Multimodal Causal Reasoning for UAV Object Detection
Authors: Nianxin Li
Mao Ye
Lihua Zhou
Shuaifeng Li
Song Tang
Luping Ji
Ce Zhu
Venue: NeurIPS 2025 poster ()
Keywords: UAV object detection
Causal reasoning
Vision-language model
Primary area: applications
TL;DR: Casual reasoning based on VLM model discovers the condition-invariant object representation for easy detection.

Abstract: Unmanned Aerial Vehicle (UAV) object detection faces significant challenges due to complex environmental conditions and different imaging conditions. These factors introduce significant changes in scale and appearance, particularly for small objects that occupy limited pixels and exhibit limited information, complicating detection tasks.  To address these challenges, we propose a Multimodel Causal Reasoning framework based on YOLO backbone for UAV Object Detection (MCR-UOD). The key idea is to use the backdoor adjustment to discover the condition-invariant object representation for easy detection. Specifically, the YOLO backbone is first adjusted to incorporate the pre-trained vision-language model. The original category labels are replaced with semantic text prompts, and the detection head is replaced with text-image contrastive learning. Based on this backbone, our method consists of two parts. The first part, named language guided region exploration, discovers the regions with high probability of object existence using text embeddings based on vision-language model such as CLIP. Another part is the backdoor adjustment casual reasoning module, which constructs a confounder dictionary tailored to different imaging conditions to capture global image semantics and derives a prior probability distribution of shooting conditions. During causal inference, we use the confounder dictionary and the prior to intervene on local instance features, disentangling condition variations, and obtaining condition-invariant representations. Experimental results on several public datasets confirm the state-of-the-art performance of our approach. The code, data and models will be released upon publication of this paper.

Multimodal Causal Reasoning for UAV Object Detection
Nianxin Li', Mao Y Zhou', Shuaifeng Li', Song Tang', Luping Ji', Ce Zhu'
'University of Electronic Science and Technology of China CAIR, HKSIS, CAS
University of Shanghai for Science and Technology, China
linianxin 1 1220 gom, cvlab.ueste @maarbueestt@mmlcocom
https//gattuu.comm/mwwwo//KCCUCD
Abstract
Unmanned Aerial Vehicle (UAV) object detection faces significant challenges due to complex environmental conditions and different imaging conditions. These factors introduce significant changes in scale and appearance, particularly for small objects that occupy limited pixels and exhibit limited information, compli- cating detection tasks. To address these challenges, Causal Reasoning framework based on YOLO backhone for UAV Object Detec- tion (MCR-UOD). The key idea is the condition-invariant object representation for easy detection. Specifically, the YOLO backbone is first adjusted incorporate the pre-trained vision-language model. The original category labels are replaced with semantic text prompts, and the detection head is replaced with text-image contrastive learning. Based on this backbone, our method consists of two parts. The first part, named language guided region exploration, discovers the regions with high probability of object existence using text embeddings based on langune model such CLIP. Another part the backdoor adjustment casual reasoning module, which constructs a confounder dictionary tailored to different imaging conditions to capture global image semantics and derives prior probability distribution of shooting conditions. During causal inference, we use the confounder dictionary and the prior to inter- vene on local instance features, disentangling condition variations, and obtaining condition-invariant representations. Experimental results on several public datasets confirm the state-of-the-art performance of our approach. The code, data and models will be released upon publication of this paper.
propose Multimodel
the backdoor adjustment discover
Introduction
Deep learning has driven remarkable progress in object detection, with models such as Y5LO and Faster- MAnter-RCNN[III achieving strong performance on standard datasets. However, these methods struggle with Unmanned Acrial Vehicle (UAV) imagery due to unique acrial imaging challenges. The bird's-eye view introduces dense and cluttered backgrounds, where target objects are often obscured by complex environmental patterns. Combined with varying lighting and weather conditions, these factors create severe interference that disrupts feature learning and localization. Consequently, existing detectors suffer from high false alarm rates and missed detections, limiting their effectiveness in critical UAV applications such as surveillance and disaster monitoring. Developing robust algorithms overcome these background interference challenges remains open research problem.
Corresponding author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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westher
sonother
anglle
occluaion
detectur
accdusion
detector
Previous UAVOD methods
The propused MCR method)
Figure 1: (a) Previous methods do not handle confounders in UAV images, confusing detector. (b) Our approach removes the confounders via backdoor causal reasoning, enabling a better detector. The previous methods are mainly divided into three routes. The first focuses on region-based strategies, selectively up-scaling regions with dense objects to improve detection accuracy [14 10
43 28
The second route introduces additional network modules, such as attention mechanisms and multi- scale feature fusion, enhance feature representations [58] [55 30 52). The final employs data augmentation techniques to increase data diversity, allowing the models to handle a wider range of scenarios 42 [4]. Although these methods have made progress, they still have the following aspects for improvement: 1) Previous works rely solely on single visual features, limiting the improvement of detector performance; 2) Feature enhancement through attention mechanisms lacks interpretability. and does not clearly address how confounders like interference caused by redundant background information or challenging shooting conditions are mitigated.
To address the above issues, we propose the following solutions. First, utilizing a vision-language model for detection enables the full utilization of multimodal knowledge. By integrating the detection network with a vision- lasion-languag model, such as CLIP 34], the ability of the model to understand and correlate visual and textual information can be improved. This multimodal integration allows for a more context-aware understanding of the objects in the scenarios, improving the detection process, especially in scenarios where visual cues alone cannot offer enough discriminate information. Second, we incorporate causal inference 32) B1]. a framework designed to model cause-and-effee relationships, which allows us systematically address confounding effects (33) Specifieally, we focus on intra-class inconsistencies caused by factors such as environment and shooting conditions. Using causal reasoning, we can effectively eliminate the influence of these confounders, leading to more robust and accurate object detection, even in challenging environments. Finally, relying on visual- -angule models pre-trained on large data, we can construct confounding factors from textual perspective that are difficult to capture from an image acquisition perspective. In this way, the detection model that learns to remove these confounding factors has stronger and more robust generalization ability.
Based on the above analysis. we propose novel method called Multimodal Causal Reasoning for UAV Object Detection (MCR-UOD), Specifically, this method consists of two modules. The first is the language-guide between visual and linguistic information to guide the detection process. By integrating multimodal knowledge, LGRE endows the model with the ability to focus on object regions, improving detection accuracy in complex scenarios where visual information is insufficient. It first encodes category names features using the CLIP text encoder and then computes the object existence score for each region in the visual feature map based on these text features, highlighting regions more likely to contain objects. This process helps to remove the intervention from the background. The second is the Backdoor Adjustment Causal Reasoning (BACR) module, which aims to construct confounder dictionary based on CLIP text embeddings to eliminate the interference of confounders on the objects, enabling the model to handle intra-class feature inconsistencies due to environment and shooting condition variations. This module first selects regions based on the object existence scores. Then applies do-operator using the confounder dictionary to enhance the selected features and updates the confounder dictionary accordingly.
region exploration (LGRE) module. This module leverages the synergy
Our contributions can be summarized as follows: (1) We are the first to apply the mathematical principles of causal inference UAV object detection. The effects of varying confounders, caused by environment and shooting conditions, are weakened, allowing the detectors to ensure feature consistency within the same category, thus enhancing the robustness and accuracy of object detection. (2) Unlike previous causal inference methods in computer vision problems, we utilize text embeddings to construct and initialize a confounder dictionary, achieving cross-modal deconfounding. Due to the diversity of UAV environments and shooting conditions, it is clearly impossible to rely on UAV images to construct a confounder dictionary. The lision-languae model provides the possibility to construct such a dictionary. (3) Unlike traditional methods that rely solely on visual features,

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propose novel approach that leverages multimodal knowledge for UAV object detection. Based on text embeddings, the detector backbone will focus more on the object regions. So, although our method is based on the single-stage YOLO backbone, we can still obtain the region of interest and apply causal reasoning to enhance its features.
