Dynamic Semantic-Aware Correlation Modeling for UAV Tracking
Authors: Xinyu Zhou
Tongxin Pan
Lingyi Hong
Pinxue Guo
HaiJing Guo
Zhaoyu Chen
Kaixun Jiang
Wenqiang Zhang
Venue: NeurIPS 2025 poster ()
Keywords: UAV Tracking
Semantic Awareness
Dynamic
Primary area: applications

Abstract: UAV tracking can be widely applied in scenarios such as disaster rescue, environmental monitoring, and logistics transportation. However, existing UAV tracking methods predominantly emphasize speed and lack exploration in semantic awareness, which hinders the search region from extracting accurate localization information from the template. The limitation results in suboptimal performance under typical UAV tracking challenges such as camera motion, fast motion, and low resolution, etc. To address this issue, we propose a dynamic semantic aware correlation modeling tracking framework. The core of our framework is a Dynamic Semantic Relevance Generator, which, in combination with the correlation map from the Transformer, explore semantic relevance. The approach enhances the search region's ability to extract important information from the template, improving accuracy and robustness under the aforementioned challenges.
Additionally, to enhance the tracking speed, we design a pruning method for the proposed framework. Therefore, we present multiple model variants that achieve trade-offs between speed and accuracy, enabling flexible deployment according to the available computational resources. Experimental results validate the effectiveness of our method, achieving competitive performance on multiple UAV tracking datasets.

Dynamic Semantic-Aware Correlation Modeling for UAV Tracking
Xinyu Zhou
Tongxin Pan
Lingyi Hong'
Pinxue Guo
Haijing Guo
Zhaoyu Chen Kaixun Jiang? Wenqiang Zhang4.21
Colleege of Computer Science and Artificial Intelligence, Fudan University "oollege of Intelligent Robotics and Advanced Manufacturing, Fudan University
haapxinny220wwwgzzan | Sludan.edu.co kamipantx @ gmail.com
Abstract
UAV tracking can be widely applied in scenarios such disaster rescue, en- vironmental monitoring, and logistics transportation. However, existing UAV tracking methods predominantly emphasize speed and lack exploration semantic awareness, which hinders the search region from extracting accurate localization information from the template. The limitation results in suboptimal performance under typical UAV tracking challenges such as camera motion, fast motion, and low resolution, etc. To address this issue, we propose a dynamic semantic aware correlation modeling tracking framework. The core of our framework is Dynamic Semantie Relevance Generator, which, in combination with the correlation map from the Transformer, explore semantic relevance. The approach enhances the search region's 's ability to extract important information from the template, improv- ing accuracy and robustness under the aforementioned challenges. Additionally, to enhance the tracking speed, we design a pruning method for the proposed framework. Therefore, we present multiple model variants that achieve trade-off's between speed and accuracy, enabling flexible deployment according to the avail- able computational resources. Experimental results validate the effectiveness of our method, achieving competitive performance on multiple UAV tracking datasets. The code is available at [ttps://gthbbbcoo///yy//////////
Introduction
fundamental and important task in computer vision. In recent years, unmanned aerial vehicles (UAV) technology has rapidly developed and demonstrated broad application in various fields such as military, transportation, environmental monitoring, and disaster relief [43, However, achieve autonomous task execution in complex environments, object tracking crucial component. UAV tracking must address various challenges in dynamic scenarios.
Visual object tracking 55 55
28 59
The early UAV tracking methods [36
35 27 employed a correlation filter to enhance the discriminative capability, enabling location and tracking. However, these correlation filter-based methods lacked robust representation capabilities. Consequently, UAV tracking methods based on emerged, utilizing Siamese network enhance feature representation, significantly improving tracking accuracy. With the rise of Transformers [55] II 51) UAV tracking based on one-stream framework [37 34 32 21 have been studied.
31
convolutional neural networks (CNNs) 4 49
52
Most of the methods focus on improving the efficiency of UAV tracking, while neglecting exploration
"Equal contribution.
t Corresponniig author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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#0037
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0
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#01
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HiFT
TCTrack
AVTrack
Ours
Ground Truth
Figure 1: Comparison with SOTA methods under typical UAV challenges. The first to third rows represent camera motion, fast motion, and low resolution, respectively.
of the essence of tracking. Correlation filter-based methods and CNN-based methods rely local modeling, while Transformer-based methods utilize global modeling. All the approaches overlook the relevance between semantic features, as shown Figure I leading to suboptimal performance in UAV scenarios, such as camera motion, fast motion, and low resolution, etc.
To address this issue, we propose dynamic semantic-aware correlation modeling method and develop UAV tracking framework based on the approach, named DSATrack. Specifically, we use the search region and template&patches semantic matching, semantic awareness, and feature extraction and fusion. Importantly, we design a Dynamic Semantic Relevance Generator within the Transformer dynamically achieve Semantic Aware Modeling. By leveraging the Semantic Aware Modeling to fuse similar semantic features, our method alleviates semantic ambiguities. The semantic-aware correlation map further facilitates the search region in extracting representative features from the template&patchir the perception and localization capability of UAV tracking and mitigating tracking drift. Finally, to improve the tracking efficiency, we design a pruning technique tailored to the characteristics of DSATrack. By selectively pruning Transformer blocks, retain the tracking precision to the greatest extent while achieving high speed tracking. We provides multiple variants for different UAV Tracking application scenarios. For instance, offline tasks such as aerial video prioritize tracking accuracy, while real-time applications like autonomous target following demand fast response times.
inputs and employ Transformer blocks to achieve
thereby enhancing
Our contributions be summarized as follows.
We propose a dynamic semantic-aware UAV tracking framework, DSATrack, which utilizes Dynamic Semantic Relevance Generator to produce semantic-aware correlation map. These maps used to fuse features with similar semantic features, effectively reducing tracking drift.
To further optimize DSATrack, we design a pruning technique that selectively removes Transformer blocks with minimal impact on accuracy. This approach achieves a balance between precision and speed, enabling flexible deployment according to the available computational resources.
We conduct extensive experiments, demonstrating that DSATrack achieves state-of-the-art perfor- mance. Additionally, ablation studies validate the effectiveness of the proposed methods.
Related Works
2.1 UAV Tracking
Correlation filter-aseed methods, such as KCF (25 and DSST 13,
were among the earliest applied to UAV tracking due to their high efficiency, achieving strong performance in speed and accuracy.

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However, they struggle under camera motion or fast motion, which are common in UAV scenarios.
With the rise of deep learning, Siamese network-bar based trackers- -ucrssssuchhaaaa as TransT 9 AiATrack [9]
STARK
53 ToMP [4]), and KeepTrack [42 have significantly improved tracking robustness.
TCTrack 4 further adapts Siamese models to UAV tracking. Yet, their heavy computational demands
hinder real-time UAV deployment. Recently, one-stream Transformer frameworks [55] 11
g 6 (54]
20 have emerged, offering enhanced modeling of long-range dependencies with improved efficiency.
Trackers like TATrack [33] and AVTrack (37] utilize this framework to better capture interactions
between template and search regions under UAV-specific challenges. Although LoReTrrakk [4] is not tailored for UAV tracking and lacks real-world UAV evaluation, it demonstrates promising results on a UAV dataset using a lightweight ote-stream design. In contrast, we propose a Dynamic Semantic-Awer Correlation Modeling approach within a one-stream Transformer framework to address key UAV challenges such as camera motion, fast motion, and low resolution.
2.2 Semantic Correspondence
In visual perception tasks, semantic correspondence is typically categorized into two types: explicit correspondence and implicit correspondence. Explicit correspondence involves semantic-aware modeling through supervised loss functions, while implicit correspondence does not rely direct supervision for semantic correspondence. Explicit correspondence methods, such as contrastive learning 77 24. Triplet Loss [46]. and InfoNCE [45] supervise the semantic relationships between images or pixels by pulling closer those belonging to the same category and pushing apart those of different categories. This enables models to learn semantic relationships, improving the accuracy of On the other hand, implicit correspondence is commonly employed in detection [6]]. segmentation [10] and Tracking [ using Transformer-
downstream tasks [2 [58
tasks like classification
40
15 These architectures implicitly learn semantic relationships among image features through task-specific losses. Specifically, Transformers compute a correlation map using queries and keys [3] 47). where the correlation map represents the semantic similarity between features (12). However, in implicit semantic correspondence methods, there limited exploration into explicitly optimizing semantic correspondence, which often results in suboptimal modeling of the correlation map. This lack of optimization can hinder the model's ability to capture semantic relationships. Therefore, we propose dynamic semantic-aware correlation modeling method for UAV tracking to enhance semantic perception capabilities, improving tracking accuracy.
based architectures 488
3 Method
In this section, we will first present our proposed tracking framework, DSATrack. Next, will describe the Dynamic Semantic-aware Transformer. Following that, we will detail the pruning method designed specifically for DSATrack. Finally, will present description of the prediction head.
3.1 Overall Framework
The overall framework of the proposed DSATrack shown in Figure The entire tracking framework consists of three components: input representation, feature matching and interaction, and prediction.
Input Representation. The input of DSATrack is the Template & Patches H, xW, and Search Region E H, where H and W represent the height and width of the Template and Search Region. The Template Patches consist of fixed template R I xW: and several patchese (nn-1)xH,,X, that exhibit representative object characteristics. The Patches are generated and updated over time steps in exactly the same manner as RFGM [60]. Subsequently, the inputs are projected into tokens through a Patch Embedding. Feature matching and Interaction. The generated Template Tokens T3 E 22xxx,, Xws, and Search Tokens T, E xxxx3x Xw, are then fed into the Transformer Blocks for feature matching and interaction, where h and w represent the height and width of the features after 16x downsampling. The specific computation method of the Transformer block is consistent with OSTrack (55). Importantly, we propose Dynamic Semantic- Transformer for semantic matching and feature fusion. Specifically, we designed a Dynamic Semantic Relevance Generator to construct Semantic Relevance. Using the Semantic Aware Modeling, we fuse the similarities of features with similar semantics to obtain an optimized Semantic-Aware Correlation Map. Based on this correlation map, unimportant template tokens are discarded. The filtered Template Tokens, Search Tokens, and the Semantic-Awen Correlation Map
3

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are then fed into a Hybrid Attention module to facilitate feature interaction. Prediction. Finally, the Search Tokens are reassembled into an image and passed the prediction head, which predicts the bounding box of the object.
Dysamic Sempaticanan Tranduemer black
Tamplate
Ceneearrnn Mack
UAA
Templaac & Pacho
Patch Eaatedling
aeeedemo Lune
Predicdio Heal
Complasoe Mae
Semmati
Semartie Cunclatio Map
Dpummic Samort Somenar
Relevance Generator
Dynamic Semantic
Modeling
MIP
Hobeid
Search Region
Token Dioepina
Scarc
Search
Toicor
+0
Scasstic Relevune
Filtered Templte Tokes
Dynomic Semoatic Helevance Generntor
Hybrid Alluation
Crus Aftention
Carcalonnaa
MLLP
G
Conretatio Map (N0,
(9 R.)
Og
Figure 2: The framework of DSATrack. The figure above illustrates the overall pipeline of DSATrack, while the two figures below provide detailed explanation of the Dynamic Semantic Relevance Generator and the Hybrid Attention, respectively.
3.2 Dynamic Semantic-aware Transformer
The Template Tokens Tg E xgxxhxxxxde xds and Search Tokens Ta E Rlx are inputs of Dynamic Semantic-aware Transformer, where dg is the dimensionality of the features. For simplicity and clarity, we present the details using single template, T'2 E RR X We use linear layer to transform the Search Tokens and Template Tokens into (Qn X2, and (K₃
Xw,
As shown in Figure 2 we compute the Correlation Map C' between the Q2 and K2 as:
Q Vdle
C.z
(1)
where Czn captures the correlation scores between query from and key from corresponding to the z-to-r similarity. represents the number heads in the Transformer.
In the Correlation Map C.., the modeling of the similarity relationship between points on Q₂ and points on K2 can easily lead ambiguity. We employ a dynamic perception -aaeedd prediction approach to calculate and fuse the similarity between semantically related points on K₂ and Qu-
Dynamic Semantic Relevance Generator. We first reshape C.u E
xh, xx,xt into C.n RN, XN.X xi where N₂ = hgg x we and N = hg x Wgg Then, We propose a Semantic Aware {ā E Czz, S N₂ represents the of nodes and the shape of C is N₂ x 1 x I. In other words, we retrieve nodes along the second dimension of Czz- {(u, v) Vu E V, Vo V} represents the
Modeling G v,& E), where
Semantic Relevance. Specifically, apply average pooling to the node features and we concatenate the vector o; to form T:
T T E RlIxll CO.
(2)
8 [ og .... CN. N.XII
(3)
where [:] represents concatenation. Subsequently, we compute the similarity score between nodes via matrix multiplication:
A
(T) (7) A R N.NNN
(4)

