GSAlign: Geometric and Semantic Alignment Network for Aerial-Ground Person Re-Identification
Authors: Qiao Li
Jie Li
Yukang Zhang
Lei Tan
Jing Chen
Jiayi Ji
Venue: NeurIPS 2025 poster ()
Keywords: Aerial-Grounded Person Re-identification
Primary area: applications

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.

GSAlign: Geometric and Semantic Alignment Network for Aerial-Ground Person Re-Identification
Qiao Li', Jie Li?; Yukang Zhang', Lei Tan' Jing Chen', Jiayi Ji
Key Laboratory of Acrospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University
Xiamen University
National University of Singapore
liqiaoqiao2330vhu edu. en, lei. tanCnus edu. sg
Abstract
person re-identification (AG-ReID) is an emerging yet challenging task that aims to match pedestrian images captured from drastically different view- points, typically from unmanned aerial vehicles (UAVs) and moound-based surveil- lance 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 jointly tackle geometric distortion and semantic misalignment in aerial-grov ground matching: Learnable Thin Plate Spline (LTPS) Module and Dynamic Alignment Module (DAM). The LTPS module adaptively warps pedestrian features based on a 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 observa- tions in cross-view correspondence. comprehensive evaluation on CARGO with four matching protocols demonstrates the effectiveness of Alglig,, achieving significant improvements of +18.8% mAP and + +1..8% in Rank-1 accuracy over previous state-of-the-art methods on the aerial-ground setting.
Introduction
Person re-identification (ReID), aiming to address the problem of matching people over distributed set of nonoverlapping cameras, has attracted intensive attention in the last few years due to its wide applications in surveillance systems. Although traditional person re-identification (ReID) has achieved remarkable progress [1, 2, 3, 4, 5], there growing interest in aerial-ground RelD (AG-ReID) settings, driven by the increasing deployment of unmanned aerial vehicles (UAVs) and low-altitude platforms surveillance applications [6, 7, 8, 9, 10]. This, in practice, puts Re-ID problem in an aerial-ground setting and requires the approaches to properly handle both the significant geometric variation and semantic misalignment.
Despite its potential, AG-ReID remains highly underexplored and technically challenging problem. Unlike conventional ReID tasks [11, 12. 13, 14, 15]. AG-ReID suffers from extreme viewpoint
Project Leader Corresponding Author: Tan
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Netwark
Network
Mask
R&D
Latign
Learnable TPS
ReID
Semantic Mask
Representation
Input Image
Input Image
ligned Image
Latign
Match
Implicit Alignment-
*
Geometric Alignment
*
Semantic Alignment
(a) Current aligument strategy.
(b) Our proposed Geometric and Semantic A lignment Network (GSA ligm).
Figure Illustration of the motivation of GSAlign. (a) Prior methods rely solely on implicit alignment, which is insufficient to fully address spatial and semantic distortions. (b) In contrast,
our GS Align performs explicit alignment at both the geometric and semantic levels via LTPS and visibility-awaa semantic masks, respectively. This design equips GSAlign with stronger capability for robust aerial-ground matching.
disparities, where aerial images exhibit severe top-down perspectives while ground views contain mostly frontal or profile views. These differences cause significant geometric distortions and drastic appearance changes, which conventional Rell) models that are primarily trained on ground-view data are ill-equipped to handle. In addition, AG-ReID often involves frequent occlusions and unbalanced visibility across views, further complicating cross-view matching.
To mitigate these challenges, several recent studies have attempted to adapt existing ReID techniques to the serial-groune scenario [16, 8]. These methods seek to learn modality-specinn representations or map features into a shared embedding space using metric learning or adversarial objectives. Others employ pross-view decomposition strategies or design view-invarian constraints to bridge the modality gap. However, these methods tend to focus on global alignment and overlook two critical issues: (1) the severe geometric distortion induced by cross-view perspectives, and (2) semantic misalignment caused by partial occlusions and varying visible body regions. These limitations lead to suboptimal performance, especially under large viewpoint gaps.
In this paper, as shown in Figure 1 (b), we propose a Geometric and Semantic Alignment Net- work (GSAlign), which is specifically designed to tackle the core challenges of Aerial-Ground Re-Identification
The key insight of our approach is to explicitly model both geometric deformation and visibility- aware semantics within unified ViT framework. To this end, GSAlign introduces two complementary modules: First, the Learnable Thin Plate Spline (LTPS) Transforma- tion Module. ITPS adaptively warps pedestrian representations based on a set of learned keypoints. Unlike hand-crafted alignment strategies, our L.TPS module is fully differentiable and end-to-end trainable, enabling the network to learn viewpoint-conditione transformations that effectively and dynamically compensate for severe spatial distortions caused by extreme cross-view differences. To avoid potential errors caused by one-shot correction, LTPS is progressively integrated into the hierarchical layers of a Vision Transformer (ViT). This design enables the network to iteratively refine geometric alignment across layers, allowing fine-grained, stage-wise correction throughout the feature propagation process. Second, the Dynamic Alignment Module (DAM). DAM enables each imput image to predict its
visibility representation masks the semantic level, highlighting visible body regions while suppressing noisy or occluded areas. The predicted masks then applied the corresponding gallery features filter out noisy signals from invisible regions, thereby enhancing cross-view feature alignment. By dynamically adapting to the visibility of different body parts, DAM guides the network to focus on semantically consistent and disrriiiaiisr cues across aerial and ground views. This allows the model to focus on semantically consistent cues across views, thereby improving robustness under occlusions and pose variations.
To sum up, the main contributions of this paper are as follows:
We propose GSAlign, a novel framework for aerial-ground person re-identification that jointly addresses geometric deformation and semantic misalignment within a unified archi- tecture. GSAlign is specifically designed handle the extreme cross- view variations and visibility inconsistencies inherent in UAV-to-ground matching scenarios.
We introduce a Learnable Thin Plate Spline (LTPPS) Module and a Dynamic Alignment Mod- ule (DAM). LTPS performs keypoint-guided feature warping to compensate for severe spatial

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distortions. while DAM enhances semantic alignment by estimating usibility-semmaaee rep- resentation masks to highlight visible body regions and suppress noisy or occluded areas.
Extensive experiments on the challenging CARGO dataset validate the effectiveness of GSAlign, which achieves state-of-the-an performance with absolute gains of +18.8% in mAP and +16.8% in Rank-1 I accuracy on the acrial grriud setting.
2 Related Work
Building upon well-established ground surveillance infrastructure, advanced person re-identification algorithms have made significant progress across a range of scenarios, including general scenarios [17, 18, 1, 4], occluded scenarios [19, 12, 20, 21J. cross-modal scenarios [22, 23, 24, 25), multi-spectral scenarios [26, 27, 28], and cross-spectral scenarios [29, 30, 31, 32, 33].
However, with the proliferation of unmanned aerial vehicles (UAVs) and low-altitude platforms augmenting traditional surveillance systems, person ReID research is increasingly extending to aerial-view scenarios [34]. Initial efforts this direction focused on acrial-acrial retrieval tasks using datasets such UAV-Human [35] and PRAI-1581 [7]. More recently, attention has shifted towards connecting the aerial views with traditional ground systems. Aerial-Ground person ReID (AG-ReID), where the query and gallery images originate from fundamentally different viewpoints, typically aerial and ground-level perspectíves. This setup introduces severe spatial and semantic discrepancies, often leading to substantial degradation in identity-preet prreseriin cues such as pose, silhouette, and clothing texture [36]. Several approaches have been proposed to mitigate these challenges. Nguyen el al. [6] proposed dual-stream structure guided by attributes to enhance semantic disentanglement, later extended to a three-stream model with modality- supervision [16]. Zhang et al. [8] developed the View-Decoupled Transformer (VDT), which explicitly models and separates niewpoint-specife and viewpoint-invarian features. SD-ReID [37] proposes a generative framework that leverages diffusion models to synthesize view-specific features and enhance view-invariant person representations. In addition, LATex [38] incorporates attribute- based text knowledge via prompt-tuning strategies on vision-language models, explicitly exploiting the viewpoint-invan invariant nature of person attributes to improve cross-view retrieval. Despite these advances, existing AG-ReID methods still face difficulties in handling extreme spatial distortions and in leveraging visible semantic regions effectively. To overcome these limitations, we propose GSAlign, a novel framework that performs gestance-adaptive geometric transformation and visibility-awarr semantic alignment, promoting robust and spatially consistent feature learning across acrial and ground domains.
3 Methodology
3.1
Overview
The overall framework of the Geometric and Semantic Alignment Network (GSAlign), illustrated in Fig. built upon ViT-Base backbone and designed to address feature misalignment under com- plex multi-view conditions. Inspired by the success of the Decoupedd Transformer (VDT) [39], we adopt the VDT architecture as the foundation of GSAlign, which effectively captures both global semantics and fine-gr mne-grained structural cues. Following its design, we introduce an additional view token model siewpoint-spechie information. The token sequence during training defined as:
X Xview
X imge VDT (( Xview, (okenize(Ximn)))
(1)
where X Xview, and Xing represent the class token, view token, and image tokens, respectively.
To improve geometric robustness, we integrate Learnable Thin Plate Spline module (LTPS) into transformer layers. This module predicts rotation-aware control point offsets and applies a non-rigid spatial transformation to warp local patch features. By doing so, LTPS mitigates pose- induced geometric distortions and enhances structural alignment across views. addition, we introduce a Dynamic Alignment Module (DAM) during training, which generates channel-wiss semantic masks guided by class-specific prototype features. These masks dynamically highlight identity subspaces, allowing the model to focus on discriminative patterns while suppressing background noise and occlusion artifacts. The generated masks serve a specific purpose: aligning the features toward consistent subspaces. Overall, the Aligg integrates the LTPS and DAM, jointly optimizing
3