2 Related Work
UAV object detection (UAVOD) refers to the task of identifying and localizing objects in UAV- captured images. The existing approaches be categorized into three main routes. The first is zoom-in strategies, which improve detection accuracy by selectively upscaling regions containing dense or small objects. For example, EVORL employs an evolutionary reinforcement learning mech- anism guided by a reward function to determine optimal image patch scales [51], while AdaZ.om dynamically adjusts the size and aspect ratio of zoomed regions according to the spatial distribution of small objects 43). The second route enhances feature representation through additional network mod- ules, such as attention mechanisms multi-scale fusion. The representative method TPH-YOLOvv5 integrates the Transformer and CBAM (Convolutional block Attention Module) to emphasize key regions and improve feature extraction capability (58). Other works introduce lightweight modules or hierarchical attention to further improve performance [52 30 The third route adopts data aug- mentation techniques to increase sample diversity and improve generalization in different scenarios. These methods generate varied input distributions through weather simulation, viewpoint changes, or domain transfer to train better detectors
42 54
Vision-language model for object detection approach generally falls into two categories. The first focus on mapping language representations to region prompts. Coop optimizes class prompts with contrastive learning to boost the performance of the vision-language model (57). GLIP treats object detection as an association problem, aligning regions or bounding boxes with corresponding text prompts [18. The second category integrates the vision-language model into the existing detection framework. DenseCLIP adapts CLIP for dense prediction by integrating feature adaptation and dense semantic guidance to enhance localization and segmentation tasks [35]. ProposaICLIP employs an unsupervised approach to directly label images of objects [36), while RegionCLIP applies region- uses region-text alignment with CLIIP features to enhance open- vocabulary detection, which employs multi-scale feature fusion and query-based decoding to improve generalization to unseen objects [4].
based pre-training to associate image regions with textual descriptions [56].
Causal inference is increasingly applied visual tasks, with methods generally categorized as explicit confounder construction and implicit intervention removal [49 25 The former approach builds dictionaries based on object features or relationships and applies do-operation
remove confounding effects 41 50 29 17 45). VC R-CNN computes the frequency of occurrence of each category as prior probability and leverages prototypes to mitigate the negative impact of confounder objects during relational reasoning (1I. MAWCA constructs and updates confounder dictionary using ROI features, then uses a Transformer during inference to obtain interference- free object features. allowing transfer across different weather conditions [50]. The latter estimates causal effects by using sample augmentation or attention mechanisms to infer results under different interventions and averaging them. CT-MRI achieves single-source domain generation by randomly applying various style augmentations regions in the image, thereby obtaining Nomain-invarian features [4)) CIRL learns causal representations that can mimic causal factors based on the ideal properties emphasized, thereby enhancing the robustness of the learned features 27
3 Preliminaries
i=1 Problem statement. Suppose that the UAV object detection training set Derain = (X Yi h
No.
where Y;- = (bi,, ofx) represents the boxes and classes of objects in the i-th training image. Ner is the cardinality of the training set. The test set is Dtest = {X Y I. where Nie is the cardinality of the test set and Yie is unknown. Our goal is to use vision- model (CLIP) and casual reasoning to train a better object detector, improving detection performance on UAV images.
Structural causal model. As illustrated in Fig
2 we construct structural causal model to represent the relationships between variables in the detection process. F denote the features of the objects, Z represent the confounders, and Y are the classification results of the objects; the directed edges
3

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represent the causal relationships between the variables. In the directed acyclic graph, the path F F Y indicates that the features of objects F directly influence their classification results Y. For instance, distinctive visual patterns (e.g. wheels for cars) causally determine the predictions of the model. The backdoor path F < 7 - Y highlights the confounding effect of Z, which introduces spurious associations between features F and predictions Y. For example, Z could represent environmental factors (e.g. lighting, weather), data set biases (e.g. class imbalance), or sensor distortions (e.g. camera noise). These confounders corrupt the feature representation process (F + Z) while simultaneously influencing the model's decision (Z + Y). For example, poor lighting (Z) may degrade visual features (F), making objects harder recognize, while also skewing label distributions (Y) if certain classes dominate low-light scenarios. Such backdoor paths can lead models to rely on non-causal shortcuts. To address this, we employ a backdoor adjustment to isolate the genuine causal relationship F Y.
Backdoor adjustment for causal learning. To eliminate the confounding bias introduced by Z, we implement a backdoor adjustment method based on causal inference theory. The core idea is to stratify the data according to the confounder Z and then calculate the weighted average of the predictions in all strata. Formally, the causal effect of F on Y can be estimated as
2
dol-)
(a)
(b)
P(Y]do(F)) = Σ 2 = 2), (1) Figure 2: (a) Structural causal model shows the direct effect F' + Y and con- founding path F t- Z - Y; (b) Inter- vention model where the confounding path F + Z is blocked (indicated by x), enables estimation of the true causal effect F + Y.
where the do-operator signifies an intervention that re- moves the influence of Z. Here, P,Y|F, sents the prediction conditioned on both features F and specific value of Z, while P(Z 2) accounts for the prior distribution of the confounder. In practice, we first discretize continuous confounders (c.g.. lighting levels) into interpretable bins with a confounder dictionary Z. Then, we train our model to estimate P(Y Z) and compute P(Z) empirically from the training data. Finally, we aggregated the predictions across all Z strata to obtain an unbiased estimate of P(Y|de [do F)). This approach effectively blocks the backdoor path F t Z + Y, ensuring that the learned relationships reflect true causal mechanisms rather than spurious correlations. For further technical details, see Section 4.2.
repre-
4 Proposed Method
Overview. As shown in Fig the proposed method consists of two parts: the Language Guided Region Exploration (LGRE) module and the Backdoor Adjustment Causal Reasoning (BACR) module. The first module selects possible regions where objects may exist, and then the second module refines the features by backdoor adjustment ignoring the effects of confounders. Specifically, for the UAV image X,, multilevel features {C1, C22, }3} can be obtained based on YOLOV8 [40]. Since the challenges of UAVOD stem primarily from small objects, we focus on refining the C1 layer features. In the LGRE module, K category prompts and the corresponding text embeddings {ex } are obtained using the CLIP text encoder 39. K is also the number of categories. Then, the object existence probability map for each pixel is obtained by taking the product of the text embeddings {e){_ and the low-level feature C1. In the BACR module, the GPT model I is used to generate a series of confounder prompts, which are encoded as text embeddings Z to construct and initialize a confounder dictionary. Based on the probability map 8, we perform causal interventions on the features with high probability. The modulated features F" are projected back into the original feature map C1, resulting new feature map CT. Finally, we input {CT- C2, C3} along with the text embedding E into the contrastive head and the box head to obtain the final detection results.