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After that, a tiny prediction network consisting of multi-layer (erceptron(MLPP) and Log- Sog-Softmax function is utilized predict the possibility that each Relevance will be retained and discarded:
exp (MLP (At)) E ER? 1 iij No.
JJ
log
exp (MLP (A.+))
where Tyjo is the probability retaining the Relevance (i,3) and Tzj.1 is probability of removing
Next, we apply the Gumbel-Softme to produce the Dynamic Semantic Relevance &, making the differentiable and retaining the most relevant connections:
Gumbel Softmax (T) {0,1] E RNNNN
(6)
Semantic Aware Modeling. Therefore, we utilize the Semantic Aware Modeling to optimize the Correlation Map, as defined by the following formula:
C²2- & & C W, E N.
where E = & + T, is the identity matrix, and A = E, &ij is the degree matrix.
Hybrid Attention. The Semantic-aware Correlation Map is fed into an MLP and cumbel-softtaal to evaluate the importance of the Template Tokens. Less significant tokens are discarded based on M {0, 1} E N=NN to accelerate computation:
= Gumbel - Softmax (Log Softmax(MLP((....
(8)
Subsequently, we combine Q., Qz Kg, Kg, V2, Vgs and C²,, compute the Hybrid Attention:
HALtention K2 Va, K2, Vv2) [SelfAttention( Q,, K V₂), SclfAttention(Q.. K2, V2).
(9)
CrossfAttention/ Qz. K2, V=)].
The formula for Self-attention is as follows:
SelfAttentionl (Qn K) = SoftMax QKT
)V. Vdk.
(10)
Cross-Attention can be calculated as follows:
CrossAllention()) K2, Vz) SoftMax V2.)
(11)
Finally, incorporated a Feedforward Neural Network (FFN) to enhance the model's fitting ability. The Dynamic Semantic-aware transformer can be summarized as follows:
T- =T= FFN(T
(12)
T- HAttention(Qu, Kr, Ve, K2, Vz) Tzz: where Tas represents the concatenation of T., and Tz.
(13)
3.3 Hierarchical Contribution Ranking Pruning
We introduce the hierarchical contribution metric &; which quantifies the role of each Transformer block the feature transformation process DSATrack. The definition of hierarchical contribution is based on the similarity between the hidden state of each layer and its successor. If the input of layer has high similarity with the input of the next layer, it indicates that the feature transformation of this layer has minimal effect on the subsequent layer's 's input, resulting in lower contribution. Conversely, the similarity is low, it suggests that this layer plays a significant role.
The calculation formula for the hierarchical contribution 6, of the i-th Transformer layer is as follows:
-
M Σ (T, T 1.
1 M
Σ [Tem
Tem T 1.m
(14)
ll2 [|Te11 ml
where Tem is the m-th hidden state of i-th Transformer block, and M is the number of hidden states.
5

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The model consists of 12 Transformer blocks, with two types: Dynamic Semantic-Awar Transformer blocks located layers = (4. 7. 10), and Standard Transformer blocks in the remaining layers S (1,2,3, 5, 6, 8, 9. 11, 12). For the Dynamic Semantic-Awe Aware Transformer blocks and the Standard Transformer blocks, we first sort the contribution scores & separately for each type of block:
CD - (o D)), Cs Sort((;; | E S)).
(15)
Based these sorted values, select the lowest contributing blocks for pruning. The pruning sets for each type of block are determined by pruning ratios pa and Ps as follows:
D prine = { i is the lowest [pa- [D]] layers CD), Sprune (i i is the lowest [p= S|yeeye in Cs}.
(16)
These pruned blocks are then discarded, and the remaining blocks from both sets are combined to form the new pruned model. The remaining Dynamic Semantic-Aware Transformer blocks and Standard Transformer blocks are:
Dnew \ D Prune:
Snew = S Sprane
(17)
Finally, the new model's layer set after pruning is:
Luew Dnew U Spew-
(18)
This process allows for balance between model efficiency and performance, with the pruning ratio Pa and p. adjustable to optimize computational speed and storage requirements.
3.4 Prediction Head
Following OSTrack (55. we construct our prediction head to estimate the bounding box from the transformeer-ennrrated search region features. The head consists of three branches: one predicts the classification score map S E [0, 1| x== another predicts the offset O E [0, 1)2xk= xw,, and the third predicts the normalized box size E E [0, 1]2xh, xws, The location with the highest score, (Ira: ya) arg (2,92 Szy, is combined with O and E to generate the final bounding box.
x = ad - 0 Fzi ya), = Ya + O va, Ma), w E00, xdi Va), h = E(1, Idi Va).
(19)
Table 1:
The hierarchical contribution of DSATrack-ViTT ViT-B transformer layers on UAV123 benchmark.
Dataset
Layer 2
Layer
Layer
Layer
Layer
Layer 7
Layer
Layer
Layer
Layer
Layer 12
UAV123
0.1325
0.0739
0.0615
0.0581
0.0429
0.0457
0.0438
0.0358
0.0428
0.0657
0.1444
Table 2: Setup of removed layers for the prumed variants.
Variant
Removed Layers
Variant
Removed Layers
DSATrack-DD -D8 DSATrack-D7
6, 7, 9, 10
DSATrack-Da DSATrack-D4
5, 6, 7, 8, 9, 10 6, 8, 9, 10, 11
6, 7, 8, 9, 10
4 Experiments
4.1 Implementation Details
Model. DSATrack employs the VIT-B 15 as the backbone, initialized with MAE 23 referred as DSATrack- VIT-B. For the pruned variants of DSATrack, we consider four configurations with 8, 7, 6. and 4 transformer layers, named DSATrack-D8, DSATrack-D7, DSATrack-D6. and DSATrack-D4. All pruned models apply the pruning ratio Pa = The pruning ratios p. are set to 2 3 g. g* and 6 for the four variants. Table Presents the hierarchical contribution of DSATrack- VIT-B transformer layers on UAV123, yielding the layer removal setup for pruned model variant, as shown in Table In addition, we provide versions based on ViT-Tiny and DeiT-Tiny, named DSATrack- ViT-T and DSATrack-Deii DeiT-T. Templates resized to 128 128, while Search Region resized to 256 x 256.
3.

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Training. We train the model on TrackingNet [44] GOT-10k (26.. LaSOT 177, and COCO [39. The training process consists of two stages. In the first stage, the model is trained for 300 epochs with 3 templates. The learning rate for the Dynamic Semantic-aware Transformer and prediction head set to 4 x 10-1, the learning rate for the remaining parameters is set to x 10-5. At epoch 240, the learning rate is decayed by factor of 10. In the second stage, the model is finetuned for 50 epochs with templates. Adam optimizer is used with a weight decay of 10-4, 4, The loss function comprises the LI loss, Focal loss, GloU loss and ratio loss, consistent with RFGM [60]. All training tasks are conducted on 4 NVIDIA Geforce RTX 3090 GPUs with a batch size of 24.
Inference. For fairess,, the speed tests of other models and our model are conducted on the same NVIDIA RTX 3090 GPU without data loading overhead. The size of our Templates & Patches is set to 3 x N₁. The update interval of Patches is set to 5 for t S 100, doubled every 100 frames until t 500, and then remains 160. The interval doubles every 100 frames until it reaches 160, where remains fixed beyond 160 frames. At the 4th, 7th, and 10th layers, the number of retained template
tokens set to [3 x N₂ 0.9]. [3 x N₂ x 0.8), and [3 x N₂ x 0.7.. [-] denotes the floor operation.
4.2 State-of-the-Art Comparisons
Figure D presents a comparison of DS speed and accuracy against the SOTA methods.
Premikes OFP Crrama Metoe
Procuin plate of OPE. Fast Moliee
Preciies phto of OPS Lan Hoxdatee
Ta
DR PrctooLE
ArTha A1w--37777
D4
5.5% 11Thwa
RLOP
DhS RA17
12
UAVI23
Precidas ofPE Creama Hodke
Prodikon plans
Fast Mudion
Preciáas pits of OPS. Las Reuisties
101
Localon
(b) VaDion=2018
Figure 3: Comparison of state-of-the-eee methods under representative UAV challenges.
DTB70. DSATrack outperformed SOTA methods. Specifically, DSATrack -EAT--ck-Vii-B attained the highest precision (91.2%) and success (70.6%). Notably, even the lightweight variants, DSATrack-DS and DSATrack-D7, achieved second (89.4% precision) and third place (88. % precision). demonstrating the effectiveness of our method across different model complexities. DSATrack- VIT-T acquired 86.6% in Precison and 17.4% % in on success, which is outperform the sota method by 2% to 3%. The results highlight the robustness of dynamic semantic- correlation modeling in UAV tracking.
UAVDT. AT VIT-B achieved SOTA results with precision and success of 88.1 1 1% and 66.3%. ATrckak also achieved competitive performance, ranking second in precision (85.7%) and achieving the third-best success (63.4%). Importantly, DSATrack-DD exhibited strong success metrics (62.1%), outperforming prior works such as TATrack and AVTrack. Besides, DSATrack-Vin VTT-T and DSATrack-DeiT- achieved better performance than SOTA methods, such as AVTrack and LightFC These results underscore the scalability of DSATrack across varying depths and complexities.
VisDrone2018. DSATrack-D8 and DSATrack- -iTTrack-V---BBB- achieving the top two rankings for success, 69.1% and 67.0%. D7, ViT-T and DeÎT-T achieved competitive results, validating the efficacy of our framework challenging conditions. Compared to existing SOTA methods, DSATrack consistently presented superior semantic-aware modeling, reflecting its adaptability to UAV scenarios. Moreover, the second row of the Figure 3 demonstrates that the pruned version of ATrack also exhibits significant advantages in addressing the representative challenges of UAV tracking.
7