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Sumantic Mask
Dynamkc Aligument Module (DAM)
SAL
NT1
Represitation
Semantic Mask
Lieer Ardectiin
Plate Spline (LTPPS)
Semanttc Allgnment
Balthh Features
Angle Prodictor
Rotation Point
Sinilastty Matching
Convolutional Layurs
Initial Point
Batched Images
Representation
Masked Feature
Figure 2: Overview of the GSAlign architecture. Given aerial or ground-view inputs, GSAlign first applies an initial geometric transformation via Learnable Thin Plate Spline (LTPS) module, followed by progressive alignment through LTPS blocks inserted before each ViT layer. In parallel, a Dynamic Alignment Module (DAM) generates visibility semantic mask according to the input image, which is then applied to the representations of other images in the batch to suppress irrelevant occluded features.
for classification accuracy, feature consistency, and robustness to view and shape variations. This synergy enables GSAlign show strong re-identification ability severe view variations.
Learnable Thin Plate Spline
To address the spatial misalignment problem in person re-identification, we design Learnable Thin Plate Spline (LTPS) deformation module that explicitly models geometric variations arising from changes in human pose and viewpoint. By dynamically predicting control point displacements and rotation angles, the module performs nonlinear transformations on feature maps, thereby enhancing their ability to capture non-rigid spatial deformations. As illustrated in Fig. 2, the LTPS modules integrated into each transformer block to build a hierarchical deformation-awee representation. The LTPS modules at shallower layers focus capturing local deformation details, while those at deeper layers are responsible for modeling global pose variations.
The core idea of the LTPS module is to perform non-rigid deformation based on control points interpolation. We initialize set of 2D source contro points P. E R& X2 as regular grid uniformly distributed over the normalized coordinate space [-1, 1] x [-1, 1] and treat them as learnable parameters optimized during training. parallel, we define set target control points P, R which are fixed and share the same regular grid positions the initial P.. representing the canonical target shape. During training, a rotation angle is predicted from the input features and applied to P.. and smooth deformation mapping is constructed by enforcing interpolation constraint from the rotated P, to the fixed P., guiding the feature map to undergo spatial transformation.
In traditional thin plate spline (TPS) transformations, the source control points P, are fixed, and spatial rearrangement is achieved only by learning the target control points Pt, which limits the ability to handle global rotations and complex deformations. To address this, we design rotation prediction module that predicts a rotation angle from the global orientation the input feature map, and applies this rotation to the source control points for improved modeling of spatial deformations. The process defined as:
P (rot)
cos 0 sin 0
sin cos 0
0 fo(
Here, F represents the patch-level feature map of each layer, and fe(-) is the rotation prediction network that outputs the angle 0 E [- =]-
To obtain the transformation function T(-), the TPS requires the source control points P, and the target control points P, to form the following interpolation constraint:
T(P..) = Pr.i. =i 1,,,,,,, K.
This constraint requires that the transformation function precisely maps the source points P, to the target points P, at each control point. Based on this, for the rotated source points prot and the target

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control points Pt, the TPS transformation function T(-) is defined as follows:
T(p=) A P Wi U[| P
(mx) DD,
where Ps can be any point from the feature map. A E TR is an affine matrix. w, is the deformation weight for the i-th TPS kernel function, U,(:), which is defined as:
(rr) = r² log r², r= P - P..el2.
(5)
Here, r is the Euclidean distance between the feature point PN and the i-th source control point.
The patch feature after deformation by (-) is denoted as Fips. We apply residual fusion with the original patch feature F:
F final F + 17 F Fips.
Here, 1/ is tunable fusion factors. This fusion strategy allows the model to retain original semantic features while explicitly incorporating dynamic spatial structure awareness. result, it improves the model's ability to recognize people under occlusion, rotation, and deformation in complex scenes.
3.3 Dynamic Alignment Module
In person re-identification, training images often contain occlusions, background noise, or identity- irrelevant regions. Directly encoding features from the entire image may lead the model to learn redundant or non-discriminative representations. To address this issue, we explore whether the visibil- ity patterns of given input image be used to dynamically modulate the feature representations of other samples. Inspired by DPM [40], we propose the Dynamic Alignment Module (DAM), which leverages input-guided masks improve semantic alignment. Specifically, DAM treats the mean feature representation of images with the same identity as its ideal prototype, assuming that noise or occlusion in different samples manifests as partial loss of this prototype's semantic structure. Based on this formulation, DAM introduces a dynamic channel-wise mask generator based on the input and modulates prototypes by referencing the input-aware mask. This allows the model to suppress irrelevant or noisy dimensions and emphasize input-relevant subspaces in content-aware manner.
Specifically, during training, for each batch, firstly construct prototype features for all classes as:
N
1
G.
Here, No is the number samples belonging to class e within the current mini-batch, and f, represents the feature embedding of the i-th sample. All prototype features are fz-normalized both before and after the update to ensure consistent alignment with the sample features.
To enable dynamic selection of relevant prototype subspace, we design channel-wist mask generator. This generator guides the model to identify and emphasize the prototype subspace most relevant for discriminating the current input image. Unlike spatial domain masks operating on pixel regions, we focus on channel-wise selection. The goal to generate a sparse, discriminative channel-wis wise mask for each image, highlighting the discriminative dimensions during prototype matching.
The mask generator creates channel-wise mask based the current sample feature with two-layer multilayer perceptron (MLP), followed by Sigmoid activation function to ensure mask values are between and I:
m; Sigmoid (W2- ReLU (W, f, + b,) + b2)
where W1 E IR Dx and W₂ E IR 2 xD are learnable weight matrices, b1 E R and b2 E R denote the bias terms, and f, is the input feature vector.
The generated sample-specific mask is then used to weight the prototype features, focusing on the parts of the features that relevant to the category. The mask is applied by performing element-wise multiplication with the prototype features as follow:
P masked
Pe ② Mcs
(9)
5

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where Pe denotes the prototype feature of class and m. is a channel-wise mask generated by the Channel-wise Mask Generator based on the feature representation of each sample and its correspond- ing label.
This mask is employed exclusively during the training phase reweight the prototype Pa along the channel dimension. By emphasizing informative feature channels and suppressing less relevant ones, this mechanism encourages the model focus on more discriminative aspects the prototype representation. It is important to emphasize that this masked prototype weighting strategy is only applied during training facilitate effective prototype learning. During inference, the model directly compares the extracted features with the unmasked prototypes, without relying on any class labels or mask generation.
Overall, the dynamic weighting mechanism enables the model to adaptively adjust prototype rep- resentations according the feature distribution of each class. This enhances the discriminative capacity of the learned representations by selectively highlighting important channels and filtering out irrelevant dimensions. In addition, this module is utilized only during the training phase, which will not add the inference computation cost.
3.4 Loss Function and Optimization
Our model is trained using a composite loss function designed to optimize identity discrimination, geometric stability, and feature-level alignment. In addition to the standard ID) classification loss Lii and the Triplet loss Lui commonly employed in RelD tasks, we introduce a deformation loss Luetfrm and the mask loss Lmask.
Deformation Loss. To enhance the stability of the LTPS module's deformation modeling, introduce regularization term as the deformation loss Letefom. This loss discourages the rotation prediction submodule from outputting excessively large rotation angles, thereby promoting geometri- cally plausible transformations. While the angle prediction mechanism intrinsically limits angles via tanh activation and scaling, this loss provides an additional constraint. It is defined the average absolute predicted rotation angle across all L encoder layers where an LTPS module is integrated:
Ldeform +
Σ 10
(10)
where 0: represents the predicted rotation angle the (-th layer. By penalizing large rotation magnitudes, this loss helps maintain geometric consistency during the feature transformation process.
Mask Loss. To enhance the effectiveness of the Dynamic Alignment Module, introduce mask alignment loss to ensure consistency between the dynamically masked prototype and the input sample feature. formulate a composite mask loss Lmask- This loss comprises two components: an alignment term Latign and an entropy-based regularization term Lentroy:
F
Lalign + An
(11)
Calign aims to ensure that an input sample's feature representation, when masked, aligns closely with its corresponding class prototype, also masked by the same sample-specific mask. This encourages consistency in the feature subspace highlighted by the dynamic mask, even in the presence of occlusions or pose variations which might otherwise lead disparate semantic activations. Let be the feature of i-th sample with class of c, m; its generated channel-wise mask, and P. the prototype for the class c. The alignment loss is computed
N
Calign
Norm m θ P - (mr + f.)l..
(12)
N
where is the batch size, and Norm means the lg normalization. This loss encourages intra-class compactness within the dynamically selected channel subspace. By applying the mask m, to both the sample feature and its prototype, the model learns to emphasize discriminative channels corresponding to visible regions while down-weighting channels associated with occlusions background clutter.
Lentropy designed to promote channel selectivity in the generated masks. During training, the mask tends to degenerate into fully activated state, i.e., m, 1. This loss drives the mask vector towards

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a binary distribution by maximizing the information entropy, thus retaining the most relevant channels for each sample. is defined as the negative sum of binary entropies for each mask component:
Centupy
A
m (d) log m (1)
+(1 m! (1)) log(1 (1 -m! mn{)]]
(13)
ND
Lalign and Lentrpy work as complementary constraints. Lalign guides the mask to select channels that maximize prototype similarity, while Lemtropy encourages the mask to make more definitive selections. balancing hyperparameter
introduced to regulate the trade-off between the two objectives.
Overall Loss. The model is trained end-to-end by minimizing a comprehensive loss function that aggregates the aforementioned components:
Loal = (Lia + Lui + a + mmask
(14)
where the and B is the hyper-parameters to adjust the weight of Ldetomm and Lmusk respectively. This joint optimization framework encourages the learning of discriminative and robust representations that are resilient to spatial misalignments and feature irrelevant variations.
4 Experiments
4.1
Implementation
Datasets and Evaluation Protocols. We conduct experiments on the CARGO dataset [8], large- scale benchmark specifically designed for aerial-ground person re-identification (AG-RelD). CARGO contains 108,563 images of 5,000 synthetic identities captured by 13 cameras (5 aerial and 8 ground), with diverse conditions including extreme viewpoint changes, resolution variations, illumination shifts, and occlusions. The dataset is constructed in simulated urban environment using MakeHuman for identity modeling and Unity3D for camera deployment.
Following the standard protocol, we use 51,451 images with 2500 identities for training and 51,024 images with the remaining 2.500 identities for testing. The testing phase contains four evaluation protocols: ALL., ground to ground( G++ aerial to aerial ( (A<>A), and aerial to ground (A++G) G), where each protocol focuses on specific matching scenario. To further verify the generalization ability of GSAlign in real-world scenarios, we also conduct comparative experiments on the AG- RelD [6] and AG-ReID v2 [16] datasets, which contain real aerial and ground imagery captured in outdoor environments. We use Cumulative Matching Characteristies (CMC) at Rank-1 accuracy, mean Average Precision (mAP), and mean Inverse Negative Penalty (mINP) [41] as evaluation metrics.
Implementation Details. All implementation settings strictly follow the protocol of VDT [8] for fair comparison. We adopt ViT-Base as the backbone, initialized with ImageNet-2IK pretraining. Input images are resized to 256 x 128, and horizontal flipping is applied during training for data augmentation. The model is optimized using AdamW with weight decay of 0.05 and a cosine learning rate schedule. The initial learning rate is set 3.5 10-4 with linear warm-up for the first 20 epochs. X is set to 0.1 during the training. We train for 120 epochs in total, using a batch size of 64.
4.2 Comparison with State-of-the-Arts
In this section, we compare our proposed GSAlign with range of state-of-the-ae person RelD methods, including both conventional CNN-based models (e.g., PCB [17], BoT [43], MGN [44]) and Transformer-base frameworks (c.g.. ViT [39], VDT [8]). As shown Table 1, GSAlign achieves the best overall performance across all four protocols. Specifically, under the most comprehensive ALL protocol, GSAlign achieves 65. 5.06% Rank- 57.55% mAP, and 44.97% mINP, outperforming the strong baseline VDT [8] by +0.96% +2.75% and +3.84% respectively. On the most challenging A++G cross-view setting, our method surpasses all previous methods by a large margin, reaching 64.89% Rank-1, 61.55% mAP, and 52.81% % mINP, significantly ahead of the second- best VDT by +16.77%, +18.79% and +22.86%, respectively. Although GSAlign is not specifically designed for single-view matching protocols, it does not introduce any negative effects in such settings. On both A and G++G, GSAlign still achieves competitive performance. We attribute this to the fact that, in single-vien view scenarios, the features are already well aligned due to the limited viewpoint variation,
7