4.1 Language Guided Region Exploration
In UAVOD, one of the key challenges is to accurately localize small objects, which are often represented in low-level features. To address this issue, we leverage the rich prior knowledge learned by language models to enhance object localization. The core idea to utilize textual information to guide the model's 's attention towards regions in the image that are likely to contain objects, improving

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(b) BACR Module
CLIP Text Encoder
Read
a car tt surnyy day with occlusion from front vlew
Confondor Dictionary
a LGRE Module
Write
photu of peuple cluax.
ppespecttine]
people
corrfrront top
bloycle
Concat
locelusion]:
palppartiallae [weather]]:
Susany, faggy...
select feature
Input image
Contrastive Detection
classffication
Max Sigmoid
<7
LGRE-BACR
Box
YOLO Backbone
Head
Figure 3: The overall framework of MCR-UOD. (a) Language Guided Region Exploration (LGRE) module computes object existence probability map using CLIP text embeddings and (b) Backdoor Adjustment Causal Reasoning (BACR) module performs causal intervention through cross-attention between selected high-probabiling pixel-level visual features and confounder dictionary. The de- confounder feature map CT is then fed into detection head.
the object existence score for different regions of interest. Specifically, we generate prompts by constructing descriptive text based on category names, such as 'a photo of [category]', and obtain text embeddings = {ex
by encoding these prompts using the CLIP text encoder [39]. where E Rx and D1 is the dimension of the text embedding. The input image X encoded by CSPDarknet (2) generating multi-scale feature maps {C1, C2, Cz). Since the challenges of UAVOD are primarily stemmed from small objects in low-level features, we focus on C₁ E x x accordingly, where H. W and D2 denote height, width, and channel number, respectively. First, the similaritics between pixel-level features in C1 and text embeddings { are calculated to obtain the score map G as follows,
== (A(E)),, GE R
where fi and f2 represent two fully connected layers, which transform C1 and E into the same dimension feature space, respectively. Then we calculate the maximum value G at each pixel and apply Sigmoid operation the following.
s(h, w) m ( ((,
Rxxxx1
where (f, w) represents the pixel coordinate, and s indicates the likelihood of object existence this pixel. According to the object existence probability map the regions that are likely to contain objects can be selected. By perform element-wiss multiplication with C₁ and s, the original feature map C can be updated as
= C C R #xxx>
where C represents the element-wise multiplication, and Cf is the updated C₁ feature map.
4.2 Backdoor Adjustment Causal Reasoning
In LGRE module, the updated low-level features C/ and the corresponding object existence probabil- ity map s are obtained. We select the highest 7 pixel features to form a feature matrix F E Nx D2 based on the probability values follows,
F= (C)( 4}, 1. Topx(ss), = HW
(5)
5

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where T is threshold hyperparameter. N is the number of selected pixels. This feature matrix F records the regions that are more likely to contain objects. According to Eq.(1) in Section 3. we implement backdoor adjustment for visual features F using a confounder dictionary Z. P(Y F. = 2) refers to the classification result Y obtained from the features F, given = z. The features F first pass through function f with cross-attention to the dictionary Z to obtain the classification logits, which are then passed through a softmax operation to produce the classification result Y. Therefore:
P(Y | do(FF) = 2
= =..Sooftmaz(((F
(6)
We utilize the NWGM 44 method to approximate the aforementioned expectation. In brief, NWGM efficiently transforms the outer expectation into the Softmax function as follows:
E [=[Sfttmaz(((( (FF,
NWGM
Soffmar((E_][(F==]]]
(7)
Thus, the causal intervention is to compute the object probability under different confounder condi- tions E Z, blocking the confounding path F + Z - Y through expectation marginalization.
Confounder dictionary construction. It is extremely difficult to collect confounder images for different UAV shoot conditions and scenarios. However, by fully utilizing multi-modal knowledge, can construct and initialize the confounder dictionary through text prompts. Specifically, employ the language model GPT [I to generate texts of different confounders, such as "a photo of a in a rainy day with no occlusion from
view". The confounders include weather (sunny.rainy, foggy, nighttime), occlusion (no. partial, heavy), and perspective (front, side, rear, top). In this way, XxM we obtain K' x M prompts {PTm } m=1 where M is the number of combinations of confounders. The corresponding text embeddings Z E RS*D D2 are obtained using the CLIP text encoder [39]. These embeddings initialize the confounder dictionary, where S K . represents the number of items in the dictionary. To facilitate subsequent operations, we unify the dimensions of text and image representations as D2-
Causal reasoning. Cross-attention is performed to complete the task in Eq. 60. The selected features F are projected as query embeddings Q E x via a linear mapping, and the confounder dictionary Z is independently projected as key and value embeddings K E R x and V E RSxD= respectively. Formally,
(8) where Wq. Wk. and W, are the parameters of the linear transformation layers. bquery: Okeys and bualue are the corresponding bias values, respectively. Then we calculate the attention weight matrix A through dot product operation and apply Softmax function for normalization as
Q = Wg * F bquery, K W& * Z + bkey. V= Wp * Z + bealue.
QKT
A = so smax
VD² )
A E NxS assigns soft weights to the probability of each confounder interfering with the feature F. which allows us to approximate the expectation over confounders in the causal prediction formu- lation (7). Specifically, we model [f =)] by fusing the original feature F' with a weighted confounder context AVT. The combined representation is then transformed via learnable function fr to produce a refined feature:
F' (Cat(FFAA)))) FERNND.
(10)
This enables the network to learn confounder-aware representations while preserving task-relevant in- formation. fr E D22 is a fully connected layer, and Cat() denotes the concatenation operation. F' represents the features after applying the do-operator, corresponding [_[ =)] in Equation Subsequently, F' is passed through the classification head to obtain Softmar (E, [f (F, z)]). We interpolate F" into the feature map C₁ according the indices obtain the final feature CT. which, along with C2 and C3, is fed into the detection head.
Dictionary update. To ensure the effectiveness and representativeness of the confounder dictionary, we continuously update the items in the dictionary during forward propagation of the network. Specifically, we fuse the visual feature Fp some pixel into the most similar confounder 2; from the confounder dictionary based on the similarity as follows,
2
++1 azt + aa)F
(1I)

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Table I: Comparison of different approaches UAVDT and VisDrone. The best and second best values are highlighted in bold and red, respectively.
UAVDT
VisDrone AP50
Method
Backbone
APG0
AP75
AP
API6
FPN
22 (NeurIPS.2015)
ResNet.50
16.9
30.7
17.2
16.9
30.7
17.2
Faster R-CNN (TPAMI,2017)
ResNet-50
12.1
23.5
10.8
21.8
41.8
20.1
CascadeRCNN (CVPR,2018)
ResNet.50
17.1
30.5
18.6
23.6
38.9
24.6
ClusDet
46
(ICCV.2019)
ResNet-101
13.7
26.5
12.5
32.4
56.2
31.6
DMNet
20 (CVPR.2020)
RosNet-50
16.3
28.2
GLSAN 5
(11P:2021)
ResNet-50
17.0
28.1
18.8
30.7
S5.4
30.0
AdaZaom
21.5
40.3
Zoom&Rensoning [SP..20022))
ResNet-50
21.8
34,9
24.8
39.0
66.5
39.7
UFPMPDe [4]] (AA2).2022)
ResNet-50
24.6
38.7
28.0
36.1
57.3
38.2
EVORL [Si] (AAAL,2024)
ResNet-50
28.0
43.8
31,5
42.2
66.0
44.5
YOLOO5 [S8 (ICCV.202)
CSPDarknet
26.9
41.3
32.7
42.1
63.1
45.7
TPH-YOL OvS+ [55 (MDPI,2023)
CSPDarknet
30.1
43.5
34.3x
41.4
61.9
45.0
UAV-YOLOvs A0 (MDP1.2023)
CSPDarknet
27.3
42.1
30.4
42.7
65.5
44.7
(AAAL,2025)
CSPDarknet
34.7
MCR-UOD (ours)
CSPDarknet
31.4+1133
44.7(40.8)
35.0040.9)
44.6(+
67.3(-0.4)
47.5(+1..)
where a 0.05 is a trade-off weighting parameter and 2, is the updated confounder.