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Table 3: Comparisons with SOTA methods UAV tracking datasets.
DTB70
UAVDT
VisDrone2018
UAV123
Method
Source
Prec.
Suce.
Proc.
Succ.
Proc.
Suce.
Proc.
Succ.
SiamAPN 18
ICRA21
78.4
58.5
71.1
51.7
81.5
58.5
76.5
57.5
SiamAPN++
IROS21
78.9
59.4
76.9
55.6
73.5
53.2
76.8
58.2
ICCV21
80.2
59.4
65.2
47.5
71.9
52.6
78.7
58.9
P-
ICME22
80.3
60.4
80.7
56.6
80.1
58.5
48.9
TCTrack
CVPR22
81.2
62.2
72.5
53.0
79.9
59.4
80.0
60.4
55
CVPR22
80.6
61.8
80.1
59.2
81.6
61.9
76.1
59.0
ABDNet
RAL.23
76.8
59.6
75.5
55.3
75.0
57.2
79.3
60.7
DRCI
ICME23
81.4
61.8
84.0
59.0
83.4
60.0
HiT
ICCV23
75.1
59.2
62.3
47.1
74.8
58.7
82.5
63.3
DDCTrack 16
ICPR24
79.0
51.1
81.2
48.7
79.1
50.1
SMAT 21
WACV24
81.9
63.8
80.8
58.7
82.5
63.4
81.8
64.6
LitcTrack 50
ICRA24
82.5
63.9
81.6
59.3
79.7
61.4
84.2
65.9
LightFC-ViT
KBS 24
82.8
64.0
83.4
60.6
82.7
62.8
84.2
65.5
AVTrack-Vis 37
ICM1.24
81,3
63.3
79.9
57.7
86.4
65.9
84.0
66.2
AVTrack-DeiT kY
ICML.24
84.3
65.0
82.1
58.7
85.9
65.4
84.8
66.8
DSATrack-ViTS
Ours
91.2
70.6
88.1
66.3
87.1
67.0
90.2
69.4
DSATrack-D8
Ours
89.3
69.5
63.4
90.8
69.1
67.0
DSATrack-D7
Ours
88.1
68.4
84.6
62.1
88.6
68.0
87.3
66.6
DSATrack-D6
Ours
86.6
67.3
83.5
60.6
87.0
65.7
86.9
66.0
DSATrack-D4
Ours
84.5
65.9
82.3
58.9
84.8
64.1
84.1
63.0
DSATrack-ViTS
Ours
86.6
67.4
84.3
61.6
84.8
64.1
85.5
65.5
DSATrack-DeiT--
Ours
84,5
65.7
85.0
62.0
82.0
60.8
84.4
64.5
UAV123. DSATrack- VIT-B set new performance of 90.2% (precsion) and of 69.4% (success), surpassing existing SOTA trackers. DSATrack-D8/D7 maintaining competitive scores of 87.6% (precision) and 87.3% (precision). Furthermore, D4, VIT-T and DeiT-T achieved satisfactory accuracy, making it suitable for real-time UAV applications. As shown in the first row of the Figure DSATrack exhibits better performance addressing challenges such as camera motion, fast motion, and low resolution. This demonstrates the flexibility of DSATrack in balancing efficiency and accuracy.
4.3
Ablation Study on Correlation Modeling
Table 4: Ablation study on correlation modeling. The best results are highlighted in bold.
DTB70
UAVITT
VisDronc2018
UAV123
Variant
Method
Prec.
Suec.
Proc.
Suec.
Prec:
Suoc.
Prec.
Suce.
Buseline
87.7
67.9
85.0
63.3
86.5
65.7
87.3
67.2
DSATrack VIT-B
Buseline-SSAM
DaselineDSAAM
91.2
70.6
$8.1
66.3
87.1
67.0
90.2
69.4
Table Comparisons of Hierarchical Contribution Ranking Pruning and Sequential Pruning.
UAVDT
VisDronc2018
UAV123
Backhone
Method
FPS
Sequential Pruning
162.1
D7
Contribution Ranking Praming
88.1
68.4
84.6
88.6
68.0
87.3
66.6
162.4
Sequential Pruning
D4
Contribution Ranking Praming
84.5
65.9
82.3
84.8
64.1
$4.1
63.0
214.1

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bar
Precision vs Speed
101
80
Precision
80
DSATTick D77
Fus Matee
USSKTrock
00-
ATrad-DD
DS. D6.TTi VIT-I
120
140
100
180
2001
223
Speed (FPS)
(1a)
(1b)
(1c)
(2k)
(2b)
(2c)
Figure 4: Visualization of correlation map (a) search region. (b) correlation map before the
Figure 5: A comparison of speed and accuracy with state-of-the-an methods. The performance is the average across four datasets.
DSAM. (c) correlation map after the DSAM.
We evaluate our Dynamic Semantic-Aware Modeling (DSAM) against the Baseline and Static Semantic-Aware Modeling (SSAM). Dynamic semantic modeling refers to the dynamic semantic prediction method proposed in this paper, whereas static semantic modeling denotes the approach that applics 3 convolution kernel to the correlation maps from the search region to the template and from the template the scarch region, respectively. In this case, the receptive field remains fixed in shape. On DTB70, DSAM with ViT-B achieves 91.% precision and 70.6% success, outperforming both alternatives. Similar trends are observed UAVDT and UAV123. Unlike SSAM's fixed- region modeling, DSAM adaptively captures semantic variability, enhancing tracking robustness. As shown in Figure DSAM yields more focused correlation maps, effectively reducing background noise. These results confirm that DSAM significantly improves UAV tracking by modeling dynamic semantic correlations more accurately than static baseline approaches.
4.4 Ablation Study on Pruning
Sequential Pruning (SP), similar to the proposed Contribution Ranking Pruning (CRP), removes layers 4 and 7 while retaining layer 10. However, SP performs pruning in fixed sequential order, without explicitly evaluating the relative contribution of each layer to the overall tracking performance. In contrast, CRP introduces hierarchical contribution ranking mechanism that quantifies each layer's importance before pruning, ensuring that critical layers are preserved while redundant ones are removed.
As shown in Table 5 CRP consistently outperforms across all backbones and datasets. For the D7 variant, CRP achieves higher precision and success rates on all four benchmarks (DTB70, UAVDT, VisDrone2018, and UAV UAV123) with negligible changes in speed. The performance gap becomes even more pronounced in shallower configurations such D4, where SP suffers from severe accuracy degradation due to over-pruning essential layers, while CRP maintains stable accuracy with similar inference speed (214.1 FPS 210.7 FPS).
These results demonstrate that contribution-awee aware layer selection enables CRP to achieve better balance between accuracy and computational efficiency. By adaptively ranking layers based on their hierarchical importance rather than following a rigid pruning schedule, CRP achieves more robust pruning strategy, especially on challenging datasets such as VisDrone201 8 and UAV123, where fine-grained spatial details are crucial for tracking performance.
Real Word UAV Test
As illustrated in F conduct series of real-world UAV tracking experiments to further verify the practicality and deployment feasibility of the proposed DSATrack framework. Four representative tracking scenarios presented, involving diverse targets such a person, covering both rigid and non-rigid motion patterns as well as varying motion speeds and background complexities. These experiments computing platform to emulate realistic onboard conditions with limited computational resources.
motorbike, dog, and
performed using Jetson AGX Xavier edge
To assess the runtime performance, we evaluate the inference speed of different ASATrack variants on the Xavier platform. The lightweight transformer-basee variants, DSATrack- VTT-T and ATrara De achieve real-time tracking speeds of 27.6 FPS and 27.4 4 FPS, respectively, demonstrating their

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suitability for low-latency applications embedded hardware. In contrast, larger pruned variants such DSATrack-D4 and DSATrack-D") run approximately 10 FPS, offering stronger accuracy margin when higher computational capacity is available.
These results highlight the adaptability of DSATrack diverse deployment scenarios. Depending the trade-off between accuracy and efficiency, users can flexibly select the appropriate model variant meet the demands of specific UAV task. The consistent performance across both dynamic and static scenes further confirms the robustness and scalability of the proposed framework in real-works UAV tracking applications.
Dog
Dog
Deg
Figure 6: The real-world UAV tracking test includes tracking car, motorcycle, dog, and person.
6 Conclusion and Limitation
The paper presents DSATrack, a dynamic semantic-aware UAV tracking framework addressing typical UAV Tracking challenges. It incorporates Dynamic Semantic Relevance Generator enhance feature fusion and localization, effectively reducing tracking drift. Additionally, a Hierarchical Contribution Ranking Pruning optimizes DSATrack by balancing tracking precision and real-time. Extensive experiments show that DSATrack achieves SOTA performance across various datasets, while ablation studies further validate the effectiveness. DSATrack also has certain limitations. Similar to most trackers, DSATrack is still local tracker and lacks re-detection capability once the object is lost. In future work, we will explore global UAV tracking algorithms.
Acknowledgement This work was supported by National Natural Science Foundation of China (No.62576109, 62072112.
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A Technical Appendices and Supplementary Material
A.1 Ablation Study on Correlation Modeling on attributes of UAV123.
Table 6: Ablation Study on Correlation Modeling on attributes UAV12 123 including Camera Motion, Fast Motion and Low Resolution. Baseline refers to standard Transformer. SSAM represents the static semantic-aware modeling, while DSAM denotes the proposed dynamic semantic-aware modeling. The best results highlighted in bold.
Camera Motion
Tast Motion
Low Resolution
Overall
Variant
Method
Prec.
Suce.
Suce.
Suce.
Baseline
90.9
69.9
84.0
64.2
74.2
50.5
87.3
DSATrack-VT-B
Baseline-SSAM
90.6
69.9
84.3
64.5
80.6
55.1
88.8
68.5
Basclinc+DSAM
93.5
72.2
89.8
68.6
81.8
55.9
90.2
69.4
As shown in Table 6, the ablation study demonstrates the effectiveness of Dynamic Semantic- Aware Modeling (DSAM) in enhancing tracking performance across various challenging attributes. Compared to the baseline Transformer, DSAM consistently improves both precision and success rates all evaluated scenarios. Specifically, for DSATrack- ViT-B, DSAM outperforms the baseline by 2.6% and 2.3% in Camera Motion, 5.8% and 4.4% in Fast Motion, and 7.6% and 5,4% in Low Resolution. This suggests that DSAM effectively captures evolving target representations under complex UAV tracking conditions, where static modeling may struggle to adapt to rapid environmental changes. The performance gap is particularly evident in more challenging scenarios, such as Fast Motion and Low Resolution, where DSAM significantly enhances tracking accuracy and robustness.
To investigate the effect of different template input formats, we compare two variants under the ViT-D8 backbone: using only the template image (A Template) and combining the template with patch embeddings Patchess As shown in Table g incorporating patch-level information consistently improves performance across all UAV tracking benchmarks. Specifically, the Tem- plate&Patches design achieves the best overall results, with a precision/success gain of +1.6/+1..6 on UAVDT and +2.3/+1.7 on VisDronc2018 compared to the single-templen input. These results demonstrate that introducing fine-graen aneeggriied patch features enhances the representation of template cues, leading to more robust matching and improved generalization across diverse UAV tracking scenarios
Table 7: Ablation study on the input format of the Template. The best results are highlighted in bold.
DTB70
UAVDT
VisDronc2018
UAV123
Backbone
Method
Prec.
Suce.
Prec.
Suce.
Prec.
Succ.
Prec.
Template
89.5
69.3
85.0
61.8
88.5
67.4
86.4
65.7
VIT-D8
Putthhste&Pttchee
89.3
69.5
85.2
63.4
90.8
69.1
87.6
67.0
A.2
Ablation study on Token Elimination.
Table 8: Ablation study on token elimination. The best results are highlighted in bold.
DTB70
UAVDT
VisDromc2018
Backhone
Method
FPS
Suce.
Prec.
wlo token climination
90.6
69.8
87.3
65.7
86.7
66.3
89.5
67.8
105.6
ViT-Base
w/ tokes elimination
91.2
70.6
88.1
66.3
87.1
67.0
90.2
69.4
111.7
To evaluate the effectiveness of token elimination, we conduct an ablation study across four UAV tracking benchmarks: DTB70, UAVDT, VisDrone2018, 8, and UAV123. As shown in Table incor- porating token elimination consistently improves both tracking precision and success rate across all datasets, demonstrating the robustness and generalizability of the approach. For the ViT-Base backbone, token elimination yields consistent improvements over the baseline that retains all tokens. On DTB70, it achieves gain of 0.6% in precision and 0.8% in success rate. Similar improvements
15

