of occlusions and partial observations in cross-view correspondence. A comprehensive evaluation on CARGO with four matching protocols demonstrates the effectiveness of GSAlign, achieving significant improvements of +18.8% in mAP and +16.8% in Rank-1 accuracy over previous state-of-the-art methods on the aerial-ground setting.