Remark. Unlike previous methods that rely solely on visual data [4] we propose multi-modal approach that constructs the confounder dictionary using are encoded via the CLIP text encoder, enabling explicit and controllable modeling of various confounders. These confounders are difficult to collect on the basis visual images. Our strategy improves the adaptability of the dictionary and improves causal reasoning in complex aerial scenarios.
prompts. These prompts
4.3 Detection Head and Loss Function
Following YOLOv8 we use a decoupled head with two 3x3 convolutions obtain the object bounding boxes (by 1 j==1 and object embedding 2, where J denotes the number of objects. Addition- ally, we replace the original classification head with text contrastive head. The class probability vector e is computed as follows,
FT
c=a
+ E||
(12)
where vy is the object embedding, E is the category text embeddings, ||- is the L2 norm. In addition, we add an affine transformation, where a is the learnable scaling factor and |3 is the learnable shifting factor. Our method follows the same end-to-end training approach YOLOv8 [40]. Additionally, to
better handle the large number of small objects in UAV images, we replace the original IoU loss with
the more effective WloU loss (37
BnB BUB"
LWIOU = =1- -1-c-r(f"w)
w(h"'")
(13)
where B and 3 represent the predicted bounding box and the ground truth bounding box, respectively, r 0.05 is fixed hyperparameter that controls the decay rate of the weight factor; ht and w" the height and width of B*, respectively. The overall training loss is
= CwioU + CDFL Less
(14)
where Las is the classification loss, always calculated using cross-entropy, and CDFL is the Distribution Focal Loss [16], which improves bounding box prediction accuracy by learning discrete distribution.
Training and test. We integrate the proposed method into the baseline and perform end-to-end training. During both training and inference, the parameters of the CLIP text encoder are frozen, while the confounder dictionary is continuously updated.
Experiments
Experiment setup. Three public datasets are used for aerial image object detection: VisDrone X
UAVITT 77) and HRSC2016 (26). VisDrone contains 8599 drone-captured images (2000x 1500 pixels), split into 6471 for training, 548 for validation, and 1580 for testing. It includes 10 object
7

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Table 2: Comparison on HRSC2016.
Table 3: Ablation study of MCR-UOD.
Method
AP
Method
AP
Method
AP
AP50
AP75
R2PN(GRSL'T8
70.06
REDICVPR'18 18)
84.30
YOLOv8
42.2
44.5
RaIT(CVPR119 19)
86.20
2DDE(AAA
89.26
+WIOU
42.5(+0.3)
65.2(+0.5)
44.7(+0.2)
CSL/ECCV'20 20)
89.62
ReDeiCVPR 22)
90.46
+WIOU+LORE
43.6(+1.1)
66.5(+1.3)
45.9(+1.2)
FSM[TPAMI 25)
91.60
MCR-UOD(ours)
92.04
MCR UOD
44.6(+1.0)
67.3(+0.8)
47.5(+116)
categories, mainly vehicles and pedestrians. UAVDT designed for object detection and tracking, comprising 24143 training images and 16592 testing images x1024x540 pixels). It features diverse aerial scenes and is widely used for detecting cars, trucks, and buses. HRSC2016 contains high- resolution aerial images focused on ship detection, featuring large-scale variations and complex backgrounds. Object detection models are evaluated using standard metrics 23), including AP (Average Precision), AP50 and AP75. We chose YOLOv8 [40] as the backbone of our method and performed all training and validation on two NVIDIA FeForce RTX 3090 GPUs. The number of training epochs is 75, with batch size of 4. The initial learning rate Iro is 0.001; the final learning rate y is 0.01; and the weight decay is 0.0005.
5.1 Comparison with State-of-the-art Methods
Compared methods. To validate the effectiveness of our method, we compare it with several state-of-the-ard approaches proposed recent These methods can be categorized according to the descriptions in the related works section as follows: zoom-in strategy based methods in-
cluding ClusDet [46], DMNet (20), UFPMP [14), Adazoom 43 Zoom& Reasoning [TO; feature
representation enhanced methods such as FPN [22],
YOLL [58) YOL-5500 (55)
SPAR [9] ReDet 12 RolT R2PN (53 FSM 24 PRD 21 and data augmentation based method
PAOD
3
FSM
24
R Det
48 and CSL
Quantitative comparison. Table presents a summary of the comparison results between our method and nine state-of-the-art approaches on the UAVDT and VisDrone datasets. In terms of three key evaluation metrics, the proposed method significantly outperforms Specifically, on the VisDrone dataset the proposed MCR-UO -OOD method achieves an AP of 44.6, representing a 1.8% improvement over the previous best pest-performance model SPAR; On the UAVDT dataset, MCR-UOD achieves an AP of 31 31.4, outperforming TPH- by 1.3 points. Our method achieves an AP50 of 44.6, exceeding SPAR by 0.8 points, and an AP75 of 35.6, outperforming TPH-YOLOv5+++ by 0.9 points. These substantial improvements on three metrics demonstrate that our proposed method excels not only in recognizing object regions but also in achieving accurate localization. This indicates that the model is capable of learning more discriminative and fine-grained features, leading to more precise bounding-box regression. Furthermore, on the HRSC2016 dataset, as shown in Table MCR-UOD achieves an mAP of 13, outperforming all existing methods. All gains come from the LGRE module for precise region attention and the backdoor adjustment for removing confounding factors, which enhances feature robustness in complex scenes.
models compared.
Visualization comparisons. Fig. 4 provides a comprehensive visualization of the performance the MCR-UOD method on UAVDT and VisDrone, column (a) shows the original images, while columns (b) and (c) present the detection results of previous state-of-the-aae methods, UFPMP and SPAR, respectively. Column (d) illustrates the detection results of our proposed method, MCR-UOD, The regions marked with red circles indicate areas where the previous methods failed to detect objects. In the first row, a truck in a very dark lighting condition is completely missed by the previous methods, but MCR-UOD successfully detects it. the second row, due to overexposure the image, the car is difficult to detect using previous methods, while our method correctly identifies it. In the third row, UFPMP and SPAR miss several small objects due to heavy occlusion and their tiny sizes. In contrast, MCR-UOD successfully identifies these small targets. These improvements can be attributed to the core design of MCR-UOD, by integrating vision-language models and causal inference, the method effectively utilizes contextual knowledge and systematically eliminates confounding factors such as lighting and viewpoint changes.
5.2 Further Studies
Ablation study. Ablation experiments are conducted on VisDrone dataset, as shown in Table 3 Yolov8 [40] the baseline model; "+WIOU" the baseline with WloU loss; "+WIoU+I.GRE" de- notes using WIOU loss and LGRE module; "MCR-UOD" denotes the complete method. From Table
8

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L3
(a) Images
(b) UFPMP
(c) SPAR
(d) MCR-UOD
Figure 4: Visualization comparison. (a) shows the original image, while (b), (c), and (d) present the detection results of UFPMP, SPAR, and our proposed MCR-UOD method, respectively. The red circles highlight the objects missed by previous state-of-the-art methods but successfully detected by MCR-UOD. The yellow circles indicate false detections. Zoom in for best view.
Table Performance and efficiency comparison across different YOLO backbones.
Method
AP
AP50
AP75
Parameters
GFLOPs
YOLOv8s
38.2
56.9
41.3
11.14M
28.7
YOLOv8m
40.7
59.6
42.5
25.8M
79.1
YOLOv8I
41.1
62.8
44.0
43.6M
165.4
YOLOv8x
42.2
64.7
44.5
68.2M
258.2
YOLOv&+MMR-UOO)
40.1
60.3
44.9
10.6M
28.4
YOLOv8m=MCR-UOO
21.8
41.8
20.1
23.7M
77.6
YOLOV8L-MCR-UOD
43.2
66.4
45.9
39.5M
159.2
YOLOVBX+MCR-UOD
44.6
47.5
61.5M
247.6
3 can be concluded that all modules contribute positively to the final performance. Specifically, the combined use of L.GRE and BACR modules results in significantly improved performance compared to baseline; the performance values in terms of AP, AP50 and AP75 in VisDrone are all increased.
Computational efficiency. In integrating method with the backbone, modify and remove parts of the C2f module, replace the detection head with a text contrastive detector, achieve a lightweight conducted across YOLOv8 variants
design. Please refer the appendix for details. Experiments (n/s/m/l/x) on VisDrone dataset to explore the speed-accuracy trade-off under different backbone scales. As shown in Table 4 present the speed and accuracy comparison between models of different sizes. From the table, it evident that compared to the YOLOv8 baseline, the proposed MCR-UOD achieves faster inference speed and higher detection accuracy across various backbone scales, demonstrating a better trade-off between performance and efficiency. More experimental results are shown in the appendix.