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Table 9: Comparison with recent transformerrbasel SOT trackers. AVisT reports /p TrackingNet and LaSOT report AUC/P GOT-10k reports s " denotes not reported.
AVisT OP
TrackingNet Porm
LaSOT
GOT-IOK
Method
AUC
OP75
AUC
AUC
Frrm
SR..5
SRo.75
SwinTrack
81.1
78.4
67.2
70.8
47.6
71.3
81.9
64.5
MixforerrV2
83.4
88.1
81.6
70.6
80.8
76.2
Asym/Track
80.0
84.5
77.4
67.7
76.6
61.4
64.7
73.0
67.8
LoRAT-B-224
83.5
87.9
82.1
71.7
80.9
77.3
72.1
81.8
70.7
2rack2a222
82.9
81.4
70.3
76.2
73.5
84.0
70.4
SeyTrack
56.8
66.8
45.6
83.3
88.3
82.2
69.9
79.7
76.3
74.7
84.7
71.8
AQATrack
83.8
88.6
83.1
71.4
81.9
78.6
73.8
83.2
72.1
OSTrack
54.2
63.2
42.2
83.1
87.8
82.0
69.1
78.7
75.2
71.0
80.4
68.2
DSATrack-Vis ViT-B (Ours)
60.2
69.1
50.2
84.1
88.6
82.7
69.4
78.6
74.6
75.0
85.6
73.7
Table 10: Comparison across four UAV tracking benchmarks and efficiency metries, Pre.: precision; Suc.: success; FLOPs measured in GigaFLOPs (G).
Method
DTB70
UAYDT
VisDrone2018
CAV123 Pre.
FLOPs(G)
Purams(M)
Xavier(PPS)
RTX3030FPS)
Suc.
Pre.
Suc.
Pre.
Suc.
Suc.
HiT
62.3
47.1
74.8
58.7
82.5
63.3
4.35
42.14
159.0
VVTrack-VIT
81.3
63.3
79.9
57.7
86.4
65.9
84.0
66.2
1.82
6.20
30.0
184.8
LiteTrack
82.5
63.9
81.6
59.3
79.7
61.4
84.2
65.9
12.81
49.59
15.9
160.1
DSATrack-D7
88.1
68.4
84.6
62.1
88.6
68.0
87.3
66.6
23.64
56.76
14.0
162,4
DSATrack-D4
84.5
65.9
82.3
58.9
84.8
64.1
84.1
63.0
14.12
35.50
17.4
214.1
DSATrack VIT-
61.6
64.1
65.5
2.99
SSATrack-DeiTS
84.5
65.7
85.0
62.0
82.0
60.8
84.4
64.5
2.99
8.21
27.4
114.4
observed UAVDT (+0.8 precision, +0.6 success), VisDrone2018 (+0.4 precision, +0.7 success), and UAV12 123 (+0.7 precision, +1.6 success). These gains are particularly significant under challenging scenarios such camera motion, small object size, and background clutter, which common in UAV tracking.
Beyond accuracy, token elimination also contributes to runtime efficiency. For instance, with the ViT-Base backbone, the frame rate increases from 105.6 FPS to 111.7 FPS, reflecting reduced computational overhead without sacrificing performance. This efficiency gain makes the model more suitable for real-time UAV deployment, especially on edge computing platforms with limited resources. Overall, the results validate that token elimination effectively filters out redundant or uninformative tokens, allowing the model to focus on semantically relevant features.
A.3 Comparison with General Trackers
Table presents a comprehensive comparison between DSATrack and recent transformer-basse single- object trackers across four large-scale benchmarks: AVisT, TrackingNet, LaSOT, and GOT-10k. On AVisT, DSATrack- ViT-B achieves competitve overall performance, obtaining an AUC/OP50/0P75 of 60.2/69. 1/50.2, demonstrating superior robustness under adverse visibility and occlusion. On Track- ingNet, DSATrack attains the highest AUC 84.1 and Pmor of 88.6, showing strong generalization large-scale, high-diversity scenes. For LaSOT, DSATrack remains competitive with AUC of 82.7 and precision of 74.6, comparable to the top-performing AQATrack while requiring no task-specific tuning. On GOT-10k, DSATrack delivers the best accuracy across all metrics (AO/SRO.5/SR0.75 75.0/85.6/73.7), surpassing previous transformer-based trackers such as OSTrack and LoRAT-B-224.
These results confirm that DSATrack maintains favorable balance between tracking precision and robustness, matching state-of-the-art transformer trackers across both long-term and general-purpose benchmarks. Its consistent advantage stricter metries (OP75, SR0.75) highlights the model's improved localization accuracy and resilience in challenging conditions.
A.4 Comparison with UAV Trackers
Table T0 compares DSATrack with recent transformer-based UAV trackers in terms of accuracy and efficiency across four representative UAV benchmarks- DTB70, UAVDT, VisDronc2018. and
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UAV123 well as computational complexity metrics. Among all competitors, DSATrack-D7 achieves the best overall accuracy, attaining the highest precision and success scores on every dataset (DTB70: 88. 1/68.4, UAVDT: 84.6/62.1, VisDronee018 8: 88.6/68.0 O, UAV123: 87.3/66.6). This indicates that the hierarchical contribution- pruning and dynamic alignment strategy effectively preserve discriminative representations across diverse UAV scenarios.
Meanwhile, DSATrack-D4 maintains competitive accuracy while significandly improving efficiency, achieving the highest inference speed of 214.1 FPS on RTX 3090 and 17.4 FPS on NVIDIA Xavier, demonstrating excellent scalability for real-time embedded deployment. The lightweight variants DSATrack- TTT--cck-ViT-T and DSATrack-Dees DeiT-T further reduce parameters and FLOPs (8.21 M / 2.99 G) while sustaining robust tracking performance, highlighting the flexibility of the proposed framework in balancing accuracy and efficiency across different model scales and hardware settings.
Overall, DSATrack exhibits favorable accuracy-effiiinng trade-off, outperforming existing UAV- specific trackers in both precision and speed, thereby underscoring its practicality for onboard UAV perception applications.
A.5 Broader Impact
The proposed UAV tracking method could benefit public safety and disaster monitoring by enabling real-time, robust tracking from acrial views. However, also acknowledge the potential for misuse in surveillance applications and stress the importance of deploying such technology within clear legal and ethical boundaries.
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dog, and person, demonstrating the adaptability of our method in diverse environments.
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Figure 9: Real-world UAV tracking failure cases on four representative scenarios, including car, motorcycle, dog, and person, demonstrating the adaptability of our method in diverse environments.
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Reviews:
[{"review_id": "lXr1W0ooUS", "reviewer": "Reviewer_gMhJ", "summary": "This paper presents DSATrack, a UAV tracking framework featuring a Dynamic Semantic Relevance Generator to improve semantic correlation modeling. A Transformer pruning strategy is also proposed to balance speed and accuracy. Experiments demonstrate state-of-the-art performance across multiple benchmarks.", "questions": "- How generalizable is DSATrack across different UAV tracking scenarios?\n\n- Can the semantic-aware correlation maps be visualized to better demonstrate the interpretability of the method?\n- More issues are shown in the weakness above.", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 2, "strengths_and_weaknesses": "### Strengths\n- The paper identifies an important gap in current UAV tracking methods—the lack of semantic awareness in correlation modeling, which is well-justified.\n\n- By introducing a pruning-based variant generation, the method can flexibly adapt to different computational constraints (e.g., real-time vs. offline UAV applications).\n\n- Extensive experimental results and ablation studies are conducted, proving the effectiveness of each proposed module.\n\n### Weaknesses\n\n- The details of the Dynamic Semantic Relevance Generator are insufficiently elaborated—how exactly it computes relevance, and how it integrates with the Transformer pipeline should be clarified.\n\n- While pruning is introduced for speed-up, the comparison with existing efficient tracking baselines (e.g., lightweight Transformers or mobile-friendly trackers) is not reported.\n\n- It's unclear how robust DSATrack is to extreme low-resolution or partial occlusion cases, which are critical for UAV applications.", "was_revised": true, "final_justification": "Thanks for your detailed response. I'll raise the rating, as my concerns have been resolved.", "rebuttal": "Thank you for your valuable suggestions. **During the rebuttal period, we have made significant efforts to address your concerns.**. Additionally, **we have provided detailed responses to your other questions. We sincerely hope that you will reconsider our paper**.\n\n>***Q1**. While pruning is introduced for speed-up, the comparison with existing efficient tracking baselines (e.g., lightweight Transformers or mobile-friendly trackers) is not reported.*\n- **Thank you for your valuable suggestion**. **We would like to clarify that all the tracking methods presented in our work are lightweight and efficient trackers**, as our target application is UAV tracking, which is typically constrained by limited onboard computational resources. Therefore, we focused exclusively on reporting lightweight tracking methods in our comparisons.\n- **To facilitate understanding and to avoid potential confusion, we have further categorized the trackers listed in Table 3** based on their type, architecture, and application field, as shown below: \n|**Method**|Type|Architecture|Field|\n|-|-|-|-|\n|**SiamAPN**|lightweight/Efficient Tracker| CNN-based Method|UAV Tracking|\n|**SiamAPN++**|lightweight/Efficient Tracker|CNN-based Method|UAV Tracking|\n|**HiFT**|lightweight/Efficient Tracker|CNN-based Method|UAVTracking|\n|**P-SiamFC++**|lightweight/Efficient Tracker|CNN-based Method|UAVTracking|\n|**TCTrack**|lightweight/Efficient Tracker|CNN-based Method|UAVTracking|\n|**UDAT**|lightweight/Efficient Tracker|CNN-based Method|UAVTracking|\n|**ABDNet**|lightweight/Efficient Tracker|CNN-based Method|UAVTracking|\n|**DRCI**|lightweight/Efficient Tracker|CNN-based Method|UAVTracking|\n|**HiT**|lightweight/Efficient Tracker|CNN-based Method|General Tracking|\n|**DDCTrack**|lightweight/Efficient Tracker|Transformer-based Method|UAV Tracking|\n|**SMAT**|lightweight/Efficient Tracker|Transformer-based Method|General Tracking|\n|**LiteTrack**|lightweight/Efficient Tracker|Transformer-based Method|General Tracking|\n|**AVTrack-ViT**|lightweight/Efficient Tracker|Transformer-based Method|UAV Tracking|\n|**AVTrack-DeiT**|lightweight/Efficient Tracker|Transformer-based Method|UAV Tracking|\n\n- For your reference, the performance comparison can be found in **Table 3**, and the speed comparison is presented in **Figure 5**.\n- **Once again, thank you for your valuable feedback. If you consider this table appropriate, I will incorporate it into the revised version of the manuscript in accordance with your suggestion**.\n\n>***Q2**. It's unclear how robust DSATrack is to extreme low-resolution or partial occlusion cases, which are critical for UAV applications.*\n- **Thank you for your valuable comment. We appreciate your attention** to evaluation under challenging scenarios. **To clarify, we have included the performance** of our model under low-resolution settings in **Table 6 of Appendix A.1**, which covers cases such as camera motion, fast motion, and low resolution.\n\n- Additionally, for your reference, **we have provided a comparison of precision with other state-of-the-art trackers under low-resolution and partial occlusion conditions in Figure 3**.\n\n- The results presented in Table 6 and Figure 3 demonstrate that our method **exhibits strong robustness under low-resolution and partial occlusion scenarios**. This can be attributed to our semantic-aware strategy, which enables more accurate feature alignment and consequently enhances overall model performance.\n\n>***Q3**. How generalizable is DSATrack across different UAV tracking scenarios?*\n - **Thank you** for your valuable comment. **We would like to clarify that we have provided the precision performance comparison** across different UAV tracking scenarios in **Figure 3 and Figure 7 of Appendix A.1**, covering challenging scenarios such as **Low Resolution, Partial Occlusion, Full Occlusion, Camera Motion, Fast Motion, Background Clutters, Aspect Ratio Change,  Illumination Variation, Out-of-View, Scale Variation, Similar Object and Viewpoint Change**.\n\n- **Thank you for your comment**. To further clarify the results, **we create a table accodirng to Figure3 and Figure 7 of Appendix A.1** to facilitate your understanding and to avoid potential confusion. **If you find the table more appropriate, we will replace the Figure 3 and Figure 7 of Appendix A.1 with the table in the revised manuscript to present the results across different scenarios more clearly**.\n |**Method**|Low Resolution|Partial Occlusion|Fast Motion|Camera Motion|Full Occlusion|Background Clutter |Aspect Ratio Change | Out-of-View|\n|-|-|-|-|-|-|-|-|-|\n|**ECO-HC**|54.7|79.6|67.9|80.0|75.1|64.9|88.3|87.0|\n|**SiamAPN++**|58.8|69.8|75.9|70.8|64.4|51.9|81.2|81.4|\n|**BACF**|59.8|71.8|57.3|77.3|67.1|60.2|73.7|81.2|\n|**KCF**|64.2|62.1|56.9|70.0|58.1|52.8|62.9|58.2|\n|**TCTrack**|64.4|76.0|73.8|78.5|71.7|62.4|86.0|87.9||\n|**AVTrack**|73.8|79.2|82.7|86.5|80.3|73.0|92.1|91.0|\n|**DSATrack-ViT-B**|74.5|82.8|74.5|85.8|78.1|74.6|**91.7**|94.8|96.4|\n|**DSATrack-ViT-T**|77.1|78.4|76.5|86.3|77.9|76.0|**95.9**|94.1|\n|**DSATrack-D8**|86.8|**86.3**|**88.4**|**89.7**|**82.3**|**80.4**|93.5|96.4|\n|**DSATrack-D7**|**87.2**|82.2|82.0|87.0|76.2|78.0|94.7|96.9|\n|**DSATrack-D6**|76.2|82.1|85.9|87.7|81.3|76.5|85.9|86.2|\n|**DSATrack-D4**|80.6|77.4|86.0|87.6|79.0|74.0|88.4|89.0|\n\n>***Q4**. Can the semantic-aware correlation maps be visualized to better demonstrate the interpretability of the method?*\n- Thank you for your valuable comment.  