Title: GSAlign: Geometric and Semantic Alignment Network for Aerial-Ground Person Re-Identification
Venue: NeurIPS 2025
Accepted: yes
Keywords: Aerial-Grounded Person Re-identification
Abstract: 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) Transformation 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. Extensive experiments on the challenging CARGO benchmark demonstrate 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.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes GSAlign, a person re-identification (ReID) framework specifically designed for synthesized datasets featuring extreme viewpoint variations and occlusions, such as those from UAV footage or virtual environments. Experiments on the synthetic CARGO dataset show that GSAlign achieves good performance.\n\nSTRENGTHS AND WEAKNESSES:\nStrength:\nThe paper is well-motivated. It tackles a specific and practically important problem: ReID under extreme camera angles and occlusions by leveraging synthetic data. The strong results on CARGO dataset is quite confident.\n\nWeaknesses:\n1. Experiments are conducted only on the synthetic CARGO dataset. Despite its diversity, there is no validation on real-world benchmarks, making it unclear whether the method generalizes beyond virtual domains.\n2. DAM is used solely during training, while inference uses the unmasked prototypes. The authors do not evaluate whether DAM improves inference features or whether it merely functions as a training regularizer.\n3. The paper lacks an analysis of the additional computational cost of applying LTPS at every ViT layer. This may hinder real-time or edge deployment.\n4. The model implicitly depends on accurate keypoint detection, which can be unreliable under occlusions or low resolution scenarios, which are common in real-world UAV data.\nDespite the weaknesses, I am looking forward to the authors’ reply.\n\nQUESTIONS:\n1. Have you tried GSAlign on real-world datasets? If not, what are your expectations regarding generalization beyond synthetic domains?\n2. What is the additional inference cost (such as FLOPs) introduced by LTPS per layer?\n\nLIMITATIONS:\nAs mentioned in the Cons: The framework is validated only on synthetic data, limiting its practical impact unless real-world evaluations are added. This method implicitly depends on accurate keypoint detection, which can be unreliable under occlusions or low resolution.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-23"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes GSAlign, a novel framework for aerial-ground person re-identification (AG-ReID), addressing the challenges of extreme viewpoint variations and occlusions. GSAlign introduces two key components: a learnable thin plate spline (LTPS) module and a dynamic alignment module (DAM). Built on a Vision Transformer backbone, the model adaptively corrects spatial distortions and highlights visible body regions. Experiments on the CARGO dataset show significant improvements over prior methods, particularly in challenging cross-view scenarios, demonstrating robustness and effectiveness of GSAlign in AG-ReID tasks.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. This paper is well-organized from its motivation and method. The problem is specific with reasonable module design and strong performance improvement.\n2. The idea of learnable thin plate spline (LTPS) is innovation on this topic. Visualization also shows interesting results.\n\nWeaknesses:\n1. In this paper, the authors evaluate GSAlign solely on the synthetic CARGO dataset. I suggest the authors include results on real-world datasets such as AG-ReID [1] to better demonstrate the method’s generalizability. \n2. The paper lacks some details, such as the selection of hyperparameters and evidence of the effectiveness of the proposed improvements. While I believe these designs are theoretically sound, supporting experimental results are necessary to validate them. \n\n[1] View-decoupled transformer for person re-identification under aerial-ground camera network. CVPR 2024.\n\nQUESTIONS:\n1. How does the proposed method perform on the AGReID dataset? AGReID is a widely used and real-world benchmark for Aerial-Ground Person Re-Identification, and evaluating on it would better validate the practical effectiveness of the approach.\n2. Eq. (6) introduces a fusion factor. How does it work? Is it a learnable parameter or a handcraft parameter? If it is a handcraft parameter, how you get the value?\n3. Compared to the original TPS, the proposed LTPS makes some rules during the training. So, how does this change influence the performance when compared to the original TPS? \n\n[1] View-decoupled transformer for person re-identification under aerial-ground camera network. CVPR 2024.