1-SNE visualization of the BACR module. To verify the effectiveness the BACR module, we visualize category-wist features on the VisDrone dataset using t-SNE 38 shown in Fig. 5 Without BACR, features of the same category are scattered and easily confused due to diverse UAV imaging conditions. In contrast, with BACR, intra-class features become more compact and inter-class boundaries clearer, demonstrating improved feature discrimination for UAV object detection.
Sensitive analysis. In our method, there are not many parameters. The only adjustable parameter is 7 in (5). To investigate impact of different values 7 on detection performance, we conducted parameter sensitivity analysis shown in Figure 6 We systematically tested 7 values within the

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(a) Without BACR
(b) With BACR
1,0
1.0
0.6-
0.6
0.0
0.0
(a) Without BACR
(b) With BACR
Figure 5: Visualization of t-SNE with and without BACR module.
Sensitivity Analysis Threshold
80
AP
AP50
AP75
70
60
Optimel 0.55
50
40
30
0.40
0.45
0.50
0.55
0.60
0.65
0.70
Threshold r
Figure 6: Sensitivity analysis of threshold
range [0.4, 0.7]. The results show that both excessively small and large T values lead to reduced accuracy. Overly small values introduce more false negatives by including inaccurate regions, while overly large J values produce false positives by missing valid detection areas. The optimal performance is achieved 5=0.55, which consequently selected for our experiments. At the same time, we can also observe that as the value changes, the performance changes are also flat. It confirms that the value of r not very sensitive to performance.
Conclusion
We proposed a new UAV object detection method, called MCR-UOD, which improves the perfor- mance of UAV object detection through causal inference and multimodal learning. The framework comprises two key modules: LGRE and BACR. LGRE module leverages a pre-trained vision- language model, such CLIP text embeddings compute text-guided attention maps for high- lighting possible object regions. The BACR module maintains dynamic confounder dictionary for causal intervention. Due to the inability to obtain diversity UAV images under various envi- ronmental and imaging conditions, the confounder dictionary is constructed and initialized with Clip text embeddings. Backdoor adjustment is applied based on this confounder dictionary that can reduce the influence of confounding factors, thus the extracted object features are imaging condition-invarian and more robust. Experimental results demonstrate that the proposed MCR-UOD outperforms existing methods while maintaining computational efficiency.
10

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Acknowledgments and Disclosure of Funding
This work was supported by the National Natural Science Foundation of China (62276048, 62476169, 62476049).
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A Illustration of Backdoor Adjustment
Backdoor adjustment is method of causal inference used to eliminate confounding bias. The key idea is to adjust set of confounders Z that block all non-causal paths (ack door) between treatment X and outcome Y, allowing estimation of the causal effect X on Y.
A.I Backdoor Adjustment Formula
If the variable set Z satisfies the backdoor criterion, the causal effect of X on Y (Average Causal Effect, ACE) can be estimated using:
P(Y = y]do(X)) =
= 2)
(15)
where the Z must satisfy the backdoor criterion: 1) Blocks all backdoor paths: Z must block every path between X and Y that contains an arrow into X. 2) No new bias introduced: Z must not include any descendants X.
A.2 Derivation Process.
The backdoor adjustment is derived using causal graph rules and probability theory, with the following key steps: From intervention to Conditional Probability. The intervention do(X=x) corresponds to removing all incoming edges to in the causal graph and fixing X = In this intervention, the distribution of Y depends only X and its parents. If Z satisfies the backdoor criterion, the post-intervention distribution can be expressed as:
x, PoY|do(X =x)) = EP = 2= 2) P(Z 2|do(X) x)
(16)
Since do(X=x) does not affect Z(because Z is not descendant of X), have P(Z 2 xX = x))= = P(z = 2) leading to:
P(Y|do(X = x)) = EP 2 2) P(Z
x))
(17)
A.3 Confounder Dictionary Construction.
Collecting confounder images in 218 different images and UAV conditions is extremely challenging. To address this, we fully leverage multi-modal knowledge by constructing and initializing the confounder dictionary using textual prompts. Specifically, we employ the large language model GPT [1] to generate descriptive texts for various confounders, such as "a photo of a car on a rainy day without occlusion from a rear view." The confounders include weather conditions (sumny, rainy, foggy, nighttime), occlusion levels (none, partial, heavy), and viewing perspectives (front, side, rear, top). In this way, we systematically generate linguistic priors for confounder modeling, thus providing rich semantic support for downstream tasks, as shown in T
More Experiment Results
Evaluation metries. To evaluate the detection performance of our proposed enhanced model, we use several metrics: AP, AP50 and AP75 99 23). The following parameters are utilized: TP (true positives), FP (false positives), and FN (false negatives). Intersection over Union (IoU) measures the overlap between the predicted bounding box and the ground truth box. Precision is defined the ratio of true positive predictions to the total number of detected samples, calculated as follows:
TP
Precision
(18)
TP + FP
Recall represents the ratio of true positive predictions the total number of actual positive samples, calculated as:
TP
Recall
(19)
TP FN
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Table 5: The 36 prompt templates used in our method, each describing a [CLS] token in various UAV imaging conditions including weather, occlusion, scale, and viewpoint.
Pronpt
Template
[CLS] sunny scene with no occlusion, viewed from front at a large scale.
[CLS] sunny scene with no occlusion. viewed from the side a medium scale.
a [CLS] sunny scene with no occlusion, viewed from the rear at a small scale.
with partial occlusion, viewed from the lop a large scale. a [CI S] sunny scene with partial occlusion, viewed from the front at medium scale.
[CLS] sunny
[CLS] sunny scene with purtial occlusion, viewed from the at small scale.
[CLS] in sunny scene with heavy neclusion, viewed from the rear at large scale.
[CLS] sunny scene with heavy occlusion. viewed from top at medium scale.
with heavy occlusion, viewed from the front small scale. 10 a [CLS] rainy sceme with no acclusion. viewed from the side al large scale.
[CLS] sunny
11
with no acclusion, viewed from the rear a [CLS] in rainy seene with no occlusion, viewed from the top at small scale.
[CLS] miny
medium scale.
12
13
[CLS] many scene with partial occlusion, viewed from the front at large scale.
14
[CLS] miny seene with partial occlusion, viewed from the side at a medium scale.
15
[CLS] rainy socne with partial occlusion, viewed from the rear at a small scale.
16
[CLS] miny scene with heavy occlusion, viewed from the top a large scale.
17
[CLS] rainy sceme with hewvy occlusion, viewed from the fromt at medium scale,
18
[CL miny
with heavy occlusion, viewed from the side [CLS] in foggy scene with no occlusion, viewed from the rear at a large scale.
small scale.
19
20
[CLS] foggy scene with occlusion, viewed from top
medium scale.
21
[CLS] foggy scene with occlusion, viewed from the front at a small scale.
22
[CLS] in foggy sccno with pertial occlusion. viewed from the side at large scale.
23
[CLS] loggy
with partial occlusion, viewed from the [CLS] foggy scene with partial occlusion, viewed from the top at a small scale.
medium scale.
24
25
[CLS] foggy scene with heavy occlusion, viewed from the front a large scale.
26
[CLS] in foggy sccne with heavy occlusion. viewed from the side a medium scale.
27
[CLS] foggy scene with heavy occlusion, viewed from rear a small scale.
28
[CLS] in night scene with no occlusion, viewed from the top at large scale.
29
[CLS] in night sceme with no occlusion. viewed from the front a medium scale.
30
[CLS] night scene with occlusion, viewed from the side
small scale.
31
[CI S] in night sceme with partial occlusion, viewed from the rear at a large scale.
32
[CLS] night scene with partial occlusion, viewed from the top modium scale.
33
[CLS] in night scene with partial ecclusion. viewed from the front at small scale.