The visualization of semantic-aware correlation maps has been presented in Figure 4. In each row of Figure 4, (a) shows the input frame, while (b) and (c) depict the correlation maps before and after applying the semantic correlation module, respectively. Brighter areas indicate stronger similarity. The enhanced focus and localization in (c) demonstrate the effectiveness of our semantic-aware module under challenging conditions such as camera motion, fast motion, and low resolution.\n\n>***Q5**. The details of the Dynamic Semantic Relevance Generator are insufficiently elaborated—how exactly it computes relevance, and how it integrates with the Transformer pipeline should be clarified.*\n\n- Thank you for your constructive comment. We now provide a detailed clarification of how the **Dynamic Semantic Relevance Generator (DSRG)** computes relevance and integrates with the Transformer pipeline. Please refer to **Section 3.2** and **Figure 2** of the paper for visual references, and to **Equations (1)–(13)** for full definitions. \n\n---\n\n- ### (1) Initial Correlation Map Computation\n- We begin by computing a high-dimensional correlation tensor between search queries and template keys:\n$\nC_{zx} = \\frac{Q_x K_z^\\top}{\\sqrt{d_k}} \\in \\mathbb{R}^{h_x \\times w_x \\times h_z \\times w_z \\times l}\n$\nThis represents dense similarity scores between all pairs of tokens. (See Equation (1), Line 120)\n\n---\n\n- ### (2) Dynamic Semantic Relevance Estimation\n\n- We reshape $$\nC_{zx} \\in \\mathbb{R}^{h_x \\times w_x \\times h_z \\times w_z \\times l}\n\\ \\rightarrow \\ \nC_{zx} \\in \\mathbb{R}^{N_x \\times N_z \\times l}, \\quad\nN_x = h_x \\cdot w_x, \\quad N_z = h_z \\cdot w_z \\tag{1}\n$$.\nThen we:\nConstruct a semantic graph \\( G = (V, E) \\), where each node corresponds to a template token.\nApply average pooling and concatenate features into a vector matrix $$\\vec{v} \\in \\mathbb{R}^{N_z \\times l}$$:\n$$\n\\vec{v}\\_i = \\frac{1}{N\\_x} \\sum\\_{j=1}^{N\\_x} C\\_{zx}[j, i, :]\\tag{2}\n$$\nCompute pairwise semantic similarity matrix:\n$\nA = \\vec{v} \\cdot \\vec{v}^\\top \\in \\mathbb{R}^{N_z \\times N_z \\times l} \\tag{3}\n$\nEstimate relevance scores using an MLP and LogSoftmax:\n$$\n\\pi_{ij} = \\text{LogSoftmax}(\\text{MLP}(A_{ij})) \\in \\mathbb{R}^{2} \\tag{4}\n$$\nUse Gumbel-Softmax to dynamically generate a binary semantic relevance mask:\n$$\nE = \\text{GumbelSoftmax}(\\pi) \\in \\{0,1\\}^{N_z \\times N_z} \\tag{5}\n$$\n(See Equations (2)–(5), Lines 131–139; and Figure 2, bottom-left panel.)\n\n---\n\n- ### (3) Refined Semantic-Aware Correlation Map\n- We propagate correlation features across semantically relevant nodes using graph convolution with normalization:\n$C^{\\prime}_{zx} = \\Lambda^{-1/2} \\hat{E} \\Lambda^{-1/2}C^\\top\\_{zx} W_v \\in \\mathbb{R}^{N_x \\times N_z \\times l} \\tag{6}\n$\nWhere $\\hat{E} = E + I $, $I$ is the identity matrix and $ \\Lambda $ is the degree matrix.  \n(See Equation (7), Line 141)\n\n---\n\n- ### (4) Integration into Transformer Pipeline\n- The refined semantic-aware correlation map $( C'_{zx} )$ is used in two ways:\n- **Token Filtering** (Eq. (8) Line 145): Discards less informative template tokens based on MLP + Gumbel-Softmax decisions.\n**Hybrid Attention Module** (Eq. (9)–(11) Line 146-148): Combines Self-attention on both search and template tokens, and Cross-attention from search to template using both $C_{zx}$  and $ C'_{zx}$ :\n$$\n\\text{CrossAttention}(Q\\_x, K\\_z, V\\_z) = \\text{Softmax}(C\\_{zx} + C'\\_{zx}) V_z \\tag{7}\n$$\nThe final updated tokens are passed through a feedforward network:\n$$\nT''\\_{xz} = T'\\_{xz} + \\text{FFN}(T'\\_{xz}), \\quad T'\\_{xz}=\\text{HybridAttention}(\\cdot) + T\\_{xz} \\tag{8}\n$$\n(See Equations (8)–(13), Lines 143–152; and Figure 2, bottom-right block.)\n\n---\n\n- ### (5) Summary\n- The **DSRG** dynamically refines semantic relevance via learnable attention.\nIt is inserted at selected Transformer layers (layers 4, 7, 10).\nThe resulting semantic-aware correlation map helps eliminate background noise and improves attention focus.\nIts benefit is validated by:\n  - Ablation study in **Table 4** and **Table 6**,\n  - Visual evidence in **Figure 4**.\n\nWe hope this clarified explanation helps the reviewer understand the motivation, computation, and integration of the proposed DSRG module. Thank you again for your valuable feedback."}, {"review_id": "wMTcQ9HtSe", "reviewer": "Reviewer_cKGP", "summary": "This paper proposes a UAV tracking framework called DSATrack, which aims to address the lack of semantic awareness in existing methods. DSATrack introduces a dynamic semantic correlation generator and combines this generator with a Transformer-based correlation graph to explore semantic relations. This design enhances the ability to extract key information from the template within the search region. The framework also includes a pruning technique, thus achieving a balance between speed and accuracy. The experimental results on multiple datasets demonstrate its effectiveness and robustness.", "questions": "1.The paper states that the inputs of DSATrack are the template and the patch. It is unclear where the patch comes from. It is also unclear how the model performs without the patch. This should be explained and tested in an ablation study.\n2.The description of the Dynamic Semantic-aware Transformer in Figure 2 is confusing. It is unclear if Semantic-aware Correlation Map is generated by Semantic Aware Modeling module or if it is unrelated to it, as Figure 2 suggests. The authors should review Figure 2 and the text and decide if they need to revise the figure or the description. Figure 2 also shows both a Dynamic Semantic-aware Transformer block and a Transformer block. It is unclear which Transformer layers are removed.\n3.In Table 3, LiteTrack is not left aligned.\n4.The paper does not explain the difference between SSAM and DSAM.\n5.I also doubt the efficiency and accuracy of the DSATrack-ViT-B model.", "limitations": "1. Recent advanced SOT transformer-based methods are missing in the experiments, such as OSTrack, LoReTrack, LoRAT, MixformerV2, AQATrack, AsymTrack. Although not UAV-specific trackers, it is necessary to compare the proposed tracker with these non-real-time SOTA trackers for a comprehensive evaluation.\n2. Discussion on Computational Complexity, while DSATrack outperforms SOTA models in speed, the paper lacks a detailed comparison of FLOPs and parameter.\n3. Failure Case Analysis: It is recommended to include a detailed failure analysis, highlighting scenarios where DSATrack struggles.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "This paper brings dynamic semantic aware correlation modeling into the field of aerial tracking. It has good validity. However, the description of the method can be confusing. Some concepts are not clearly explained. The experiments are not thorough enough. In addition, the credibility of the efficiency and accuracy of the DSATrack-ViT-B model is open to question.", "was_revised": true, "final_justification": "Thanks for the detailed response. I tend to raise the rating, as my concerns have been mostly resolved.", "rebuttal": "**We sincerely apologize for the omission. In fact, many of the experiments you pointed out have already been conducted in accordance with the suggestions**. However, **due to NeurIPS’s strict 9-page limit for single-column submissions— which provides even less space than the standard 8-page double-column**— we were unfortunately forced to leave out some of the content.\n\n**In camera ready,  NeurIPS allows an extension to 10 pages, and we will make sure to include all the experiments and address your comments directly in the main text. We truly hope you will reconsider our submission**. \n\n>***Q1**. I also doubt the efficiency and accuracy of the DSATrack-ViT-B model.*\n- The discrepancy from previous results arises because our method is developed based on the codebase of GRM (CVPR 2023)[1], in which the parameter **exclude_invalid_frames** of testing code is set to **True** by default. \n- **Through multiple rounds of testing, we found that enabling this setting to True to yield higher performance**. However, after reviewing relevant literature and consulting with peers, we found that there is **no established consensus** on whether invalid frames should be excluded.\n- **To ensure the rigor of our evaluation**, we reset this parameter to **False**. Our model underwent **extensive training and repeated evaluation**, and the results presented in the paper reflect the most rigorous setting.\n- **We will release the complete training and evaluation code, along with model weights, for full transparency and reproducibility**.\n\n>***Q2**. It is unclear which Transformer layers are removed.*\n- In Section 3.3 of the paper, Hierarchical Contribution Ranking Pruning is used to prune Transformer blocks. **Table 1 of the paper presents the contribution scores of each layer (See Line 179-180)，I have restated the data from Table 1 below for your reference**:\n|Dataset|Layer2|Layer3|Layer4|Layer5|Layer6|Layer7|Layer8|Layer9|Layer10|Layer11|Layer12|\n|-|-|-|-|-|-|-|-|-|-|-|-|\n|UAV123|0.1325|0.0739|0.0615|0.0581|0.0429|0.0457|0.0438|0.0358|0.0428|0.0657|0.1444|\n\n- **While Table 2 of the paper lists the specific layers that are pruned (See Line 179-180), I have restated the data from Table 2 below for your reference**:\n|Variant|RemovedLayers|Variant|RemovedLayers|\n|-|-|-|-|\n|DSATrack-D8|6,7,9,10|DSATrack-D6|5,6,7,8,9,10|\n|DSATrack-D7|6,7,8,9,10|DSATrack-D4|3,5,6,7,8,9,10,11|\n\n\n>***Q3**. The paper states that the inputs of DSATrack are the template and the patch. It is unclear where the patch... It is..., should be explained and tested in an ablation study.*\n- Thank you for your comment. We apologize for the lack of clarity regarding the “patch”. **It is not our main contribution**. The design is based on RFGM[2], we mentioned this part in Section 3.1, line 102 of the paper.   Each template $z_t \\in \\mathbb{R}^{3 \\times H \\times W} $ is divided into non-overlapping patches of size $S \\times S $, producing $N_z = \\frac{H \\times W}{S^2}$ patches. The patches are projected into token embeddings via a shared convolutional layer:\n$$\nT^z_t = \\{ T^z_1, T^z_2, ..., T^z_{N_z} \\}, \\quad T^z_i \\in \\mathbb{R}^C\n$$\nwhere $C$ is the token embedding dimension. These tokens are appended to the token memory $\\mathcal{M}_{t-1}$ from previous frames to form a candidate token pool:\n$$\n\\mathcal{M}\\_{t} = \\mathcal{M}\\_{t-1} \\cup T^z\\_t\n$$\n- The token update mechanism can be referred to in RFGM[2]. **Thank you for your suggestion — we will provide a detailed explanation of the implementation in the appendix of the camera-ready version**.\n\n- The ablation study is as below:\n|Backbone|Method|DTB70-Prec.|DTB70-Succ.|UAVDT-Prec.|UAVDT-Succ.|VisDrone-Prec.|VisDrone-Succ.|UAV123-Prec.|UAV123-Succ.|\n|-|-|-|-|-|-|-|-|-|-|\n|ViT-D8|One template|89.5|69.3|85.0|61.8|88.5|67.4|86.4|65.7|\n|ViT-D8|Temaple&Patches|89.3|69.5|85.7|63.4|90.8|69.1|87.6|67.0|\n\n>***Q4**. The description of the Dynamic Semantic-aware Transformer in Figure 2 is confusing. It is unclear...*\n- **The Semantic aware Correlation Map is outputed by Semantic-Aware Modeling. In addition, we will enclose the Graph and Semantic Model label within a single box** in Figure 2 to present more clearly.\n- In the main pipeline (top of Figure 2), the Graph block  **“Semantic-Aware Modeling”** outputs a tensor labeled with **“Semantic-aware Correlation Map”** that follows the solid arrow into the **Hybrid Attention**.  \n- As noted in lines 109–111 of the manuscript, \n > “Using the Semantic Aware Modeling, we fuse the similarities of features with similar semantics to obtain an optimized Semantic-Aware Correlation Map.”  (see  Line109-111).\n* **Equation (7)** (line 140-142) – We will incoparate the description :\n > “The Semantic aware Correlation Map is obtained through Semantic-Aware Modeling.”\n  $$\n  C'\\_{zx} = \\Lambda^{-\\tfrac12}\\,\\hat{E}\\,\\Lambda^{-\\tfrac12}\\,C\\_{zx}^{\\top}\\,W\\_v ,\n  $$\n  where $C'\\_{zx}$ is precisely the **Semantic-aware Correlation Map**.\n- **Thank you for your suggestion, whichi is helpful to improve our manuscript**.\n\n>***Q5**. In Table 3, LiteTrack is not left aligned.*\n- Thank you for your reminder. I will incorporate the revision, it is very helpful for enhancing the quality of the manuscript.\n\n>***Q6**. The paper does not explain the difference between SSAM and DSAM.*\n- SSAM employs convolution operation. The receptive field is limited to the size of the convolutional kernel. It can only capture and fuse semantic correlations within a local region. In contrast, our proposed DSAM enables the model to adaptively integrate the relevant semantic information across global semantic correlation.\n- Our initial design aimed to capture semantic correlations using 4D convolution (SSAM), which indeed brought performance improvements. Motivated by these gains, we further extended the idea by exploring whether dynamically selected (DSAM) could lead to even greater enhancements. The experimental results confirmed the validity of this hypothesis. As shown in the table, our method achieved improvements across all datasets, with gains of 2 to 3%.\n|Method| DTB70-Prec. | DTB70-Succ.|UAVDT-Prec.|UAVDT-Succ.|VisDrone2018-Prec.|VisDrone2018-Succ.|UAV123-Prec.|UAV123-Succ.