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Table I: Performance comparison of the mainstream methods under four settings of the CARGO dataset. "ALL" denotes the overall retrieval performance of each method. "G+*G", "A++A", and "A++G" represent the performance of each model in several specific retrieval patterns. Rank1, mAP, and mINP are reported (%). The best performance is shown in bold.
Protocal I: ALI
Protocol 2: G-+G
Protocal 3: At+A
Protocal 4: A
Method
Rankl
mAP
mINP
Rankl
mAP
mINP
Rank1
mAP
mINP
Rankl
mAP
mLNP
SBS [42]
50.32
43.09
29.76
72.31
62.99
48.24
67.50
49.73
29.32
31.25
29.00
18.71
PCB [17]
51.00
44.50
32,20
74.10
67.60
55.10
55.00
44.60
27.00
34.40
30.40
20.10
BoT [43]
54.81
46,49
32,40
77.68
66.47
51.34
65.00
49.79
29.82
36.25
32.56
21.46
MGN [44]
54.81
49.08
36.52
83.93
71.05
55.20
65.00
52.96
36.78
31.87
33.47
24.64
VV [[45.40 46]
45.83
38.84
39.57
72.31
62.99
48.24
67.50
49.73
29.32
31.25
29.00
18.71
AGW [41]
60.26
53.44
40.22
81,25
71.66
58.09
67.50
56.48
40.40
43.57
40.90
29.39
BAU [47]
45.20
38.40
61.60
51.20
50.00
42.60
40.40
36.70
PAT [48]
37.90
15.30
52.70
24.20
50.00
23,10
35.10
15.50
DTST [49]
64.42
55.73
41.92
78.57
72.40
62.10
80.00
63.31
44.67
50.53
43,49
29,46
VIT [39]
61.54
53.54
39.62
82.14
71.34
57.55
80.00
64.47
47.07
43.13
40.11
28.20
VDT [8]
64.10
55.20
41.13
82.14
71.59
58.39
82.50
66.83
50.22
48.12
42.76
29.95
GSAlign
65.06
57.95
44.97
83.04
73.86
62.73
80.00
65.55
49.81
64.89
61.55
52.81
thus requiring minimal additional geometric or semantic correction. In conclusion, these results clearly demonstrate that our proposed GSAlign not only achieves strong global retrieval accuracy but also exhibits exceptional robustness in the presence of extreme viewpoint changes, validating the effectiveness of our geometric and semantic alignment design.
Furthermore, we evaluate the proposed GSAlign on the real-world AG-ReID [6] and AG-ReID v2 [16] datasets, and com- pare it with wide range of state-of-the-art Rell methods, as summarized in Table 2 and Table 3. On the AG-ReID dataset, GSAlign achieves strong and consistent performance under both cross-view proto- cols. Under the A+G setting, GSAlign obtains the best results across all metrics, reaching 83.75% Rank-1, 1, 75.01% % mAP, and 52.1 % mINP, outperforming the view- aware baseline VDT by +0.84% +0.57% and +1.05% respectively. Under the in- verse G+A protocol, GSAlign achieves competitive Rank-1 (84.10%) and mAP (77.73%) scores, while yielding the highest mINP of 53.63%, demonstrating its effec- tiveness in sard-sample and long-tail tail retrieval under severe viewpoint changes. On the more chal- lenging AG-ReID v2 dataset, which features more realistic and diverse scenes, GSAlign continues to exhibit strong generalization ability. As shown in Table 3, GSAlign achieves the highest mAP across all four protocols, including A++G, G++A, A+ W, and W with mAP values of 81,38% 81.05%, 83.98% %. and 80.90%, respectively. Although some methods attain slightly higher Rank-1 accuracy under specific settings, GSAlign remains highly competitive while delivering more stable overall retrieval performance.
Table 2: Performance comparison of the mainstream methods under two settings of the AG-RelD dataset. Results are reported under four matching protocols. The best values per column are shown in bold.
Protocol A+G
Protocal 2: G++A
Setting
Rankl
mAP
mINP
Rankl
mAP mINP
SBS [42]
73.54
59.77
73.70
62.27
BoT [43]
70.01
55.47
71.20
58.83
OsNet [50]
72.59
58.32
74.22
60.99
VV [45, 46]
77.22
67.23
41.43
79.73
69.83 42.37
Explain
81.28
72.38
82.64
73.35
VIT [39]
81.47
72.61
82.85
73.39
VDT [8]
82.91
74.44
51.06
86.59
78.57
52.87
GSAlign
83.75
75.01
52.11
84.10
77.73
53.63
In summary, the experimental results on both AG-ReID and AG-ReID v2 datasets demonstrate that GSAlign effectively enhances cross-view feature alignment and retrieval robustness. The consistent gains mAP and mINP across diverse protocols validate the effectiveness and robustness of our geometric and semantic alignment design in real-world perial-ground RelD scenarios.
4.3 Ablation Study
To assess the individual contributions of each component in the proposed GSAlign framework, we conduct comprehensive ablation study on the four testing protocols of the CARGO dataset. As shown in Table 4, we start with strong transformer-based bascline [8] and progressively integrate

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Table 3: Performance comparison of the mainstream methods under four settings of the AG-
ReID v2 dataset. Results are reported under four matching protocols. The best values per column shown in bold.
Protocol 1: A++G
Protocol
G++A
Protocol
A++
Protocol 4: W++A
Setting
Rank1
mAP
Rank1
mAP
Rankl
mAP
Rankl
mAP
BoT [43]
85,40
77.03
84.65
75.90
89.77
80.48
84.65
76.90
Explain [6]
87.70
79.00
87.35
78.24
93.67
83.14
87.73
79.08
AG-ReIDv2 [16]
88,77
80.72
87.86
78.51
93.62
84.85
88.61
80.11
SeCap [51]
88.12
80.84
88.24
79.99
91.44
84.01
87.56
80.15
VDT [8]
86,46
79.13
86, 14
78.12
90.00
82.21
85.26
78.52
GSAlign
87,86
81.38
88.02
81.05
90.63
83.98
87.31
80,90
Table 4: Ablation study of the different components in GSAlign on the CARGO dataset. "L.TPS" denotes the Learnable Thin Plate Spline transformation module. "DAM" refers to the Dynamic Alignment Module. The best performance per column is shown in bold.
Protocol I: ALL
Protocol 2: Ge+G
Protocol 3: AssA
Protocol 4: A--G
Setting
Rankl
mAP
mlNP
Rank1
mAP
mLNP
Rankl
mAP
mINP
Rank1
mINP
Baseline
64.10
55,20
41.13
82.14
71.59
58.39
82.50
66.83
50.22
48.12
42.76
29.95
Baseline + LIPS
64.42
55.95
41.92
80.36
71.87
59.55
82.50
65.26
47.15
64.89
61.08
50.54
Baseline + LIPS DAM
65.06
57.95
44,97
83.04
73.86
62.73
80.00
65.55
49.81
64.89
61.55
52.81
the Learnalle Thin Plate Spline (LTPS) transformation module and the Dynamic Alignment Module (DAM). Compared the baseline, integrating LTPS brings consistent performance improvements across most settings. Specifically, on the most challenging A++ protocol, LTPS improves Rank-1 from 48. 42% to 64.89%, mAP from 42%76% to 61.08% and mINP from 29.95% to 50.54%. These results confirm the effectiveness of progressive geometric alignment in compensating for severe cross-view deformations. Interestingly, the performance gain on A++A and Ge+G is more modest, indicating that LTPS is particularly beneficial when strong viewpoint discrepancies exist. Adding the DAM module on top of LTPS yields further improvements, especially in terms of mAP and mINP. Under the full matching scenario (Protocol 1: ALL), the full model achieves the best performance with 65.06% Rank-1, 57.95% mAP, and .4% mINP. Notably, the largest relative gain comes from the A++G setting, where DAM increases mINP from 50.54% to 22. This demonstrates that DAM effectively suppresses noisy and occluded regions by leveraging visibility-aware semantic masking, thus enhancing cross-view alignment. Overall, the ablation study verifies the complementary effects of L.PS and DAM. While LTPS resolves geometric distortions in a layer-wise manner, DAM provides semantic-level refinement by selectively filtering unreliable features. Their combination allows GSAlign to maintain robust performance across diverse matching protocols, especially under severe viewpoint and occlusion conditions.
4.4 Discussions
Number of control points in LTPS. To examine the sensitivity of GSAlig to the number of control points in the Learnable Thin Plate Spline (LTPS) module, we test five settings with 4, 9, 16, 25, 36 keypoints on the CARGO dataset under four protocols. As shown in Table 5, all variants benefit from LTPS- basee geometric alignment, though performance varies with point density. The 4-point LTPS achieves the best overall results as Rank-1 (64.89%), mAP (61.55%), and mINP (52.81%) %% under the A++G protocol and remains strong across all scenarios. Increasing points (e.g., 25 or 36) slightly improves R but reduces mAP and mINP, likely due to over-flexibility and local distortion. These findings indicate that lightweight 4-point LTPS provides optimal trade-off between alignment accuracy and stability.
Different locations for LTPS. We further study the effect of inserting LTPS at different depths of the ViT backbone. As shown in Table 6, applying LTPS to all transformer layers yields the best results (64.89% 1, 61.55% mAP, 52.81 %% mINP under Protocol 4: A++G) and consistent gains across scenarios. Among partial insertions, placing LTPS in the last 4 layers performs comparably to the full setting, implying that geometric distortions persist even in higher-level features and benefit
A visualization results of LTPS (Sec. A.1) is provided in the supplementary material.