34
[CLS] night sceme with heavy occlusion, viewed from the
large scale. medium scale.
35
&CCLS] night scene with heavy occlusion, viewed from the
36 a|CLS] in night sceme with heavy occlusion, viewed from the top at a small scall.
The average precision (AP) is the area under the precision- curve, computed by:
Precision(Recal) d dRReaall) AP L.'
(20)
Mcan average precision (mAP) is obtained by averaging the AP values across all sample categories to measure the model's performance across all categories:
N AP
mAP=
(21)
Here, AP, represents the AP value for category i, and N is the number of categories in the training dataset (in this paper, N = 10). AP50 denotes the average precision when the IoU threshold is set to 0.5, while AP75 represents the average precision over IoU thresholds to 0.75.
Confusion matrix. From Fig. it can be seen that the diagonal region of the confusion matrix for MCR-UOD is darker in color compared to YOLOv8, indicating that our proposed method has improved the model's ability to correctly predict object categories. This improve- ment is particularly notable when detecting smaller objects, such as bicycles, tricycles, and triieess where our method outperforms YOLOv8, Although there are still some missed detections for these smaller objects in complex backgrounds, our method signifi- cantly reduces the proportion of objects misclassified as background compared to YOLOv8. Bicycles, tricycles, and awning swming-tricycles often appear in dense or occluded environments, making detection in complex backgrounds challenging. Our method improves the feature extraction ability and classification mechanisms of the model, leading to better detection performance and reduced missed detection rates for these small objects. Although the percentage of correctly predicted small objects still needs improvement, our method shows
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Contuston Matris Normalized
padestrion
44
people 0:91
0:91
0.32
0:35
people 0.85
0.55
0.01
9.32
6-02
0.23
0.02
0.00
-0A
0.87
8:10
0.07
0.04
455
-03
Proficted
VIIY
tricycle
0.09
4:40
0.38
9.04
trieysic
0.40
0.18
0.11
awming-trfeycle
0.65
-03
6:01
6.00
9.00
0.50
barkground
background (027 a.14
6:40
0.39
0.31
0.17
0.14
8:20
0.23
poople
MoyUl
(a) YOLOvB
(b) MCR-UOD
Figure 7: (a) Confusion matrix plot of YOLOv8; (b) confusion matrix plot of our model.
Image
YOLOVB
UFPMP
SPAR
MCR-UOD
Figure 8: Visualization of feature maps.
a notable advancement in performance over the traditional YOLOv8 model in complex scenarios.
Visualization of feature maps. The heatmap visualization of feature maps, shown in Fig. 8 highlights the superior performance of the MCR-UOD method compared to YOLOV8, SPAR 19 and UFPMP 14] The MCR-UOD heatmaps demonstrate more precise and concentrated activation areas, especially for small objects. This indicates a more refined understanding and localization of critical features in the image. In contrast, UFPMP and SPAR, the previous -fate-of-the--e art methods, while effective, show less focus on these smaller targets. This suggests that MCR-UOD particularly adapted to capture essential information, leading to enhanced detection and classification performance, especially in scenarios involving small objects.
Precision-Confidence curve. Fig. lefft) presents the Precisoo-Connideen (PC) curves for the MCR-UOD method, the baseline YOLOv8 and the sfate-of-the-arr SPAR and UFPMP: The MCR-UOD curve consistently demonstrates high precision across various confidence thresholds, indicating its effectiveness in redueing false positives. contrast, UFPMP and SPAR exhibit more variability, reflecting less precision stability with changing confidence levels. The smooth and upward trend of the MCR-UOD curve highlights its superior performance and robustness, maintaining high true positive rate as confidence increases.
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Precislon-Confidecce Curve
Precision-Reche Curve
MCR-U0D
1.0
MCB-URK
SPAR
UnTer
0.
YOLOVB
Precsion
Precision
0.4
0.4
0.0
0.0
0.2
044
1.0
0.0
0.2
0.8
1.0
Confidence
Recall
Figure 9: Comparisons of Precision-Confidenee and Precision-Recal curves between MCR-UOD and other SOTA methods.
Table 6: Statistical significance (p-values) of performance differences between MCR-UOD and SPAR.
Metric
SPAR (Mean + Std)
MCR-UOD (Mean + Std)
p-value
AP50
43.90 + 0.25
44.70 0.31
0.018
AP75
34.70 = 0.28
35.60 0.34
0.015
This comparison underscores the effectiveness of MCR-UOD in balancing precision and confidence.
Precision-Recall curve. Fig. rigght) presents a comparative analysis of Precision- Recall (PR) curves for the MCR-UOL method, YOLOv8, SPAR and UFPMP. The PR curves clearly illustrate the performance of each model at different recall levels. Our MCR-UOD method consistently demonstrates superior precision compared to YOLOv8, SPAR and UFPMP various recall rates. This indicates that the MCR-UOD method is more effective in minimizing false positives while maintaining high recall performance. In particular, the PR curve for MCR-UOD higher than those of other methods in the recall spectrum, reflecting its improved accuracy and robustness in object detection. The area under the PR curve (AP) for MCR-UOD significantly larger than that of YOLOv8, SPAR and UFPMP, further validating the effectiveness of our method. This improvement in AP underscores MCR-UOD's ability to achieve better precision and recall balance, particularly detecting small objects and handling imbalanced datasets. Overall, the comparison reveals that MCR-UOD not only surpasses YOLOv8, SPAR and UFPMP in precision but also offers a reliable detection performance. This indicates that the proposed MCR-UOD method provides substantial enhancement in object detection capabilities, making it more suitable for practical applications where high precision and recall are critical.
Statistical verification. To further validate the performance advantage of our proposed MCR-UOD framework, conducted statistical significance test against SPAR using the Wilcoxon signed-rank test, as shown in Tabel 6 This test, widely used for paired comparison without assuming data normality, allows improvements are statistically meaningful. We perform the analysis on the UAVDT dataset using two key evaluation metries: AP50 and AP75. The computed p-values reported in the corresponding table. Notably, both p-values are well below the 0.1 significance level, providing strong evidence that the performance gains of MCR-UOD to random variation. These findings confirm the robustness and consistent superiority of our causal reasoning approach to enhance UAV-based object detection.
assess whether the obscrved
SPAR not due
Model Architecture with YOLOv8
We implemented the proposed MCR-UOD method based on the YOLOvv8 detection frame- work. The overall architecture is illustrated in Figure 10 YOLOv8 adopts modern and
25

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SPPF
(e) detect
input
CBS
Cony
Conv
Conv2d
Bboskoots
MaxPool
MaxPool
MaxPool
CBSModule
Conv2d
Contractive Loss
CBSModule
CBS
C2f
Concat
MCR-UDD
Detect
CBSModule
Upsample
14
CBSModule
17
Text
Reasoning
LGRE
Text
CBSMadule
BatchNorim2d
Concat
11
CasModule
CBSModiule
BACR
Upsample
10
Concat
Text
SPPF
C21
22
Detect
(a) Backbone
(b) Neck
(c) Head
(1)
MCR-UOD
(g) CBS
Figure 10: The network structure of YOLOv8 with MCR-UOD. The (width) and (ratio) are parameters used to represent the size of the feature map. The size of the model can be controlled by setting the values of w and to meet the needs of different application scenarios.
streamlined structure composed of a backbone, neck, and detection head, offering improve- ments both detection accuracy and speed over previous YOLO versions such YOLOv5 and YOLOv7.
In our implementation, we retain the original backbone of YOLOv8 and focus modifying the neck and detection head incorporate our MCR-UOD strategy. As highlighted in the red box in Figure TO we replace the last C2f module processing the low-level feature map before the head with customized version. Specifically, the input feature C₁ is passed through two newly designed modules: LGRE and BACR. The output of this process, denoted as CT. is then fed into the detection head.