|\n|-|-|-|-|-|-|-|-|-|\n|Baseline|87.7|67.9|85.0|63.3|86.5|65.7|87.3|67.2|\n|Baseline+SSAM|88.0|68.3|85.5|62.6|**87.8**|66.6|88.8|68.5|\n|Baseline+DSAM|**91.2**|**70.6**|**88.1**|**66.3**|87.1|**67.0**|**90.2**|**69.4**|\n\n>***Q7**. Recent advanced SOT transformer-based methods are missing in the experiments, such as OSTrack, LoReTrack, LoRAT, MixformerV2, AQATrack...Although not UAV-specific trackers*\n- We sincerely appreciate your suggestions. In comparative analysis, DSATrack was evaluated against several sota methods. The results demonstrate that DSATrack achieves competitive performance compared to advanced tracking algorithms while attaining the high speed.\n|Method|---|AVisT|---|---|TrackingNet|---|---|LaSOT|---|---|GOT-10k|---|\n|-|-|-|-|-|-|-|-|-|-|-|-|-|\n||$AUC$|$OP_{50}$|$OP_{75}$|$AUC$|$P_{norm}$|$P$|$AUC$|$P_{norm}$|$P$|$AO$|$SR_{0.5}$|$SR_{0.75}$|\n|SwinTrack|-|-|-|81.1|-|78.4|67.2|70.8|47.6|71.3|81.9|64.5|\n|MixformerV2|-|-|-|83.4|88.1|81.6|70.6|80.8|76.2|-|-|-|\n| AsymTrack |-|-|-|80.0 |84.5| 77.4 |67.7| 76.6| 61.4| 64.7| 73.0| 67.8 | \n|LoRAT-B-224|-|-|-|83.5|87.9|82.1| 71.7| 80.9| 77.3|72.1|81.8|70.7|\n|LoReTrack-256|-|-|-|82.9| 81.4|-|70.3| 76.2| -|73.5|84.0| 70.4| \n|SeqTrack|56.8|66.8|45.6|83.3|88.3|82.2|69.9|79.7|76.3|74.7|84.7|71.8 |\n|AQATrack|-|-|-|83.8|88.6|**83.1**|**71.4**|**81.9**|**78.6**|73.8|83.2|72.1|\n|OSTrack|54.2|63.2|42.2| 83.1|87.8|82.0|69.1|78.7|75.2|71.0|80.4|68.2|\n|**DSATrack-ViT-B**|**60.2**|**69.1**|**50.2**|**84.1**|**88.6**|82.7|69.4|78.6|74.6|**75.0**|**85.6**|**73.7**|\n>***Q8**. While DSATrack outperforms SOTA models in speed, the paper lacks a detailed comparison of FLOPs and parameter*\n- We thank the reviewer for the suggestion. We conducted real-world experiments using newly bought drone and Jetson Xavier. The results demonstrate that our method achieves a balance between accuracy and speed, and surpasses previous methods in performance.\n|Method|DTB70-Pre.|DTB70-Suc.|UAVDT-Pre.|UAVDT-Suc.|VisDrone-Pre.|VisDrone-Suc.|UAV123-Pre.|UAV123-Suc.|Flops(G)|Params(M)|Xavier(FPS)|RTX3090(FPS)|\n|-|-|-|-|-|-|-|-|-|-|-|-|-|\n|HiT|75.1|59.2|62.3|47.1|74.8|58.7|82.5|63.3|4.35|42.14|36.6|159.0|\n|AVTrack-ViT|81.3|63.3|79.9|57.7|86.4|65.9|84.0|66.2|1.82|6.2|30.0|184.8|\n|LiteTrack|82.5|63.9|81.6|59.3|79.7|61.4|84.2|65.9|12.81|49.59|15.9|160.1|\n|**DSATrack-D7**|**88.1**|**68.4**|84.6|**62.1**|**88.6**|**68.0**|**87.3**|**66.6**|23.64|56.76|14.0|162.4|\n|DSATrack-D4|84.5|65.9|82.3|58.9|84.8|64.1|84.1|63.0|14.12|35.5|17.4|**214.1**|\n|DSATrack-ViT-T|86.6|67.4|84.3|61.6|84.8|64.1|85.5|65.5|2.99|8.21|27.6|114.3|\n|DSATrack-DeiT-T|84.5|65.7|**85.0**|62.0|82.0|60.8|84.4|64.5|2.99|8.21|27.4|114.4|\n\n>***Q9**. Failure Case Analysis.*\n- Based on your suggestion, we conducted a failure case analysis. We found that DSATrack performs poorly in out-of-view and full occlusion scenarios, where it struggles to re-detect and recover the target. This may be a common limitation faced by all window-based local trackers.\n\n- To address this issue, we plan to incorporate a two-stage re-detection mechanism in future work to recover lost targets.\n\n- As NeurIPS 2025 does not allow anonymous links or supplementary figures during the rebuttal phase, we will include these analyses and visualizations in the camera-ready. **Thank you for your suggestion—it is very helpful in improving the quality of our manuscript**.\n\n[1].Gao S, Zhou C, Zhang J. Generalized relation modeling for transformer tracking[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2023: 18686-18695.\n\n[2]. Zhou X, Guo P, Hong L, et al. Reading relevant feature from global representation memory for visual object tracking[J]. Advances in Neural Information Processing Systems, 2023, 36: 10814-10827."}, {"review_id": "YKNo7YgugW", "reviewer": "Reviewer_AQ2d", "summary": "The paper addresses the problem of tracking objects with the use of a drone equipped with a camera. The proposed solution is based on the use of a transformer architecture. The specific contribution is related to the addition of a semantic component to improve tracking ability and a pruning approach in the transformer blocks to reduce complexity and increase tracking speed.  \nThis reviewer found the idea of adding a semantic component to better track the object in the scene relevant. It is based on generating template semantic tokens and then computing a correlation with the scene/search tokens.  This allows the implementation of a dynamic semantic relevance generator. \n\nThe paper offers numerical results using various datasets and a comparison with other architectures used for tracking. Additionally, some real experimentation is also documented.", "questions": "In addition to the above comments:\n\n- Can you elaborate more on how the template tokens are generated? \n\n- The Semantic Aware Modeling section might be integrated in the previous one, since essentially this describes just a refinement of the correlation map.  \n\n- Would it be possible to run experiments that show the tracking ability of multiple objects? \n\n- The section about real-world experiments does not clarify how the initialization of the tracking process is done. That is, how the object is selected by correctly positioning the bounding box.  Additionally, the reported results show \"qualitative\" performance (some frames). Would it be possible to define and report some quantitative performance metrics? \n\n- A final minor remark is that the terminology \"UAV tracking\" is ambiguous since at first it may be interpreted as tracking the location of the UAV and not using a UAV equipped with a camera to monitor and track objects in a scene.", "limitations": "Yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "The proposed approach has been proven to offer the best performance and even the second-best performance with lighter versions.  \nThe idea of adding a semantic scene component is interesting. What is not fully understood from the paper is how the template semantic tokens are generated. It is assumed that this is achieved by pre-training with a number of possible known objects and scenes in a supervised fashion. If so, the architecture is limited to tracking \"known\" objects. \n\nAdditionally, tracking is herein limited to a single object. Would it be possible to simultaneously track multiple objects also belonging to different classes?  \n\nAnd what about using multiple cameras in a multi-drone setup? Is it something to be considered and possibly implemented by extending the current architectures?", "was_revised": true, "final_justification": "I appreciated the reply and additional experimental results made in the rebuttal phase. I have increased the significance score.", "rebuttal": "We sincerely appreciate your time and thoughtful review. We would like to take this opportunity to clarify the core ideas, address the raised issues in detail, and provide additional experimental results to support the effectiveness of our method.\n\n>***Q1**.  The idea of adding a semantic scene component is interesting. What is not fully understood from the paper is how the template semantic tokens are generated.*\n- Thank you for your insightful question. It precisely targets the core innovation of our paper. I am genuinely excited to share our idea with you.\n\n- Our method does not require prior knowledge of the target's category. Instead, our supervision only indicates where an object is present, without specifying what the object is. Given this limited information, how can we perform semantic modeling?\n\n- We address this challenge by introducing a token dropping strategy on the template. Specifically, we enforce the model to discard tokens with low semantic relevance and retain those with richer semantic content. To achieve this in an end-to-end manner, we employ the Gumbel-Softmax[1], which allows us to learn discrete token selection during training. Tokens with lower scores—i.e., less semantic information—are dropped, while higher-scoring tokens are preserved. The formulation of Gumbel-softmax[1] is as bellow:\n- $\\mathcal{M} = \\text{Gumbel-Softmax}\\left( \\log\\text{-}\\text{Softmax}(\\text{MLP}(C'_{zx})) \\right).$\n- The  Correlation Map $C'_{zx}$ is fed into an MLP and Gumbel-softmax to evaluate the importance of the Template Tokens. \nLess semantic tokens are discarded based on $\\mathcal{M} \\in \\{0,1\\} \\in \\mathbb{R}^{N_z \\times N_z}$ to accelerate computation.\n- This training strategy enables our model to acquire the ability to identify semantic tokens even during inference.\nAs a result, the patch memory progressively stores an increasing number of semantic tokens, which in turn enhances the quality of the semantic-aware correlation map during matching.\nThis improved correlation map further facilitates more accurate selection of semantic tokens, forming a mutually reinforcing cycle that continuously strengthens both components.\n\n\n\n>***Q2**. It is assumed that this is achieved by pre-training with a number of possible known objects and scenes in a supervised fashion. If so, the architecture is limited to tracking \"known\" objects.*\n- As indicated in Q1, our semantic token selection process does not require any prior knowledge of the object category.\nInstead, we leverage Gumbel-softmax to enforce the model to learn, in a self-supervised manner, which tokens correspond to potential targets.\nTherefore, our method is inherently category-agnostic—it operates without any need to know what the target is in advance.\n\n>***Q3**. Additionally, tracking is herein limited to a single object. Would it be possible to simultaneously track multiple objects also belonging to different classes?*\n- **Thank you for your valuable suggestion. Although our original focus was on single object tracking, following your advice, we conducted additional experiments on multi-object tracking during the rebuttal period.**\n- Specifically, we crop multiple search regions and corresponding templates for each target within a frame. During inference, different targets are processed simultaneously by stacking them along the batch dimension, allowing our model to track multiple objects in parallel. The dataset used for this experiment is LaGOT，which is a multiple object tracking dataset. The experimental results are as follows：\n| Metric| DSATrack-D8 | SuperDiMP | DSATrack-D7 | PrDiMP-50 | DSATrack-D6 | PrDiMP-18 | DiMP-50 | DiMP-18 | QDTrack | OVTrack |\n|-|-------------|-----------|-------------|-----------|-------------|-----------|---------|---------|---------|---------|\n| **Success**    | 60.1        | 58.7      | 58.4        | 55.9      | 56.8        | 53.3      | 53.2    | 50.8    | 19.4    | 13.6    |\n| **Precision**  | 56.7       | 56.6     | 55.1       | 53.9     | 53.3       | 51.8     | 51.5   | 48.7   | 18.5   | 12.6   |\n\n>***Q4**. And what about using multiple cameras in a multi-drone setup? Is it something to be considered and possibly implemented by extending the current architectures?*\n**Thank you for the thoughtful question. Extending single-drone tracking systems to multi-camera and multi-drone setups is indeed a meaningful and challenging direction. Such an extension would require addressing several key issues, including**:\n\n- **Cross-view calibration and synchronization** to align observations from different drones;\n\n- **Inter-drone communication and coordination mechanisms** for consistent identity management and data association;\n\n- **Robust multi-view fusion strategies** to combine information from different viewpoints, especially under occlusion or viewpoint variation.\n- **In future work, we will make efforts to extend our approach to multi-camera and multi-drone systems as per your suggestion. We believe this would lead to a highly impactful and challenging research direction**.\n\n>***Q5**.Can you elaborate more on how the template tokens are generated?*\n- Thank you for your comment. The design is based on RFGM[2], we mentioned this part in Section 3.1, line 102 of the paper.   Each template $z_t \\in \\mathbb{R}^{3 \\times H \\times W} $ is divided into non-overlapping patches of size $S \\times S $, producing $N_z = \\frac{H \\times W}{S^2}$ patches. The patches are projected into token embeddings via a shared convolutional layer:\n$$\nT^z_t = \\{ T^z_1, T^z_2, ..., T^z_{N_z} \\}, \\quad T^z_i \\in \\mathbb{R}^C\n$$\nwhere $C$ is the token embedding dimension. These tokens are appended to the token memory $\\mathcal{M}_{t-1}$ from previous frames to form a candidate token pool:\n$$\n\\mathcal{M}\\_{t} = \\mathcal{M}\\_{t-1} \\cup T^z\\_t\n$$\n\n>***Q6**. The Semantic Aware Modeling section might be integrated in the previous one, since essentially this describes just a refinement of the correlation map.*\n- Thank you for the suggestion. We agree with your comment and will integrate the Semantic-Aware Modeling section into the previous part in the revised version.\n\n>***Q7**. That is, how the object is selected by correctly positioning the bounding box.*\n- At the beginning, the UAV hovers steadily in the air. The operator selects the initial bounding box from the live video feed, and the coordinates are sent back to the onboard Jetson AGX Xavier to initialize the tracker. The tracking process then begins.\n\n\n>***Q8**. Would it be possible to define and report some quantitative performance metrics?