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 4, "DATE": "2025-06-27"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a novel framework, called GSAlign, for Aerial-Ground person re-identification (AG-ReID). It effectively integrates a Learnable Thin Plate Spline (LTPS) Module for geometric variations and a Dynamic Alignment Module (DAM) for visibility-aware semantic alignment, achieving significantly improvement on the CARGO dataset in aerial-ground settings.\n\nSTRENGTHS AND WEAKNESSES:\nQuality: This paper is technically sound and presents a complete, appropriate work. Some claims are well-supported by experimental results on CARGO dataset. The authors honestly evaluate the limitations in the Supplementary Material (Sec. A.2).\n\nClarity: The organization of this submission is fair, but its clarity needs to be improved.\n\n1. In Line 17, \"Ecomprehensive\" -> \"A comprehensive\".\n2. In Line 86, \"Aerial-Ground ...\" is not a sentence.\n3. In Line 129, \"contro\" -> \"control\".\n4. In Line 154, \"a tunable fusion factors\" -> \"a tunable fusion factor\".\n5. This paper builds on traditional thin plate spline (TPS) technique, but it lacks an introduction to TPS and related references in the 'Related Work' section. For better clarity, the authors are expected to provide more related works.\n6. LTPS is one of your main contributions, but its details are difficult to comprehend. The authors need rephrase Lines 128-156 (e.g., What is \"X\" of \"X - P^{(rot)}_{s,i} \"in Eq. 4? ) and incorporate additional figures to more clearly illustrate how control points are transformed from P_{s} to P_{t}.\n\nSignificance: The results of this paper have some impact on the community, moderately advancing our understanding. Others may build upon this idea. However, the submission needs more experiments to convince me that it is a better approach than previous work. For example, the paper mentions traditional hand-crafted alignment strategies in Line 51, but a comparison to them is lacking.\n\nOriginality: The contributions of this paper differ from previous works. This paper need to provide more related references about TPS. The combination of LTPS and DAM appears to be a reasonable approach, as both modules aim to address the problem of spatial and semantic misalignment.\n\nQUESTIONS:\nMain Concern (If the authors can convince me, I will upgrade the rating):\n\n1. Why does LTPS automatically rotate images correctly? From my perspective, the deformation loss (L_{deform} in Eq. 10) seems to suppress any rotation, and the paper does not seem to use any correctly aligned image as ground truth. Please clarify this. And I also want to know the proportion of correctly rotated images/features.\n\n2. Why is LTPS necessary at every layer? Why does adding it to shallower layers result in poor performance, while adding it to deeper layers yield significant improvements? The authors mention that shallow layers cannot predict rotation well in Line 318. Could this phenomenon be further elucidated through visualizations of the LTPS-processed results at each layer?\n\n3. Could you compare the performance of your method with a wider range of alignment approaches, including implicit methods such as BAU [1], SEAS [2] and PAT [3], as well as explicit alignment techniques such as DAGait [4] and so on? I want to know if LTPS and DAM truly outperform other traditional alignment methods.\n\n4. Could you provide the performance on the AG-ReID [5] dataset? This paper validates its performance on the CARGO dataset, which consists of synthetic data. However, in AG-ReID task, we have an AG-ReID [5] dataset with real data. Can the authors validate the feasibility of their method on real data?\n\n[1] Cho, Y., Kim, J., Kim, W. J., Jung, J., & Yoon, S. E. (2024). Generalizable person re-identification via balancing alignment and uniformity. Advances in Neural Information Processing Systems, 37, 47069-47093.\n\n[2] Zhu, H., Budhwant, P., Zheng, Z., & Nevatia, R. (2024). Seas: Shape-aligned supervision for person re-identification. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 164-174).\n\n[3] Ni, H., Li, Y., Gao, L., Shen, H. T., & Song, J. (2023). Part-aware transformer for generalizable person re-identification. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 11280-11289).\n\n[4] Wu, Z., Zhang, C., Xu, H., Jiao, P., & Wang, H. (2025). DAGait: Generalized Skeleton-Guided Data Alignment for Gait Recognition. arXiv preprint arXiv:2503.18830.\n\n[5] Nguyen, H., Nguyen, K., Sridharan, S., & Fookes, C. (2023, July). Aerial-ground person re-id. In 2023 IEEE International Conference on Multimedia and Expo (ICME) (pp. 2585-2590). IEEE.