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Table 5: Performance of GSAlign under different numbers of control points in LTPS. Results are reported under four matching protocols. The best values per column are shown in bold.
Protocol I: ALL
Protocol 2: G++G
Protocol A+tA
Protocol 4; A+tG
Setting
Rankl
mAP
mLNP
Rankl
mAP
mlNP
Rankl
mAP
mINP
Rank1
mAP
mlNP
Number of control points in LTPS
4
65.06
57.95
44.97
83.04
73.86
62.73
80.00
65.55
49.81
64.89
61.55
52.81
9
64,10
57.35
44.76
82.14
75.16
64.91
80.00
64.51
47.23
62.77
59.25
50.19
16
63.78
56.48
42.97
80.36
73.78
63.49
77.50
63.48
47.28
61.70
58.06
48.19
25
65.71
$7.62
44.15
82.14
74.06
62.93
82.50
65.95
48.20
63.83
60.23
50.53
36
65.71
57.55
44.34
81.25
72.71
61.11
82.50
65.88
48.47
63.83
60.00
51.12
Table 6: Comparison between different locations for LTPS. Results are reported under four matching protocols. The best values per column are shown in bold.
Protocol I: ALL
Protucol 2: G
Protocol A0AA
Protocul 4: A
Setting
Rankl
mAP
mINP
Rank1
mAP
mINP
Rank1
mAP
mlNP
Rank1
mAP
mINP
Different locations for LTPS
First layer
64.10
55.92
42.44
83.04
72.86
60.58
80.00
65.98
50.45
58.51
56.92
47.62
Firtt 4 layers
64.10
56.46
43.50
81.25
74.49
64.70
80.00
64.45
47.11
58.51
56.21
46.66
Middle layers
64.74
57.09
44.08
82.14
74.93
64.77
77.50
64.28
47.30
58.51
58.30
50.18
Last 4 layers
65.06
57.39
44,05
83.04
74.42
62.86
77.50
65.21
49.80
64,89
59.87
50.95
layens
65.06
$7.95
44.97
83.04
73.86
62.73
80.00
65.55
49.81
64.89
61.55
$2.81
Table 7: Comparison between different variants of DAM. Results are reported under four matching protocols. The best values per column are shown in bold.
Protocol ALL.
Protecol 2: Ge+G
Protocol 3: A+tA
Protocol A
Setting
Rankl
mAP
mINP
Rankl
mAP
mINP
Rankl
mAP
mINP
Rankl
mAP
mINP
Different variants of DAM
Inner-Batch
65.06
57.95
44.97
83.04
73.86
62.73
80.00
65.55
49.81
64,89
61.55
52.81
Memory Bank
65.38
57.34
44.09
83.04
73.72
62.05
80.00
62.70
43.88
63.83
61.06
52.52
Classification Matrix
63.14
55.64
42.07
81.25
72.04
59.53
75.00
63.79
48.06
57.45
56.55
47,33
from late-stage correction. In contrast, early-layer insertion offers limited improvement, especially in fine-grained metrics like mINP, likely because shallow features are less discriminative and harder to align precisely. These results highlight the importance of progressive alignment from shallow deep layers for handling complex viewpoint distortions in AG-ReID.
Different variants of DAM. We compare three variants of the Dynamic Alignment Module (DAM); Inner-Batch, Memory Bank, and Classification Matrix. As shown in Table 7, the Inner-Batch variant achieves the best overall performance. It dynamically generates visibility awarre masks within each banih enabling query-driven refinement of peer features. The Memory Bank variant stores identity prototypes updated by momentum, providing semantic context but suffering from stale or mismatched memory. The Classification Matrix variant reuses classifier weights as class centers [40], but these prototypes biased toward classification objectives. Overall, Inner-Batch masking proves most effective for dynamic alignment in aerial-ground person RelD.
5 Conclusion
In this paper, we propose GSAlign, a novel framework for aerial-ground person re-identification that jointly addresses geometric distortion and semantic misalignment. By introducing a Learnable Thin Plate Spline (LTPS) module and a Dynamic Alignment Module (DAM), GSAlign performs progressive geometric correction and visibility-aware semantic filtering in unified manner. Extensive experiments on the challenging CARGO and AG-RelID datasets demonstrate that GSAlign achieves state-of-the-art performance across multiple protocols, highlighting its effectiveness in handling severe viewpoint changes and partial occlusions.
Acknowledgements. This research was supported in part by the National Key R&D Program of China under grant No. 2022YFB3103300.
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References
[1] Shuting He, Hao Luo, Pichao Wang, Fan Wang, Hao Li, and Wei Jiang. Transreid: Transformer-basee object re-identification. In Proceedings of the IEEE/CVF international conference on computer vision, pages 15013-15022, 2021.
[2] Wen Li, Cheng Zou, Meng Wang, Furong Xu, Jianan Zhao, Ruobing Zheng, Yuan Cheng, and Wei Chu. De-former: Diverse and compact transformer for person re-identification. In Proceedings of the AAAI Conference on Artificial Intelligence. volume 37, pages 1415 1423,1423, 2023.
[3] Lei Tan, Pingyang Dai, Jie Chen, Liujuan Cao, Yongjann Wu, and Rongrong Ji. Partformer: Awak- ening latent diverse representation from vision transformer for object re-identification arXiv preprint arXiv:2408. 16684, 2024.
[4] Guiwei Zhang, Yongfei Zhang, Tianyu Zhang, Bo Li, and Shiliang Pu. Pha: Patch-wise high-frequency augmentation for transformer-based person re-identification. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 14133-14142, 2023.
[5] Siyuan Li, Li Sun, and Qingli Li. Clip-reid: exploiting vision- language model for image re-identificationn without concrete text labels. In Proceedings of the AAAI conference on artificial intelligence, volume 37, pages 1405-1413, 2023.
[6] Huy Nguyen, Kien Nguyen, Sridha Sridharan, and Clinton Fookes. Aerial grouud person re-id. In 2023 IEEE International Conference on Multimedia and Expo (ICME), pages 2585-2590. IEFE, 2023.
[7] Shizhou Zhang. Qi Zhang, Yifei Yang. Xing Wei, Peng Wang. Bingliang Jiao, and Yanning Zhang. Person re- re-identification in aerial imagery. IEEE Transactions on Multimedia, 23:281-299 2020.
[8] Quan Zhang, Lei Wang. Vishal M Patel, Xiaohua Xie, and Jianhaung Lai. View-decoupled transformer for person re-identification under aerial- camera network. In Proceedings of the IEEE/CVF Conference on Computer Vision and Puttern Recognition. pages 22000-22009, 2024.
[9] Zitan Chen, Zhuang Qi, Xiao Cao, Xiangxian Li, Xiangxu Meng, and Lei Meng. Class-level structural relation modeling and smoothing for visual representation learning. In Proceedings of the 31st ACM International Conference on Multimedia, pages 2964-2972, 2023.
[10] Xiao Cao, Beibei Lin, Bo Wang. Zhiyong Huang, and Robby Tan. Ssnerf: Sparse view semi-supervised neural radiance fields with augmentation. arXiv preprint arXiv:2408.091 2024.
[11] Lei Tan, Yukang Zhang, Keke Han, Pingyang Dai, Yan Zhang, Yongjian Wu, and Rongrong Ji. Rle: A unified perspective of data augmentation for spectsl re-identification. Advances in Neural Information Processing Systems, 37:126977-126999 2024.
[12] Yulin Li, Jianfeng He, Tianzhu Zhang, Xiang Liu, Yongdong Zhang, and Feng Wu. Diverse part discovery: Occluded person re- identificaatinn with part-aware transformer. In Proceedings of the IEEE/CVF conference computer vision and pattern recognition, pages 2898 2907, 2021.
[13] Chuanming Wang, Yuxin Yang. Mengshi Qi, Huanhuan Zhang, and Huadong Ma. Towards efficient object re-identification with novel cloud-edge collaborative framework. In Proceedings the AAAI Conference on Artificial Intelligence, volume 39, pages 7600-7600 2025.
[14] Yingying Feng, Jie Li, Chi Xie, Lei Tan, and Fiayi Ji. Multi-modal object re-identification via sparse mixture-of-experts Forty-second International Conference on Machine Learning.
[15] Yizhen Jia, Rong Quan, Yue Feng, Haiyan Chen, and Jie Qin. Doubly contrastive learning for source- free domain adaptive person search. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 39, pages 3949-3957, 2025.
[16] Huy Nguyen, Kien Nguyen, Sridha Sridharan, and Clinton Fookes. Ag-reid. v2: Bridging acrial and ground views for person re-identification. IEEE Transactions on Information Forensics and Security, 19:2896-2908, 2024.
[17] Yifan Sun, Liang Zheng, Yali Li, Yi Yang, Qi Tian, and Shengjin Wang. Learning part-based convolutional fcatures for person re-identification. IEEE transactions pattern analysis and machine intelligence, 43(3):902-917, 2019.
[18] Tianlong Chen, Shaojin Ding. Jingyi Xie, Ye Yuan, Wuyang Chen, Yang Yang, Zhou Ren, and Zhangyang Wang. Abd-net: Attentive but diverse person re-identification. In Proceedings of the IEEE/CVF interna- tional conference on computer vision. pages 3351-8361 2019.
[19] Guan'a an Wang. Shuo Yang, Huanyu Liu, Zhicheng Wang, Yang Yang. Shuliang Wang, Gang Yu, Erjin Zhou, and Jian Sun. High-order information matters: Learning relation and topology for occluded person re- re-identification. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pages 6449 0458-445880880 2020.