*\n- Thank you for your valuable suggestion. During the rebuttal period, we manually annotated the four recorded videos and conducted quantitative evaluations. The results are shown below. \n- We greatly appreciate your recommendation and will include this performance table in the camera-ready version. The suggestion has been very helpful in improving the quality of our manuscript.\n| Model             |   AUC |   Precision |   Norm Precision |\n|:-|------:|------------:|-----------------:|\n| DSATrack-ViT-B  | 66.9 |82.4  |79.7 |\n| DSATrack-D8  | 66.4 |83.7  |81.9 |\n| DSATrack-D7  | 61.6 |76.2  |72.0 |\n| DSATrack-D4  | 61.8  |  78.3|74.9  |\n| AVTrack  | 62.0 |   76.3  | 74.2|\n\n\n\n>***Q9**. A final minor remark is that the terminology \"UAV tracking\" is ambiguous since at first it may be interpreted as tracking the location of the UAV and not using a UAV equipped with a camera to monitor and track objects in a scene.*\n- Thank you for your valuable suggestion. We agree that the term “UAV tracking” may be ambiguous. In the revision, we will replace it with “Object Tracking with UAV” to more accurately convey that the task involves using UAVs to track objects in the scene.\n\n[1]. Jang E, Gu S, Poole B. Categorical reparameterization with gumbel-softmax[J]. arXiv preprint arXiv:1611.01144, 2016."}, {"review_id": "kktPlh0Yl0", "reviewer": "Reviewer_YeVi", "summary": "This work proposes a UAV tracker, named DSATrack, that utilizes semantic correlation to enhance tracking accuracy. The main contribution of DSATrack is the Dynamic Semantic Relevance Generator, which explores the semantic relevance according to the correlation map from the Transformer.\nBenchmark experiments are comprehensive, including DTB70, UAVDT, VisDrone2018, and UAV123, and DSATrack reaches the SOTA performance. The ablation study on correlation modeling shows the effectiveness of the proposed Dynamic Semantic Relevance Generator.\nThe inference speed of the method is well considered by pruning, and the method shows efficiency in real-world tracking experiments.", "questions": "1. Will the authors provide the source code and trained weights for reproduction?\n2. Will the authors provide the comparison on model size?\n3. Will the authors plan to include larger or more benchmarks for evaluation?\n4. How is model design intrinsically linked to the unique challenges of UAV tracking?\n5. ​What fundamentally distinguishes the proposed Dynamic Semantic Relevance Generator from prior approaches to correlation modeling?\n6. How will DSA generalize to datasets in common scenes, e.g., LaSOT?", "limitations": "Yes", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strength:\n\n1. The experiments are exceptionally comprehensive and well-designed, featuring established UAV tracking datasets and including the presentation of multiple model variants with distinct configurations, including DTB70, UAVDT, VisDrone2018, and UAV123, and DSATrack.\n\n2. Module design is reasonable and proven effective in experiments, especially in Table 4 and Table 5.\n\n3. Several variants of the model design are proposed with different architectural settings for diverse demands of tracking.\n\n4. The proposed DSATrack is deployed on an edge device for real-world application tests, where DSATrack-ViT-T and DSATrack-DeiT-T achieve 27.6 FPS and 27.4 FPS.\n\nWeakness:\n\n1. Although the evaluation benchmarks are well-established, their dataset scale appears insufficient by contemporary research standards.\n\n2. Source code and model weights are not attached.\n\n3. Comparison of model size between the proposed method and SOTA methods is not shown.\n\n4. Limited novelty compared with baselines. The idea of correlation modeling has been studied in several works, including HiFT and TCTrack. The model architecture design is also based on RFGM and OSTrack.\n\n5. Insufficient theoretical explanation of model design under UAV-specific scenarios. Special challenges of UAV tracking scenarios should be highlighted with cases.\n\n6. The generalization performance of DSATrack on generic datasets was not addressed, e.g., LaSOT, GOT-10k.", "was_revised": true, "final_justification": "The authors have addressed most of my concerns. It is recommended that the authors provide more explicit elaboration on the relevant issues in the main text. It is also suggested that the authors make further open-source contributions to the research community of UAV object tracking.​", "rebuttal": "We sincerely thank you for your positive evaluation and constructive feedback. We are glad that you found our method and experimental analysis meaningful. In the following, we address your specific comments and suggestions, and describe how we plan to further improve the manuscript accordingly\n>***Q1**.  Although the evaluation benchmarks are well-established,How will DSA generalize to datasets in common scenes, e.g., LaSOT, GOT-10k?*\n- Thank you very much for your valuable suggestion. Our model is primarily designed for object tracking using UAVs, and we found your comment particularly insightful. Accordingly, we conducted additional evaluations on LaSO, GOT-10k, TrackingNet, and AvisT, and the results demonstrate that our approach achieves competitive performance compared to mainstream single-object tracking methods.\n|Method|---|AVisT|---|---|TrackingNet|---|---|LaSOT|---|---|GOT-10k|---|\n|-|-|-|-|-|-|-|-|-|-|-|-|-|\n||$AUC$|$OP_{50}$|$OP_{75}$|$AUC$|$P_{norm}$|$P$|$AUC$|$P_{norm}$|$P$|$AO$|$SR_{0.5}$|$SR_{0.75}$|\n|SwinTrack|-|-|-|81.1|-|78.4|67.2|70.8|47.6|71.3|81.9|64.5|\n|MixformerV2|-|-|-|83.4|88.1|81.6|70.6|80.8|76.2|-|-|-|\n| AsymTrack |-|-|-|80.0 |84.5| 77.4 |67.7| 76.6| 61.4| 64.7| 73.0| 67.8 | \n|LoRAT-B-224|-|-|-|83.5|87.9|82.1| 71.7| 80.9| 77.3|72.1|81.8|70.7|\n|LoReTrack-256|-|-|-|82.9| 81.4|-|70.3| 76.2| -|73.5|84.0| 70.4| \n|SeqTrack|56.8|66.8|45.6|83.3|88.3|82.2|69.9|79.7|76.3|74.7|84.7|71.8 |\n|AQATrack|-|-|-|83.8|88.6|**83.1**|**71.4**|**81.9**|**78.6**|73.8|83.2|72.1|\n|OSTrack|54.2|63.2|42.2| 83.1|87.8|82.0|69.1|78.7|75.2|71.0|80.4|68.2|\n|**DSATrack-ViT-B**|**60.2**|**69.1**|**50.2**|**84.1**|**88.6**|82.7|69.4|78.6|74.6|**75.0**|**85.6**|**73.7**|\n\n\n- **We will include all these experimental results in the revised version during the camera-ready stage**.\n\n>***Q2**. Will the authors provide the source code and trained weights for reproduction?*\n- Thank you very much for your question. **In fact, after receiving your comment, we promptly organized the code and model weights. However, we later noted that NeurIPS does not allow anonymous links this year.** If our paper is accepted, **we will publicly release all training and testing code, along with the model weights, to ensure transparency and reproducibility**. \n\n\n>***Q3**. Will the authors provide the comparison on model size?*\n- Thank you for your valuable suggestion. We have provided the model size and inference speed as a footnote for your reference. Our approach achieves a balance between speed and accuracy, allowing different model sizes to be selected based on the computational platform for performing the tracking task. \n\n|Method|DTB70-Pre.|DTB70-Suc.|UAVDT-Pre.|UAVDT-Suc.|VisDrone-Pre.|VisDrone-Suc.|UAV123-Pre.|UAV123-Suc.|Flops(G)|Params(M)|Xavier(FPS)|RTX3090(FPS)|\n|-|-|-|-|-|-|-|-|-|-|-|-|-|\n|HiT|75.1|59.2|62.3|47.1|74.8|58.7|82.5|63.3|4.35|42.14|36.6|159.0|\n|AVTrack-ViT|81.3|63.3|79.9|57.7|86.4|65.9|84.0|66.2|1.82|6.2|30.0|184.8|\n|LiteTrack|82.5|63.9|81.6|59.3|79.7|61.4|84.2|65.9|12.81|49.59|15.9|160.1|\n|**DSATrack-D7**|**88.1**|**68.4**|84.6|**62.1**|**88.6**|**68.0**|**87.3**|**66.6**|23.64|56.76|14.0|162.4|\n|DSATrack-D4|84.5|65.9|82.3|58.9|84.8|64.1|84.1|63.0|14.12|35.5|17.4|**214.1**|\n|DSATrack-ViT-T|86.6|67.4|84.3|61.6|84.8|64.1|85.5|65.5|2.99|8.21|27.6|114.3|\n|DSATrack-DeiT-T|84.5|65.7|**85.0**|62.0|82.0|60.8|84.4|64.5|2.99|8.21|27.4|114.4|\n\n- **We will include this information in the paper, as it greatly contributes to improving the quality of our manuscript.**\n\n>***Q4**. The idea of correlation modeling has been studied in several works, including HiFT and TCTrack. The model architecture design is also based on RFGM and OSTrack.*\n- Thank you very much for your comments and questions. **Our method differs significantly from HIFT and TCTrack. Both HIFT and TCTrack are based on CNN architectures, where the correlation map is generated through convolutional operations**. Specifically, they perform matching between the template and search region in a holistic manner using convolution.\n\n- **In contrast, our method adopts a Transformer-based correlation mechanism, where matching is conducted in a point-to-point manner based on token-level similarity**. This point-wise matching inherently **lacks relational information between different positions and does not capture the global structure of the target**, which often leads to ambiguous matching.\n\n- **To address this limitation and enhance the semantic perception capability of the Transformer, we introduce a Semantic-Aware Generator that constructs a semantic relationship**. This relationship adaptively identifies which points in the template are semantically related to which regions in the search area, enabling more effective correlation fusion and significantly alleviating the semantic ambiguity problem of Transformers.\n\n- Moreover, since Transformer computes similarity based on token-wise interactions, it requires a richer set of representative semantic tokens to achieve accurate correlation. To this end, we employ a Semantic-Aware Correlation Map to guide the selection of semantic tokens, thereby improving the semantic expressiveness of the template. The enhanced semantic tokens in turn lead to a more accurate correlation map. **These two components — semantic tokens and semantic correlations — mutually reinforce and benefit each other**.\n\n- Compared with OSTrack and RFGM, our framework also adopts a one-stream Transformer architecture, which has become a strong and widely adopted baseline. However, unlike existing works, our method explicitly focuses on addressing the semantic modeling issue, which remains a core limitation of one-stream Transformers and also forms the motivation of our work.\n\n- We have further provided a comparative analysis of the four methods for your reference, as shown below:\n|Method|Hift|TCTrack|**DSATrack**|RFGM|OSTrack|\n|-|-|-|-|-|-|\n|**Architecture**|CNN-based Correlation|CNN-based Correlation|One Stream Transformer|One Stream Transformer|One Stream Transformer|\n|**Correlation Method**|Convolutional Operation|Convolutional Operation|Dynamic Semantic Matching|Transformer-based Matching|Transformer-based Matching|\n|**Semantic Aware Method**|Correlation Refinement by Transformer|Correlation Refinement by Transformer|Generate a dynamic semantic relationship|Point-to-Point|Point-to-Point|\n|**Semantic Tokens**|No|No|With Dynamic Semantic Tokens|No|No|\n|**Mutually enhance**|Only Refinement|Only Refinement|Mutually enhance between Semantic Tokens and Correlation|No|No|\n\n>***Q5**. How is model design intrinsically linked to the unique challenges of UAV tracking? Special challenges of UAV tracking scenarios should be highlighted with cases.*\n- Thank you for your comments and questions. **As mentioned in Lines 28–33 of the Introduction, our method specifically targets the challenges commonly encountered in UAV tracking scenarios, such as camera motion, fast motion, and low resolution**. These scenarios often lead to motion blur or the loss of semantic information, which significantly degrade the performance of existing trackers. However, current one-stream Transformer-based UAV tracking models do not explicitly address these issues.\n\n- To this end, we propose a semantic-aware approach to enhance the model’s generalization ability under such challenging conditions. **As illustrated in Figure 1, our method demonstrates clear advantages over existing CNN-based correlation and Transformer-based correlation strategies**.\n\n- Furthermore, in Figure 3, we present a qualitative comparison under the camera motion, fast motion, and low-resolution scenarios with other state-of-the-art trackers. For your convenience, we have also summarized the results of Figure 3 into a table, which further supports the effectiveness of our proposed semantic-aware design in mitigating the ambiguity problem caused by point-wise matching in Transformers.\n |**Method**|Low Resolution|Fast Motion|Camera Motion|\n|-|-|-|-|\n|**ECO-HC**|54.7|67.9|80.0||\n|**SiamAPN++**|58.8|75.9|70.8|\n|**BACF**|59.8|57.3|77.3|\n|**KCF**|64.2|56.9|70.0|\n|**TCTrack**|64.4|73.8|78.5|\n|**AVTrack**|73.8|82.7|86.5|\n|**DSATrack-ViT-B**|74.5|74.5|85.8|78.1|\n|**DSATrack-ViT-T**|77.1|76.5|86.3|\n|**DSATrack-D8**|86.8|**88.4**|**89.7**|\n|**DSATrack-D7**|**87.2**|82.0|87.0|\n|**DSATrack-D6**|76.2|85.9|87.7|\n|**DSATrack-D4**|80.6|86.0|87.6|\n\n- In addition, we conduct an ablation study in Appendix A.1 (Table 6), where our semantic-aware method achieves performance improvements of 2% to 3% under the three aforementioned challenges. For your reference, we include the table below:\n|Method| DTB70-Prec. | DTB70-Succ.|UAVDT-Prec.|UAVDT-Succ.|VisDrone2018-Prec.|VisDrone2018-Succ.|UAV123-Prec.|UAV123-Succ.|\n|-|-|-|-|-|-|-|-|-|\n|Baseline|87.7|67.9|85.0|63.3|86.5|65.7|87.3|67.2|\n|Baseline+DSAM|**91.2**|**70.6**|**88.1**|**66.3**|87.1|**67.0**|**90.2**|**69.4**|\n\n- **Thank you very much for your valuable suggestion. We will further enhance the explanation of the addressed challenges in our revision, as this greatly contributes to improving the quality of our manuscript.**"}]