Furthermore, we replace the original classification head with contrastive head based on text embeddings, shown in the figure. This change enables the model to perform self-prompted open-set recognition by leveraging text-based semantic information, allowing its potential generalization unseen object categories.
D Limitations
Our method uses multimodal knowledge and causal reasoning to improve object detection UAV imagery. Although shows promising results, there limitations. First, relying on CLIP for semantic guidance limits performance due to its representational capacity, particularly in low-quality or ambiguous images. In addition, prompt design is based on intuition and heuristics, limiting adaptability. Second, the integration of causal reasoning with object detection is still in the early stages. Although we use structural equation models for causal modeling, more research is needed to better link causal structures with image features, especially complex environments.
26


Reviews:
[{"review_id": "yCGoxtbalb", "reviewer": "Reviewer_dsXu", "summary": "The paper introduces MCR-UOD, a UAV object detection framework using causal reasoning and multimodal learning. It includes LGRE for text-guided object region highlighting and BACR for robust feature extraction via causal interventions. MCR-UOD achieves state-of-the-art performance on VisDrone and UAVDT datasets while maintaining computational efficiency.", "questions": "1. Ablation Study: Does the Backdoor Adjustment Causal Reasoning (BACR) still improve performance if the top-N selection mechanism is removed?\n2. Ablation Study: If the mean CLIP feature is not updated, does BACR still enhance performance?\n3. What is the source of performance improvement—classification accuracy or more precise bounding box regression? What specific problem does the proposed method solve?\n4. The YOLOv8 baseline in the paper uses category text embeddings. How does it compare to a learnable classifier (as in traditional detectors), and why were category text embeddings chosen?\n5. Is there any other CLIP enhance detector could chose as a more fair baseline?", "limitations": "No. The authors should discuss whether the work and its application methods remain generalizable to other tasks or categories.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. The writing is polished and lucid, ensuring high readability.\n2. Proposes a feature enhancement module: Language-Guided Region Exploration (LGRE), which leverages CLIP text features of given classes to filter feature maps, making them more sensitive to object presence.\n3. Introduces Backdoor Adjustment Causal Reasoning (BACR): Constructs a mean CLIP text feature for the target class across diverse conditional texts (e.g., different weather conditions), enhances image feature maps via cross-attention, and updates the mean feature using a running mean method in conjunction with image features.\n4. The method achieves state-of-the-art (SOTA) performance, demonstrating significant improvements over the baseline.\n5. The ablation study is relatively clear.\n\nWeaknesses:\n1. Ablation Study: Does the Backdoor Adjustment Causal Reasoning (BACR) still improve performance if the top-N selection mechanism is removed?\n2. Ablation Study: If the mean CLIP feature is not updated, does BACR still enhance performance?\n3. What is the source of performance improvement—classification accuracy or more precise bounding box regression? What specific problem does the proposed method solve?\n4. The YOLOv8 baseline in the paper uses category text embeddings. How does it compare to a learnable classifier (as in traditional detectors), and why were category text embeddings chosen?\n5. Is there any other CLIP enhance detector could chose as a more fair baseline?", "was_revised": true, "final_justification": "The authors' response has addressed most of my concerns. Overall, this is a rather interesting piece of work. I decide to keep my score. I did not further increase the score because, in my view, the most common approach to handling different scenarios in practical applications is to augment data related to specific aspects. The method of extracting features using language models, which requires alignment in the feature space, generally yields inferior performance compared to the former. This limits the deployment of this work in real-world scenarios. However, the idea behind this work is still quite interesting, so I have kept the score unchanged overall.", "rebuttal": "We thank the reviewer for the very encouraging comments on the originality of MCR-UOD, the effectiveness of MCR-UOD, and the overall good presentation. We hope to provide satisfying answers to the concerns raised. We kindly ask you to consider raising the score if possible . Thank you!\n\n**Q1: Ablation Study of top-N selection mechanism.**\n\nA: We added ablation experiments on VisDrone to assess the top-N selection mechanism, as shown in Table 1. Even without it, the model still improves performance.\n\nTable 1. Ablation study on whether using top-N selection mechanism.\n| Methods|AP|AP50|AP75|\n|:-:|:-:|:-:|:-:|\n|YOLOv8|42.2|64.7|44.5|\n|w/o Top-N|43.8|66.6|46.4|\n|**Top-N selection**|**44.6**|**67.3**|**47.5**|\n\n\n**Q2: Ablation Study on whether updating mean CLIP feature.**\n\nA: We added an ablation study on whether to update the CLIP features. As shown in Table 2, experimental results show that without updating the mean CLIP features, BACR can still improve performance. However, the improvement is slightly weaker compared to the setting where the feature dictionary is updated.\n\nTable 2. Ablation study on whether updating mean CLIP feature.\n|Methods|AP|AP50|AP75|\n|:-:|:-:|:-:|:-:|\n|YOLOv8|42.2|64.7|44.5|\n|w/o Updated|44.2|66.8|46.8|\n|**Updated Feature**|**44.6**|**67.3**|**47.5**|\n\n\n**Q3: What is the source of performance improvement—classification accuracy or more precise bounding box regression? What specific problem does the proposed method solve?**\n\nA: We conducted a more detailed analysis on experimental results which confirm that both classification accuracy and bounding box regression contribute to the performance gain. As shown in Table 3, the reduction in cls_loss indicates improved classification accuracy, while the decreases in box_loss and dfl_loss reflect more precise bounding box regression. These are further supported by the higher precision and recall, showing an overall enhancement in detection quality.\n\nTable 3. Analysis on performance improvement.\n|Methods|box_loss|dfl_loss|cls_loss|precision|recall|\n|:-:|:-:|:-:|:-:|:-:|:-:|\n|YOLOv8|1.14967|1.03289|0.61914|0.67492|0.61999|\n|MCR-UOD|1.07320|1.01879|0.60486|0.69434|0.63125|\n\n\n**Q4: The YOLOv8 baseline in the paper uses category text embeddings. How does it compare to a learnable classifier (as in traditional detectors), and why were category text embeddings chosen?**\n\nA: (1) We compared the baseline using a learnable classifier with our method based on CLIP classification. As shown in Table 4, using CLIP text embeddings for classification yields a noticeable performance improvement over the learnable classifier.\n\nTable 4. Performance comparison of learnable classifier with CLIP text embeddings.\n|Methods|AP|AP50|AP75|\n|:-:|:-:|:-:|:-:|\n|YOLOv8(learnable classifier)|41.3|64.2|43.1|\n|YOLOv8(CLIP)|42.2|64.7|44.5|\n\n(2) We chose CLIP text embeddings because text and image features of the same class are tightly clustered, while those of different classes are clearly separated as CLIP model is pretrained on millions of text-image pairs. By employing these embeddings in both the LGRE and BACR modules and optimizing classification loss end-to-end, the model better integrates semantic information and enhances feature consistency.\n\n**Q5: Is there any other CLIP enhance detector could chose as a more fair baseline?**\n\nA: We have added comparison results with YOLO-World[1] and YOLOE[2], which are also CLIP enhanced YOLO detectors. As shown in Table 5, our method achieves better detection performance compared to other CLIP enhanced detectors.\n\nTable 5.Comparison of different CLIP enhanced detectors.\n|Methods|AP|AP50|AP75|\n|:-:|:-:|:-:|:-:|\n|YOLOv8|42.2|64.7|44.5|\n|YOLO-World|42.8|65.0|44.9|\n|YOLOE|43.1|65.4|45.3|\n|**MCR-UOD (ours)**|**44.6**|**67.3**|**47.5**|\n\n**Q6: Limitations:No. The authors should discuss whether the work and its application methods remain generalizable to other tasks or categories.**\n\nA: Thank you for the suggestion. We would like to clarify that the limitations of our method are discussed in the appendix (lines 128–135). Regarding generalizability, although our framework is developed for UAV detection, its core components, such as multimodal semantic guidance and causal reasoning, are not restricted to this task. Theoretically, our method can also be applied to other scenarios, including transfer learning and zero-shot detection. We consider moving this discussion to the main text for easier reader access.