---

[20] Lei Tan, Jiaer Xia, Wenfeng Liu, Pingyang Dai. Yongjian Wu, and Liujuan Cao. Occluded person re- identification via saliency-guided patch transfer. In Proceedings of the AAAI conference on artificial intelligence, volume 38, pages 5070-5078, 2024.
[21] YuTeng Ye. Hang Zhou, Jiale Cai, Chenxing Gao. Youjia Zhang, Junle Wang. Qiang Hu, Junqing Yu. and Wei Yang. Dynamic feature pruning and consolidation for occluded person re-identification. In Proceedings of the AAAI conference on artificial intelligence, volume 38, pages 6684-6692, 2024.
[22] Zhiyin Shao, Xinyu Zhang, Changxing Ding, Jian Wang, and Jingdong Wang. Unified pre-training with pscudo texts for text-to-imape image person re- re-identification. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 11174-11184 2023.
[23] Yang Qin, Yingke Chen, Dezhong Peng. Xi Peng, Jocy Tianyi Zhou, and Peng Hu. coisy-correspondene learning for text-to-image person re-identification. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 27 17197-27,06, 2024.
[24) Zhiwei Zhao, Bin Liu, Yan Lu, Qi Chu, and Nenghai Yu. Unifying multi-modal uncertainty modeling and semantic alignment for text-to-image person re-identification. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pages 7534-7542, 2024.
[25] Bingjun Luo, Jinpeng Wang, Zewen Wang. Junjie Zhu, and Xibin Zhao. Graph-based cross-domain knowledge distillation for cross-dataset text-to-image person retrieval. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 39, pages 568-576, 2025.
[26] Zi Wang, Chenglong Li, Aihua Zheng, Ran He, and Jin Tang. Interact, embed, and enlarge: Boosting sspelity-specific representations for multi-modal person re-identification. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 36, pages 2633-264) 2022.
[27] Pingping Zhang, Yuhao Wang, Yang Liu, Zhengzheng Tu, and Huchuan Lu. Magic tokens: Select diverse tokens for multi-modal object re-identification. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17117-17126, 2024.
[28] Yuhao Wang, Yang Liu, Aihua Zheng, and Pingping Zhang. Decoupled feature- mixture of experts for multi-modal object re-identification. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 39, pages 8141-8149, 2025.
[29] Minsu Kim, Seungryong Kim, Jungin Park, Seongheon Park, and Kwanghoon Sohn. Partmix: Regular- ization strategy learn part discovery for visible-infrared person re-identification. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 18621-18632, 2023.
[30] Lei Tan, Yukang Zhang, Shengmei Shen, Yan Wang. Pingyang Dai, Xianming Lin, Yongjian Wu, and Rongrong Ji. Exploring invariant representation for visible-infrand person re-identification. arXiv preprint arXiv:2302. 008884 2023.
[31] Yukang Zhang, Yan Yan, Yang Lu, and Hanzi Wang. Adaptive middle modality alignment learning for visible-infrared person re-identification. International Journal of Computer Vision, 133(4):2176-2) 2196. 2025.
[32] Yukang Zhang and Hanzi Wang. Diverse embedding expansion network and low-light cross- modality benchmark for visible-infrared person re-identification. In Proceedings of the TEEE/CVE conference on computer vision and pattern recognition, pages 2153-2162, 2023.
[33] Kaijie Ren and Lei Zhang. Implicit discriminative knowledge learning for visible-infrared person re- identification. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, pages 393-402, 2024.
[34] Kien Nguyen, Clinton Fookes, Sridha Sridharan, Feng Liu, Xiaoming Liu, Arun Ross, Dana Michalski, Huy Nguyen, Debayan Deb, Mahak Kothari, et al. Ag-reid 2023: grouud person re-identification challenge results. In 2023 IEEE International Joint Conference on Biometrics (IJBB), pages 1-10. IEEE, 2023.
[35] Tianjiao Li, Jun Liu, Wei Zhang, Yun Ni, Wenqian Wang, and Zhiheng Li. Uav-human: A large benchmark for human behavior understanding with unmanned acrial vehicles. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 16266-16211 2021.
[36] Ame Schumann and Jürgen Metzler. Person re- iddetificcaiinn across aerial and ground based cameras by deep feature fusion. In Automatic Target Recognition XXVII, volume 10202, pages 56-67. SPIE, 2017.
[37] Yuhao Wang. Xiang Hu, Lixin Wang. Pingping Zhang, and Huchuan Lu. Sd-reid: View-aware stable diffusion for aerial- grouund person re-identification. arXiv preprint arXiv:2504.0953 2025.
[38] Pingping Zhang, Xiang Hu, Yuhao Wang, and Huchuan Lu. Latex: Leveraging attribute-based based text knowledge for derial-groun person re-identification. arXiv preprint 2xx1::2503..3722. 2025.
12