Author rebuttal:

Meta-review: Paper Summary:  
This paper introduces DSATrack, a UAV tracking framework that uses a novel Dynamic Semantic Relevance Generator to improve semantic-aware correlation modeling. The authors also propose a Transformer pruning strategy to create model variants that balance state-of-the-art performance with real-time speed requirements.

Main Strengths and Weaknesses:  
The primary strength is its state-of-the-art performance across multiple UAV tracking benchmarks, stemming from its well-designed semantic-aware correlation modeling and comprehensive experiments. Its main initial weakness, cited by multiple reviewers, was a lack of clarity in explaining the core mechanisms and an insufficient comparison to a broader range of contemporary tracking methods.

Rebuttal Analysis:  
The rebuttal effectively addresses the majority of concerns raised by all four reviewers. After the rebuttal, three of the four reviewers explicitly raised their ratings or scores, citing that their questions about methodological clarity, experimental comparisons, and technical details had been satisfactorily resolved. The fourth reviewer, who had already given a positive score, confirmed that the rebuttal addressed most of their concerns.

Final Justification:  
There is a clear and positive consensus among all reviewers. While the initial reviews highlighted legitimate concerns regarding clarity and the completeness of the evaluation, the responses of the authors successfully mitigated these issues. Considering the interesting idea and the state-of-the-art results on several relevant benchmarks, the AC agrees with the reviewers and recommends accepting the paper. Additionally, it is strongly suggested that the authors incorporate the content discussed in the feedback to make the paper more comprehensive.
Decision: Accept (poster)