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper integrates a learnable thin plate spline module (LTPS) and a dynamic alignment module (DAM) into the view-decoupled transformer (VDT) framework [1] for aerial-ground person re-identification. It demonstrates that the proposed method (GSAlign) outperforms existing methods, including VDT, on the CARGO dataset [1] across most setups.\n\n[1] Zhang, Quan, et al. \"View-decoupled transformer for person re-identification under aerial-ground camera network.\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n1. The concept of explicitly modeling geometric alignment (to handle viewpoint variances) and semantic alignment (to address foreground and background issues) within the ReID network presents a promising solution that directly tackles the core challenges of aerial-ground ReID.\n\n2. The proposed GSAlign method outperforms existing approaches, such as VDT, on the CARGO dataset across most setups, particularly in the targeted aerial-ground scenarios.\n\nMajor Weaknesses:\n\n1. Missing results on major real-world benchmarks: The paper demonstrates GSAlign’s superior performance solely on the CARGO dataset [1], which is a synthetic benchmark for aerial-ground person ReID. However, it does not evaluate the method’s effectiveness on real-world datasets such as AG-ReID [2] and its successor, AG-ReID v2 [3]. Notably, the AG-ReID dataset was also used by VDT [1], the method upon which GSAlign is built, for performance evaluation. Assessing GSAlign on these real-world datasets is essential to determine its practical applicability and generalizability beyond synthetic environments.\n\n2. Accuracy drops in aerial-aerial scenarios: Although GSAlign outperforms VDT in most configurations, particularly in the aerial-ground scenarios, it underperforms in the aerial-aerial scenarios. As shown in Tab. 2, this performance drop is attributed to both the LTPS and DAM modules. If, as stated in Ln 266, the aerial-ground setting is indeed “the most challenging scenario”, it raises the question of why the proposed modules negatively impact the supposedly easier aerial-aerial scenarios. This inconsistency is not addressed in the paper.\n\nMinor Weaknesses:\n\n3. The overall quality and clarity of the paper can be improved in several respects:\n\n- While numerous prior works have explored integrating Thin Plate Spline (TPS) into learning frameworks [4]–[6], the paper does not reference any of them. It remains unclear what the major technical contribution of the proposed LTPS module is in comparison to these earlier approaches.\n\n- The citation for VDT in Ln 103 appears to be incorrect and should be verified.\n\n- The clarity of Fig. 2 could be significantly improved. Terms such as “input tokens”, “located points”, and “aligned tokens” are shown in the diagram but are not defined or used elsewhere in the text, making the illustration difficult to interpret.\n\n- The paper does not clearly explain the distinction between the proposed DAM module and the existing DPM method. Clarifying this difference is crucial to justify DAM's novelty.\n\n- The rationale for discussing methods like Memory Bank and Classification Matrix in Tab. 5 is unclear. Are these methods derived from or related to prior work? Their inclusion needs further justification.\n\n[2] Nguyen, Huy, et al. \"Aerial-ground person re-id.\" 2023 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2023.\n\n[3] Nguyen, Huy, et al. \"AG-ReID. v2: Bridging aerial and ground views for person re-identification.\" IEEE Transactions on Information Forensics and Security 19 (2024): 2896-2908.\n\n[4] Jaderberg, Max, Karen Simonyan, and Andrew Zisserman. \"Spatial transformer networks.\" Advances in neural information processing systems 28 (2015).\n\n[5] Zhao, Jian, and Hui Zhang. \"Thin-plate spline motion model for image animation.\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022.\n\n[6] Nie, Lang, et al. \"Semi-supervised coupled thin-plate spline model for rotation correction and beyond.\" IEEE Transactions on Pattern Analysis and Machine Intelligence (2024).\n\n[7] Tan, Lei, et al. \"Dynamic prototype mask for occluded person re-identification.\" Proceedings of the 30th ACM international conference on multimedia. 2022.\n\nQUESTIONS:\n1. Please include experimental results on real-world aerial-ground person re-identification datasets, such as AG-ReID [2] and its successor, AG-ReID v2 [3].\n\n2. Please provide an explanation for why the proposed LTPS and DAM modules lead to decreased performance in the aerial-aerial scenarios.\n\n3. Please improve the overall clarity of the paper according to the issues outlined in Weakness 3.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-02"}