---

[39] Alexey Dosovitskiy. Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai. Thomas Unterthiner, Mostafa Dehghani, Matthis Minderer, G Heigold. Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. International Conference on Learning Representations, 2020.
[40] Lei Tan, Pingyang Dai, Rongrong Ji, and Yongjian Wu. Dynamic prototype mask for occluded person re-identification. In Proceedings of the 30th ACM international conference on multimedia, pages 531-540. 2022.
[41] Mang Ye, Jianbing Shen, Gaojie Lin, Tao Xiang, Ling Shao, and Steven CH Hoi. Deep learning for person re-identification: A survey and outlook. IEEE transactions on pattern analysis and machine intelligence, 44(6)28872-2893, 2021.
[42] Lingxiao He, Xingyu Liao, Wu Liu, Xinchen Liu, Peng Cheng, and Tao Mei. Fastreid: A pytorch toolbox for general instance re-identification. In Proceedings of the 31st ACM International Conference on Multimedia, pages 9664 9667, 2023.
[43] Hao Luo, Youzhi Gu, Xingyu Liao, Shenqi Lai, and Wei Jiang. Bag of tricks and a strong baseline for deep person re-identification. Proceedings the IEEE/CVF conference on computer vision and pattern recogmition workshops, pages 0-0, 2019.
[44] Guanshuo Wang. Yufeng Yuan, Xiong Chen, Jiwei Li, and Xi Zhou, Learning discriminative features with multiple granularities for person re-identification. In Proceedings of the 26th ACM international conference on Multimedia, pages 274 28222282222 2018.
[45] Ratnesh Kuma, Edwin Weill, Farzin Aghdasi, and Parthasarathy Sriram. Vehicle re-identification. an efficient baselinc using triplet embedding. In 2019 International Joint Conference on Neural Networks (IJCN), pages 1-9, TEEE, 2019.
[46] Ratnesh Kumar, Edwin Weill, Farzin Aghdasi, and Parthasarathy Sriram. A strong and efficient baseline for vehicle re-identification using deep triplet embedding. Journal of Artificial Intelligence and Soft Computing Research, 10(1):27-45, 2020.
[47] Yoonki Cho, Jaeyoon Kim, Woo J Kim, Junsik Jung, and Sung-Fui Yoon. Generalizable person re- identification via balancing alignment and uniformity. Advances in Neural Information Processing Systems, 37:47069-44000 47093, 2024.
[48] Hao Ni, Yuke Li, Lianli Gao, Heng Tao Shen, and Jingkuan Song. Part-aware transformer for generalizable person re- re-identification. In Proceedings of the IEEE/CVF international conference on computer vision, pages 11280 1289, 2023.
[49] Yuhai Wang and Maryam Pishgar. Dynamic token selection for aerial-ground person re-identification. arXiv preprint arXiv:2412.00433 2024.
[50] Kaiyang Zhou, Yongxin Yang, Andrea Cavallaro, and Tao Xiang. Learning generalisable omni-scale representations for person re-identification. IEEE transactions on pattern analysis and machine intelligence, 44(9):5056-5069, 2021.
[51] Shining Wang, Yunlong Wang, Ruiqi Wu, Bingliang Jiao, Wenxuan Wang, and Peng Wang. Secap: Self-calibrating and adaptive prompts for cross-view person re-identification in aerial-ground networks. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 221 19-22128, 128, 2025.
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A Supplementary Material
A.1 Visualization results of LTPS
Input Image
ligned Image
Figure 3: Qualitative comparison before and after LTPS alignment. The input image (red) exhibits significant geometric distortion due to extreme viewpoint variation. After applying the Learnable Thin Plate Spline (LTPS) transformation (green), the image is spatially rectified, highlighting improved geometric consistency and local structure alignment.
To further demonstrate the effectiveness of our LTPS strategy, we visualize LTPS outputs in Fig. 3. To facilitate better visualization, we aggregate the LTPS transformations from all layers and apply the fused transformation the original input image. As shown in Fig. 3., the input images exhibit severe geometric distortions caused by extreme acrial-ground viewpoint differences. After applying the Learnable Thin Plate Spline (LTPS) transformation, visual structure the person becomes significantly more regular consistent. These rectified features allow the network to focus on semantically consistent regions and reduce the burden of learning viewpoint-invariant representations purely through data. This visual evidence supports the quantitative gains observed in ablation studies and highlights the role of LTPS in improving cross -ies structural correspondence.
A.2 Effectiveness Analysis of the LTPS Module
Effect of the Deformation Constraint. We evaluate the rolc of the deformation constraint Laetonn LTPS module. shown in Table 8, consider three variants: (1) Fixed-ange rotation, where a constant rotation applied without learnalle deformation; (2) LTPS without Lieform, where the rotation angle is freely optimized under the supervision of the RelD loss alone; and (3) LTPS with Letefor- our final proposed design that jointly optimizes the rotation angle under both the RelD loss and the deformation constraint. The comparison reveals that unconstrained optimization of rotation angles can easily lead excessive unstable transformations, degrading feature alignment and recognition performance. deformation process, preventing overfitting to local minima and improving global spatial consistency. As result. LTPS with Lasoerm achieves the best performance across all CARGO evaluation protocols. demonstrating the necessity of this constraint in maintaining geometric stability during feature learning.
contrast, incorporating Liefern effectively regularizes the
Table 8: Performance comparison of fixed-angle rotation and LTPS on the CARGO dataset. Results are reported under four matching protocols. The best values per column are shown in bold.
Protocol ALL
Protocol 2: G++
Protocol Ae*A
Protocal A+4G
Setting
Rankl
mAP
miNP
Rankl
mAP
mINP
Rankl
mAP
miNP
Rankl
mAP
mINP
Different rotation of GSAlign
fixed-angle rotation
60.65
52.06
38.83
78.89
68.21
57.64
77.50
59.54
40.73
58.45
53.74
43.84
LTPS without Cactonn
59.94
52.67
39.25
76.79
69.83
57.68
75.00
43.49
43.49
56.38
53.22
43,96
LTPS Lastonn
56.35
Comparison with Original TPS. We further compare the proposed LTPS with the original TPS, which applies standard TPS transformations at every layer. The experiments are conducted on the CARGO dataset, and the results summarized in Table The results indicate that while the original TPS partially mitigate feature misalignment the A G setting, fixed rotation ++ G scenario. For example, although GSAlign with original TPS achieves minor gains under A + G. suffers from noticeable degradation under G + G. leading to an overall performance even lower than that of ViT. In contrast, LTPS incorporates learnable control points and deformation regularization that
interpolation strategy fail to generalize well to the
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constrains rotation magnitudes, effectively preventing excessive geometric distortion. This design allows LTPS maintain strong adaptability under A 6- C conditions while significantly enhancing retrieval accuracy under <>> scenarios. Overall, LTPS achieves more stable and balanced performance across different view settings. making it a more effective and generalizable solution than the standard TPS approach.
Table 9: Performance comparison of original TPS and LTPS on CARGO dataset. Here, GSAlign- O is ViT applying original TPS transformations at every layer.
Protocol I: ALL
Protocol 2: G GG
Protocol 3: A
Protocol 4; G
Setting
Rank
mAP
mINP
Rank1
mAP
mINP
Rank1
mAP
mINP
Rank1
mAP
mINP
GSAlign-O
63,78
55.18
41,63
80.36
71,21
57.06
82.50
65.44
47.37
58.51
57.72
49.02
GSAlign
66.67
56.35
41.42
83.04
71.57
58.68
82.50
67.70
51.80
65.96
60.50
49.32
Efficiency Analysis of LTPSS. To assess the computational efficiency of the proposed I.TPS module, we compare the inference cost between the haseline ViT model and the I.TPS- -TPS--nteerated GSAlign. The results show that both models have identical FLOPs of 17.67 GFLOPs, indicating that the additional computational cost introduced by LTPS negligible. This mainly because Thin Plate Spline (TPS) transformation in LTPS lightweight- computation is minimal compared to the large-scale matrix multiplications in the VIT backbone. Consequently, the LTPPSenhanced GS Align maintains nearly the same theoretical inference cost as the original VIT, ensuring real-time efficiency. We further measure the practical inference speed under identical experimental conditions. Using a batch size of 128, GSAlign achieves an average inference time of 0.791 seconds per batch, while the baseline ViT-based model requires 0.778 seconds per batch. The minor difference of 0.013 seconds demonstrates that integrating LTPS at each layer introduces no significant runtime overhead, confirming the model's 's suitability for real-time deployment.
A.3 Limitation and Boroader Impact
Despite the promising results the proposed Dynamic Cross-vice view Alignment framework in acrial acrial-ground person re-identification, there are still some limitations. First, the performance of the LTPS module depends the accuracy of predicted control point offsets and rotation angles. Under extreme viewpoint changes heavy occlusions, the model may fail to learn reliable spatial transformations, which affect feature alignment. Moreover, in acrial-acrial scenarios where geometric variations are relatively small and pedestrians appear with limited texture information, the L.TPS and DAM modules may introduce unnecessary local transformations, leading to a slight performance drop. This is because these modules are primarily designed to handle large spatial misalignments in the grriud setting. In addition, method is currently evaluated specilic grruudd datasets; howeven, experiments on real-world scenarios demonstrate that the framework remains effective beyond these datascts. Nevertheless, we believe the proposed framework provides a promising direction for modeling spatial variations and focusing on dentitty-reeevan regions in cross-view re-identification.
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NeurIPS Paper Checklist
1. Claims
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Reviews:
[{"review_id": "ZD6C7UJ4XF", "reviewer": "Reviewer_tvZF", "summary": "The 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.", "questions": "1. 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.", "limitations": "Yes.", "rating": 3, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\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.", "was_revised": true, "final_justification": "Although my concerns regarding the aerial-to-aerial (A $\\rightarrow$ A) scenarios remain, the additional results on the AG-ReID and AG-ReID v2 datasets provided by the authors clearly strengthen the proposed GSAlign method. As a result, I have decided to slightly raise my assessment of the paper.", "rebuttal": "Thanks for your constructive feedback. Below, we respond to your key concerns point by point.\n> **Q1:**  Please include experimental results on real-world aerial-ground person re-identification datasets, such as AG-ReID and its successor, AG-ReID v2.\n> \n\n**A1:** Following your suggestion, we conducted comprehensive comparative experiments on the AG-ReID [1] and AG-ReID v2 [2] datasets. Compared to previous state-of-the-art methods, our proposed GSAlign still achieves significant improvements. These new results will be included in the final version to strengthen the empirical validation of the proposed GSAlign further.\n\nTable A: Performance comparison of the mainstream methods under two settings of the AG-ReID dataset. Here, VDT-R is the reproduced version of its open-source code.\n\n| Method | A→G |  |  | G→A |  |  |\n| --- | --- | --- | --- | --- | --- | --- |\n|  | Rank1 | mAP | mINP | Rank1 | mAP | mINP |\n| SBS [3] | 73.54 | 59.77 | - | 73.70 | 62.27 | - |\n| BoT [4] | 70.01 | 55.47 | - | 71.20 | 58.83 | - |\n| OsNet [5] | 72.59 | 58.32 | - | 74.22 | 60.99 | - |\n| VV [6] | 77.22 | 67.23 | 41.43 | 79.73 | 69.83 | 42.37 |\n|  AG-ReIDv1 [1] | 81.28 | 72.38 | - | 82.64 | 73.35 | - |\n| ViT [7] | 81.47 | 72.61 | - | 82/85 | 73.39 | - |\n| VDT [8] | 82.91 | 74.44 | 51.06 | 86.59 | 78.57 | 52.87 |\n| VDT-R [8] | 82.73 | 74.30 | 51.77 | 84.62 | 76.56 | 49.70 |\n| GSAlign | **86.74** | **84.00** | **73.62** | **87.94** | **87.17** | **78.21** |\n\nTable B: Performance comparison of the mainstream methods under four settings of the AG-ReID v2 dataset\n\n| Method | A→G |  | G→A |  | A→W |  | W→A |  |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- |\n|  | Rank1 | mAP | Rank1 | mAP | Rank1 | mAP | Rank1 | mAP |\n| BoT [4] | 85.40 | 77.03 | 84.65 | 75.90 | 89.77 | 80.48 | 84.65 | 76.90 |\n| AG-ReIDv1 [1] | 87.70 | 79.00 | 87.35 | 78.24 | 93.67 | 83.14 | 87.73 | 79.08 |\n| VDT [8] | 86.46 | 79.13 | 86.14 | 78.12 | 90.00 | 82.21 | 85.26 | 78.52 |\n| AG-ReIDv2 [2] | 88.77 | 80.72 | 87.86 | 78.51 | **93.62** | 84.85 | **88.61** | 80.11 |\n| SeCap [9] | 88.12 | 80.84 | 88.24 | 79.99 | 91.44 | 84.01 | 87.56 | 80.15 |\n| GSALign | **91.47** | **89.78** | **88.29** | **87.62** | 93.30 | **91.84** | 88.12 | **88.62** |\n\n[1]Nguyen, Huy, et al. \"Aerial-ground person re-id.\" ICME, 2023.\n\n[2]Nguyen, Huy, et al. \"AG-ReID. v2: Bridging aerial and ground views for person re-identification.\" IEEE TIFS, 2024.\n\n[3]He, Lingxiao, et al. \"Fastreid: A pytorch toolbox for general instance re-identification.\" MM, 2023.\n\n[4]Luo, Hao, et al. \"Bag of tricks and a strong baseline for deep person re-identification.\" CVPR,  2019.\n\n[5]Zhou, Kaiyang, et al. \"Learning generalisable omni-scale representations for person re-identification.\" TPAMI, 2021.\n\n[6]Kuma, Ratnesh, et al. \"Vehicle re-identification: an efficient baseline using triplet embedding.\" IJCNN, 2019.\n\n[7]Dosovitskiy, Alexey, et al. \"An image is worth 16x16 words: Transformers for image recognition at scale.\"  ICLR, 2021\n\n[8]Zhang, Quan, et al. \"View-decoupled transformer for person re-identification under aerial-ground camera network.\" CVPR, 2024.\n\n[9] Wang, Shining, et al. \"SeCap: Self-Calibrating and Adaptive Prompts for Cross-view Person Re-Identification in Aerial-Ground Networks.\" CVPR,  2025.\n\n\n> **Q2:** Please provide an explanation for why the proposed LTPS and DAM modules lead to decreased performance in the aerial-aerial scenarios.\n> \n\n**A2:**  Thanks for your suggestion. This work is mainly focused on the viewpoint variations in the aerial-to-ground (A→G) setting and reaches a satisfying performance. Although the aerial-to-aerial(A→A) scenario involves similar viewpoints, pedestrian targets are typically very small, with scarce appearance textures and relatively uniform backgrounds. In such cases, the model does not need extra alignment transformations. Our proposed LTPS and DAM modules are designed to handle large pose variations and spatial misalignments, which are particularly severe in the aerial-to-ground (A→G) setting. These modules significantly improve performance in A→G scenarios. However, in A→A settings where geometric differences are limited, such modules may introduce unnecessary local transformations, resulting in a slight performance drop. Overall,  our method still achieves competitive results in the A→A scenario and delivers substantial improvements in the core task of A→G re-identification, demonstrating its overall effectiveness and robustness. Your suggestion indeed points us toward future research directions in which we can explore in the future: how to improve aerial-to-ground (A→G)  performance while maintaining aerial-to-aerial(A→A) performance. We will include this discussion in the limitation section. Thank you again for your constructive feedback.\n\n> **Q3:** Please improve the overall clarity of the paper according to the issues outlined in Weakness 3.\n> \n\n**A3:** We thank the reviewer for the constructive comments and helpful suggestions to improve our paper.\n\n- We would like to clarify that LTPS is fundamentally different from traditional TPS-based approaches. While prior works typically apply TPS as a fixed pre-processing step or uniformly across network layers, our LTPS module is designed as a task-specific, learnable transformation mechanism that is selectively activated within a ViT-based ReID framework. Unlike classical TPS, LTPS is optimized directly by the ReID loss to adaptively adjust spatial representations and mitigate aerial-ground viewpoint misalignment within a ViT structure. Moreover, LTPS introduces a deformation loss to regularize transformation strength, ensuring that geometric changes are meaningful and moderate. This prevents excessive distortion, which we found in preliminary experiments to significantly degrade performance. To our knowledge, this is the first work to incorporate a regularized and partially-activated TPS module into a transformer-based architecture for person ReID. We will revise the manuscript to clearly highlight these distinctions and cite relevant TPS-based studies.\n\n- The incorrect citation of VDT (Ln 103) will be corrected.\n\n- We agree that Fig. 2 can be improved. In the final version, we will refine the figure and define terms such as “input tokens,” “located points,” and “aligned tokens” for clarity.\n\n- The distinction between DAM and DPM will be clarified. Different from DPM, which uses the classification matrix as the prototype and gets the mask from sample-to-prototype similarity, DAM directly uses the inner batch sample-to-sample similarity to achieve semantic alignment. Experimental results in Table 5 show the advantage of DAM (Inner-Batch) when compared to the DPM (Classification Matrix). We will also provide proper context and justification for the use of Memory Bank and Classification Matrix in Table 5."}, {"review_id": "SK6TlOMpqy", "reviewer": "Reviewer_nnjx", "summary": "This 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.", "questions": "Main 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.", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Quality: 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.", "was_revised": true, "final_justification": "My concerns have been addressed. Overall I think it is a good appearance / semantic auto-alignment method for AG-ReID, offering some new insights and a good performance improvement in most cases.", "rebuttal": "Thanks for your constructive feedback. Below, we respond to your key concerns point by point.\n> **Q1:**  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> \n\n**A1:**  Thanks for your suggestion. We would like to clarify that the goal of LTPS is not to generate an \"absolutely correct\" rotated image, nor does it rely on any pre-aligned ground truth images. LTPS is optimized by the ReID losses to get better alignment. This is the same way with the Spatial Transformation Network [1], which is learned by the optimization of classification losses.\nFor a ViT-based model, there is no strictly defined \"correct pose\" for input images. Instead, the key objective is to alleviate the misalignment between aerial and ground viewpoints through viewpoint adjustment, thereby enabling more stable and discriminative feature representations. \n\nThe deformation loss L_deform in Eq. (10) is not intended to suppress rotation entirely. Rather, it serves as a regularization term to prevent excessive geometric deformation (e.g., overly large rotation angles) that could harm feature learning. In our preliminary experiments, directly applying fixed-angle rotations without constraint led to an average drop of about 6% in Rank-1 accuracy (as shown in Table C). Therefore, LTPS aims to mitigate cross-view misalignment through moderate and controlled geometric transformations, rather than pursuing perfect alignment on a per-image basis.\n\nTable C: Performance comparison of fixed-angle rotations and LTPS on the CARGO dataset.\n\n| Method | ALL |  |  | G→G |  |  | A→A |  |  | A→G |  |  |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n|  | Rank1 | mAP | mINP | Rank1 | mAP | mINP | Rank1 | mAP | mINP | Rank1 | mAP | mINP |\n| without L_deform | 60.65 | 52.06 | 38.83 | 78.89 | 68.21 | 57.64 | 77.50 | 59.54 | 40.73 | 58.45 | 53.74 | 43.84 |\n| with L_deform | **66.67** | **56.35** | **41.42** | **83.04** | **71.57** | **57.68** | **82.50** | **67.70** | **51.80** | **65.96** | **60.60** | **49.32** |\n\n---\n\n> **Q2:** 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> \n\n**A2:**  As shown in Table 4 in mamuscript, we provide detailed ablation studies to demonstrate the effect of applying LTPS at different network depths. The results indicate that applying LTPS only to shallow or deep layers leads to performance degradation, while applying it across all layers yields the best performance. This is because shallow features are more sensitive to local structures and low-level textures, and using LTPS alone at these layers can be affected by unstable rotation predictions. Deep layers, on the other hand, capture more semantic information, and applying LTPS only at this stage is insufficient to bridge cross-view differences. Applying LTPS across all layers helps maximize the mitigation of viewpoint misalignment. Regarding the reviewer’s concern that \"shallow layers cannot predict rotation well\",  as NeurIPS rebuttal rules do not allow adding new figures, we are unable to include them at this stage. But we are willing to add them in the camera-ready for a better explanation.\n\n---\n\n> **Q3:** 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> \n\n**A3:** Following your suggestion, in Table D, we compare various alignment methods, including implicit approaches such as BAU, PAT, and DAGait, as well as the explicit alignment method GSAlign. The results show that BAU[1], PAT[3], and DAGait[4] all perform significantly worse than our baseline and GSAlign on the CARGO dataset. In contrast, our proposed combination of LTPS and DAM consistently achieves superior performance under the same experimental conditions. Notably, DAGait, which is originally designed as an explicit alignment method for gait recognition, performs poorly on person ReID datasets. Its accuracy on the test set is only around 0.6%, which is substantially lower than all other compared methods. Detailed results are provided in Table D for reference. As for SEAS[2], since the official code has not been released and the limited rebuttal period, we were unable to include its comparison in time.\n\nTable D: Performance comparison of the implicit methods and alignment method under four settings of the CARGO dataset. \n\n| Method | ALL |  | G→G |  | A→A |  | A→G |  |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- |\n|  | Rank1 | mAP | Rank1 | mAP | Rank1 | mAP | Rank1 | mAP |\n| BAU [1] | 45.20 | 38.40 | 61.60 | 51.20 | 50.00 | 42.60 | 40.40 | 36.70 |\n| PAT [3] | 37.90 | 15.30 | 52.70 | 24.20 | 50.00 | 23.10 | 35.10 | 15.50 |\n| DAGait [4] | 0.66 | - | 0.77 | - | 0.64 | - | 0.42 | - |\n| GSALign | **66.67** | **56.35** | **83.04** | **71.57** | **82.05** | **67.70** | **65.96** | **60.50** |\n\n[1] Cho, Yoonki, et al. \"Generalizable person re-identification via balancing alignment and uniformity.\" NeurlPS, 2024. \n\n[2] Zhu, Haidong, et al. \"Seas: Shape-aligned supervision for person re-identification.\" CVPR, 2024. seas\n\n[3] Ni, Hao, et al. \"Part-aware transformer for generalizable person re-identification.\" CVPR,  2023. Pat\n\n[4] Wu, Z., et al. “AGait: Generalized Skeleton-Guided Data Alignment for Gait Recognition.” ArXiv, 2025\n\n> **Q4:** Could you provide the performance on the AG-ReID 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 dataset with real data. Can the authors validate the feasibility of their method on real data?\n> \n\n**A4:** Following your suggestion, we conducted comprehensive comparative experiments on the AG-ReID dataset [1], covering both cross-domain settings: A→G and G→A. As shown in Table E, extensive experiments on this challenging real-world dataset demonstrate the effectiveness of GSAlign. Compared to previous state-of-the-art methods, our proposed GSAlign still achieves significant improvements.\n\nTable E: Performance comparison of the mainstream methods under two settings of the AG-ReID dataset. Here, VDT-R is the reproduced version of its open-source code.\n\n| Method | A→G |  |  | G→A |  |  |\n| --- | --- | --- | --- | --- | --- | --- |\n|  | Rank1 | mAP | mINP | Rank1 | mAP | mINP |\n| SBS [6] | 73.54 | 59.77 | - | 73.70 | 62.27 | - |\n| BoT [7] | 70.01 | 55.47 | - | 71.20 | 58.83 | - |\n| OsNet [8] | 72.59 | 58.32 | - | 74.22 | 60.99 | - |\n| VV [9] | 77.22 | 67.23 | 41.43 | 79.73 | 69.83 | 42.37 |\n| AG-ReIDv1 [5] | 81.28 | 72.38 | - | 82.64 | 73.35 | - |\n| ViT [10] | 81.47 | 72.61 | - | 82/85 | 73.39 | - |\n| VDT [11] | 82.91 | 74.44 | 51.06 | 86.59 | 78.57 | 52.87 |\n| VDT-R [11] | 82.73 | 74.30 | 51.77 | 84.62 | 76.56 | 49.70 |\n| GSAlign | **86.74** | **84.00** | **73.62** | **87.94** | **87.17** | **78.21** |\n\n[5] Nguyen, Huy, et al. \"Aerial-ground person re-id.\" ICME, 2023.\n\n[6] He, Lingxiao, et al. \"Fastreid: A pytorch toolbox for general instance re-identification.\" MM, 2023.\n\n[7] Luo, Hao, et al. \"Bag of tricks and a strong baseline for deep person re-identification.\" CVPR, 2019.\n\n[8] Zhou, Kaiyang, et al. \"Learning generalisable omni-scale representations for person re-identification.\" TPAMI, 2021.\n\n[9] Kuma, Ratnesh, et al. \"Vehicle re-identification: an efficient baseline using triplet embedding.\" IJCNN, 2019.\n\n[10] Dosovitskiy, Alexey, et al. \"An image is worth 16x16 words: Transformers for image recognition at scale.\" ICLR, 2021\n\n[11] Zhang, Quan, et al. \"View-decoupled transformer for person re-identification under aerial-ground camera network.\" CVPR, 2024."}, {"review_id": "svp7S8Q7fW", "reviewer": "Reviewer_5Ks3", "summary": "This 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.", "questions": "1. 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.", "limitations": "Yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 4, "strengths_and_weaknesses": "Strengths:\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.", "was_revised": true, "final_justification": "The experiment on the AG-ReiD dataset and hyperparameters has addressed some of my concerns. I have decided to maintain this positive score.", "rebuttal": "Thanks for your constructive feedback. Below, we respond to your key concerns point by point.\n> **Q1:**  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.\n> \n\n**A1:** Following your suggestion, we conducted comprehensive comparative experiments on the AG-ReID dataset [1], covering both cross-domain settings: A→G and G→A. As shown in Table F, extensive experiments on this challenging real-world dataset demonstrate the effectiveness of GSAlign. Compared to previous state-of-the-art methods, our proposed GSAlign still achieves significant improvements.\n\nTable F: Performance comparison of the mainstream methods under two settings of the AG-ReID dataset. Here, VDT-R is the reproduced version of its open-source code.\n\n| Method | A→G |  |  | G→A |  |  |\n| --- | --- | --- | --- | --- | --- | --- |\n|  | Rank1 | mAP | mINP | Rank1 | mAP | mINP |\n| SBS [2] | 73.54 | 59.77 | - | 73.70 | 62.27 | - |\n| BoT [3] | 70.01 | 55.47 | - | 71.20 | 58.83 | - |\n| OsNet [4] | 72.59 | 58.32 | - | 74.22 | 60.99 | - |\n| VV [5] | 77.22 | 67.23 | 41.43 | 79.73 | 69.83 | 42.37 |\n| AG-ReIDv1 [1] | 81.28 | 72.38 | - | 82.64 | 73.35 | - |\n| ViT [6] | 81.47 | 72.61 | - | 82/85 | 73.39 | - |\n| VDT [7] | 82.91 | 74.44 | 51.06 | 86.59 | 78.57 | 52.87 |\n| VDT-R [7] | 82.73 | 74.30 | 51.77 | 84.62 | 76.56 | 49.70 |\n| GSAlign | **86.74** | **84.00** | **73.62** | **87.94** | **87.17** | **78.21** |\n\n[1]Nguyen, Huy, et al. \"Aerial-ground person re-id.\" ICME, 2023.\n\n[2]He, Lingxiao, et al. \"Fastreid: A pytorch toolbox for general instance re-identification.\" MM, 2023.\n\n[3]Luo, Hao, et al. \"Bag of tricks and a strong baseline for deep person re-identification.\" CVPR, 2019.\n\n[4]Zhou, Kaiyang, et al. \"Learning generalisable omni-scale representations for person re-identification.\" TPAMI, 2021.\n\n[5]Kuma, Ratnesh, et al. \"Vehicle re-identification: an efficient baseline using triplet embedding.\" IJCNN, 2019.\n\n[6]Dosovitskiy, Alexey, et al. \"An image is worth 16x16 words: Transformers for image recognition at scale.\" ICLR, 2021\n\n[7]Zhang, Quan, et al. \"View-decoupled transformer for person re-identification under aerial-ground camera network.\" CVPR, 2024.\n\n> **Q2:**  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?\n> \n\n**A2:** In Eq. (6), the introduced fusion factor $\\eta$ is used to control the weight of the residual branch in the final feature aggregation. Specifically, Eq. (6) adopts a residual structure that combines the main branch features and the auxiliary branch features through weighted summation, in order to balance their contributions to the final representation.\n$\\eta$ is not a learnable parameter; instead, it is a manually defined constant based on empirical evaluation. During model tuning, we conducted systematic experiments with different values of $\\eta$, and selected $\\eta$=0.2 based on the performance on the validation set. We found that this value effectively regularizes the learning process by leveraging the benefits of residual connections while maintaining feature stability. We chose to perform the ablation study of $η$ only on the CARGO-ALL dataset because it provides a comprehensive evaluation across the entire CARGO benchmark. Therefore, the selection of $\\eta$ was based on its performance on CARGO-ALL, which reflects the overall capability of the model across diverse cross-view scenarios.\n\nTable G: Performance comparison of different $\\eta$ values on the CARGO-ALL dataset.\n\n| CARGO-ALL | Rank1 | mAP | mINP |\n| --- | --- | --- | --- |\n| $\\eta$=0.01 | 64.10 | 57.85 | 44.76 |\n| $\\eta$=0.1 | 65.12 | 56.35 | 43.62 |\n| $\\eta$=0.2 | 66.67 | 56.35 | 41.42 |\n| $\\eta$=0.5 | 64.55 | 56.92 | 44.03 |\n\n> **Q3:** 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\n**A3:** We conducted experiments to compare the performance of the original TPS (i.e., applying original TPS transformations at every layer) with our proposed LTPS. The comparison was conducted on the CARGO dataset, and the results are presented in Table H.\nThe results show that while the original TPS can partially alleviate feature misalignment under the aerial-to-ground (A→G) scenario, its fixed rotation and interpolation strategies perform poorly when adapting to ground-to-ground (G→G) view matching. For instance, compared to ViT and VDT, dca-tps achieves a slight improvement under A→G but suffers from degraded performance under G→G, with an overall average performance even lower than ViT.\nIn contrast, our proposed LTPS introduces learnable control points and constrained rotation magnitudes through regularized design, which avoids excessive perturbation to local geometric structures. This enables LTPS to maintain strong adaptability under A→G conditions while significantly improving retrieval accuracy under G→G scenarios. Overall, LTPS provides more balanced performance across both view settings, making it a more suitable solution for the task than the standard TPS approach.\n\nTable H: Performance comparison of original TPS and LTPS on the CARGO dataset. Here, GSAlign-O is  ViT applying original TPS transformations at every layer.\n\n| Method | ALL |  |  | G→G |  |  | A→A |  |  | A→G |  |  |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n|  | Rank1 | mAP | mINP | Rank1 | mAP | mINP | Rank1 | mAP | mINP | Rank1 | mAP | mINP |\n| GSAlign-O | 63.78 | 55.18 | 41.63 | 80.36 | 71.21 | 57.06 | 82.50 | 65.44 | 47.37 | 58.51 | 57.72 | 49.02 |\n| GSAlign | 66.67 | 56.35 | 41.42 | 83.04 | 71.57 | 58.68 | 82.50 | 67.70 | 51.80 | 65.96 | 60.50 | 49.32 |"}, {"review_id": "hy7AMARSrt", "reviewer": "Reviewer_GGnw", "summary": "This 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.", "questions": "1. 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?", "limitations": "As 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.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strength:\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.", "was_revised": true, "final_justification": "Thanks for authors’ response and I will maintain my orignal rating score.", "rebuttal": "Thanks for your constructive feedback. Below, we respond to your key concerns point by point.\n> Q1:  Have you tried GSAlign on real-world datasets? If not, what are your expectations regarding generalization beyond synthetic domains?\n> \n\nA1: Following your suggestion, we conducted comprehensive comparative experiments on the real-world AG-ReID dataset [1], covering both cross-domain settings: A→G and G→A. As shown in Table I, extensive experiments on this challenging real-world dataset demonstrate the effectiveness of GSAlign. Compared to previous state-of-the-art methods, our proposed GSAlign still achieves significant improvements.\n\nTable I: Performance comparison of the mainstream methods under two settings of the AG-ReID dataset. Here, VDT-R is the reproduced version of its open-source code.\n\n| Method | A→G |  |  | G→A |  |  |\n| --- | --- | --- | --- | --- | --- | --- |\n|  | Rank1 | mAP | mINP | Rank1 | mAP | mINP |\n| SBS [2] | 73.54 | 59.77 | - | 73.70 | 62.27 | - |\n| BoT [3] | 70.01 | 55.47 | - | 71.20 | 58.83 | - |\n| OsNet [4] | 72.59 | 58.32 | - | 74.22 | 60.99 | - |\n| VV [5] | 77.22 | 67.23 | 41.43 | 79.73 | 69.83 | 42.37 |\n| AG-ReIDv1 [1] | 81.28 | 72.38 | - | 82.64 | 73.35 | - |\n| ViT [6] | 81.47 | 72.61 | - | 82/85 | 73.39 | - |\n| VDT [7] | 82.91 | 74.44 | 51.06 | 86.59 | 78.57 | 52.87 |\n| VDT-R [7] | 82.73 | 74.30 | 51.77 | 84.62 | 76.56 | 49.70 |\n| GSAlign | **86.74** | **84.00** | **73.62** | **87.94** | **87.17** | **78.21** |\n\n[1]Nguyen, Huy, et al. \"Aerial-ground person re-id.\" ICME, 2023.\n\n[2]He, Lingxiao, et al. \"Fastreid: A pytorch toolbox for general instance re-identification.\" MM, 2023.\n\n[3]Luo, Hao, et al. \"Bag of tricks and a strong baseline for deep person re-identification.\" CVPR, 2019.\n\n[4]Zhou, Kaiyang, et al. \"Learning generalisable omni-scale representations for person re-identification.\" TPAMI, 2021.\n\n[5]Kuma, Ratnesh, et al. \"Vehicle re-identification: an efficient baseline using triplet embedding.\" IJCNN, 2019.\n\n[6]Dosovitskiy, Alexey, et al. \"An image is worth 16x16 words: Transformers for image recognition at scale.\" ICLR, 2021\n\n[7]Zhang, Quan, et al. \"View-decoupled transformer for person re-identification under aerial-ground camera network.\" CVPR, 2024.\n\n> Q2:  What is the additional inference cost (such as FLOPs) introduced by LTPS per layer?\n> \n\nA2:  We used the fvcore library to compare the FLOPs of the original ViT and our GSAlign model with the LTPS module integrated. **The results show that both models have identical FLOPs of 17.67 GFLOPs, indicating that the additional computational overhead introduced by LTPS is negligible.** This outcome is attributed to the lightweight nature of the Thin Plate Spline (TPS) transformation used in LTPS. Compared to the large-scale matrix multiplications in the ViT backbone, the computation required by TPS is minimal and has little impact on the overall FLOPs. Therefore, the LTPS-enhanced model maintains nearly the same inference cost as the original ViT, ensuring real-time performance. Besides, we conducted an inference speed comparison between the baseline VDT model and our GSAlign model with LTPS under the same environment (Ubuntu 20.04, Intel Xeon Gold 6133 CPU @ 2.50GHz, NVIDIA RTX 4090 GPU, Python 3.8, PyTorch 1.12.0). **With a batch size of 128, GSAlign achieved an average inference time of 0.791 seconds per batch, while the baseline ViT-based model achieved 0.778 seconds per batch.** The difference in inference time is minimal, indicating that integrating LTPS at each layer does not introduce significant computational overhead, and the model retains strong potential for real-time deployment.\n\n> Q3: 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.\n> \n\nA3: The primary purpose of the DAM module is to serve as a regularization mechanism during training, rather than as a feature generator during inference. During training, DAM applies dynamic masking constraints in the feature space, encouraging the model to learn more discriminative and generalizable representations, thereby improving overall performance. However, in the inference phase, it is not feasible to dynamically generate masks for the test set due to the absence of label information and the need to maintain efficient parallel computation. Therefore, we perform retrieval using the unmasked prototypes. As a result, the effect of DAM lies mainly in its regularization role during training, rather than altering the feature distribution at inference. Its benefit is reflected indirectly through overall performance gains, rather than through separate measurements on inference-time features.\n\n> Q4: 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. Despite the weaknesses, I am looking forward to the authors’ reply.\n> \n\nA4:  We thank the reviewer for the insightful question. Indeed, the model’s performance can be partially influenced by the accuracy of keypoint detection, which may be challenged by common issues in UAV-captured imagery such as occlusion and low resolution.\nHowever, our work primarily focuses on addressing the cross-view misalignment problem between aerial and ground perspectives in person re-identification. While keypoint accuracy is important, it is not the central objective of this study. Through our experiments, we demonstrate the effectiveness and robustness of our approach in mitigating viewpoint misalignment.\nTo better handle low-quality images, we plan to explore the integration of super-resolution techniques, occlusion detection, and feature restoration in future work. These enhancements could improve image quality and model generalization, thereby boosting performance in challenging UAV-based scenarios. We look forward to advancing these directions in future research to further support real-world deployment."}]

Author rebuttal:

Meta-review: The paper proposed a new method for the aerial-ground person re-identification task. It received one accept, two borderline accepts, and one borderline reject from four very professional reviewers. Reviewers acknowledged the novelty/contribution of this paper, and promising experimental results. After rebuttal, all reviewers acknowledged that their concerns had been addressed except that Reviewer tvZF was still not very satisfactory with it but was not against acceptance of the paper. 

Reviewer tvZF acknowledged authors' effort in addressing most of his/her concerns, but one remaining concern was the unclear performance drop regarding the aerial-to-aerial (A-A) scenario. Authors provided explanation that this scenario involved minimal geometric misalignment which made their proposed alignment method less effective. However, reviewer tvZF argued that there is considerable viewpoint diversity from UAV flights. AC agreed with this argument, however, different from reviewer tvZF who concerned the general effectiveness of the proposed method, AC thought that it is reasonable for a new method not being able to handle all scenarios perfectly. There may be quite different characteristics in the A-A scenario that the proposed method does not account for. Nevertheless, AC believes that the authors have made a valuable contribution to the field, by providing a novel method which is validated to be effective in different challenging AG scenarios. It could be a remained open question for authors or other researchers to further improve the method for more broader scenarios.

Therefore, considering that all reviewers acknowledged the contribution of the paper and accepted the rebuttal, except the last concern, which was not a critical flaw, AC is happy to accept the paper for publication. Authors are required to update the rebuttal and discussion contents to the camera-ready version of the paper to improve it so as to address the raised concerns in the final paper.
Decision: Accept (poster)