\n\n**References**\n\n[1]Cheng T, et al. “Yolo-world...”.CVPR2024\n\n[2]Wang A, et al. “Yoloe...”. ICCV2025"}, {"review_id": "BVOIkTgKNA", "reviewer": "Reviewer_Wk5t", "summary": "This paper introduces multimodal Causal Reasoning framework based on YOLO backbone for UAV Object Detection (MCR-UOD). The approach to utilizes a vision-language model as part of the detection model to exploit multimodal knowledge and better understand and correlate visual and textual information.", "questions": "- In page 7 lines 285-286 the initial learning rate is reported as 0.001 and the final is 0.01. Usually, the learning rate reduced over the duration of the training. Is this a typo?\n- With reference to Table 4. Why does the parameters of the MCR-UOD models are lower than the original models? Don’t the language model parameters increase the total count?", "limitations": "No discussion of limitations in the main manuscript but in supplementary. Perhaps a discussion on the impact of biases introduced because of the language model could be useful.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n- The language guided approach to enhance object localization is interesting and effective in improving the average precision metrics.\n- Common datasets are used for evaluation such as UAVDT, HRSC2016, and VisDrone.  \n- Comparisons are also made with a number of established methods. \n- The presented approach with the incorporation of CLIP text encoder seems to be easily adaptable to other settings. \n\nWeaknesses:\n\n- The incorporation of CLIP with detection models is not new and has been demonstrated in other works. \n- The connection with UAV-based visual data is weak and in this regard it is not clear why the particular application domain was selected. \n- While the comparisons with vision models are adequate, the paper is missing comparisons with text based YOLO models such as (Yolo-world CVPR, YOLOE).", "was_revised": true, "final_justification": "The authors have included additional experiments with related work and have demonstrated better their motivation for this work. For this reason I have raised my score.", "rebuttal": "Thank you for your constructive feedback and for acknowledging the strengths of our work. We have carefully addressed the concerns raised and sincerely hope our responses clarify the contributions and improvements made. We would greatly appreciate your consideration of a higher score if you find the revisions satisfactory.\n\n**Q1: The incorporation of CLIP with detection models is not new and has been demonstrated in other works.**\n\nA: We would like to clarify our contribution is not a simple fusion of CLIP and detection models. (1) CLIP usage is different. Unlike previous works that focus on integrating CLIP directly into detection models, our approach leverages CLIP more as an auxiliary tool to enable causal reasoning in challenging UAV scenarios. Our core idea is to utilize a vision-language model to generate embeddings of diverse textual descriptions, which are then used to construct a confounder dictionary. This dictionary facilitates causal reasoning to address inter-class inconsistencies caused by complex scenes and scale variations in UAV data. So our method is independent to CLIP at inference stage. Of course, other VLMs can also be used instead of just CLIP. \n\n(2) Our innovation lies in introducing a causality-aware framework that goes beyond fusion of CLIP. By embedding language semantics into LGRE and and causal reasoning(BACR), the method reduces visual ambiguity and improves generation, especially for small or confusing UAV objects. This presents a novel and effective use of VLMs beyond conventional fusion-based approaches. \n\n\n**Q2: The connection with UAV-based visual data is weak and in this regard it is not clear why the particular application domain was selected.**\n\nA: We appreciate the reviewer’s comment and would like to clarify the motivation behind choosing the UAV-based visual domain. As mentioned in lines 24-32 on the first page, UAV scenarios are characterized by complex scenes, frequent scale variations, and a high degree of inter-class visual ambiguity, which are challenges that often degrade detection performance. Our framework is specifically designed to address these issues through causality-aware reasoning and enhanced semantic alignment. Therefore, UAV-based detection is not only a representative but also a highly relevant application domain for validating the effectiveness of our method. Experimental results on VisDrone and UAVDT further support this choice. \n\n**Q3: While the comparisons with vision models are adequate, the paper is missing comparisons with text based YOLO models such as (Yolo-world CVPR, YOLOE).**\n\nA: Thank you for your valuable comments. We have added comparison experiments with YOLO-World[1] and YOLOE[2].The results demonstrate that our method still achieves superior detection performance compared to other text-based YOLO detectors.\n\nTable 1. Comparison of YOLO-World and YOLOE.\n|Methods|AP|AP50|AP75|\n|-|-|-|-|\n|YOLOv8|42.2|64.7|44.5|\n|YOLO-World|42.8|65.0|44.9|\n|YOLOE|43.1|65.4|45.3|\n|**MCR-UOD (Ours)**|**44.6**|**67.3**|**47.5**|\n\n**Q4: In page 7 lines 285-286 the initial learning rate is reported as 0.001 and the final is 0.01. Usually, the learning rate reduced over the duration of the training. Is this a typo?**\n\nA: We apologize for the confusion caused by this typo, we will correct it in the paper. In our experimental settings, we set lr0 (the initial learning rate) to 0.001 and lr1 to 0.01, resulting in a final learning rate of lr0*lr1.\n\n**Q5: With reference to Table 4. Why does the parameters of the MCR-UOD models are lower than the original models? Don’t the language model parameters increase the total count?**\n\nA: Thank you for your valuable comments. As stated on page 8, line 329 (Main text), and page 6 (Appendix), line14. The reduction in model parameters primarily stems from compression both visual and text features to a unified channel dimension (256), whereas YOLOv8 uses higher dimensions (1024 and 512) in its detection head. This adjustment leads to a more lightweight structure. Importantly, despite the lower parameter count, MCR-UOD maintains competitive detection performance, effectively meeting the demands of UAV-based detection tasks. We will provide additional details in the relevant sections.\n\n**Q6: Limitations: Perhaps a discussion on the impact of biases introduced because of the language model could be useful.**\n\nA: Thank you for the suggestion. We agree that discussing potential biases from the language model is important and will add this in the revised version. Such bias often stem from imbalanced or incomplete training data, which may affect semantic guidance and lead to skewed reasoning in some scenarios. However, systematically analyzing these bias is challenging, as it requires controlled experiments, access to training data distributions, and clear fairness definitions across tasks and categories. While these bias exists, large scale dataset pretraining allows the model to capture useful semantics, particularly for common UAV categories like vehicles and pedestrians, where it has helped improve detection. \n\n&nbsp;\n\n**References**\n\n[1] Cheng T, et al. “Yolo-world...”.CVPR2024.\n\n[2] Wang A, et al. “Yoloe...”. ICCV2025."}, {"review_id": "0zag9qAEfs", "reviewer": "Reviewer_Kr7U", "summary": "This paper presents MCR-UOD, a method for the UAV object detection. The proposed method has two major components: LGRE and BACR. The LGRE part leverages a pretrained text encoder from CLIP model, to compute the attention maps for possible object regions. The BACR part maintains a dynamic confounder dictionary for causal reasoning. Empirical experiments on several benchmarks show the promising results of the proposed method, including performance and efficiency.", "questions": "1. One question is - how is the importance of the pretrained CLIP model in proposed method? if changing the CLIP models to a different pretrained models, since two parts of the framework depend on CLIP text encoder. \n\n2. What is the difference between Yolov8 in Table 3 and UAV-YOLOv8 in Table 1?", "limitations": "No limitation section for the submission.", "rating": 4, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\n\nThe proposed method leverages the pretrained text encoder from the CLIP model, and dynamic confounder dictionary for causal reasoning, demonstrating promising results on UAVDT and VisDrone, and also showing efficiency. \n\n\nWeaknesses:\n\nInterestingly, it looks Yolov8 is already a very strong baseline for VisDrone, if you directly compare the results from Table 3 with Table 1.  One potential weakness of the paper is the novelty, overall, it is more like an okay, incremental work for the UAV object detection.\n\n\n(Sorry, I am not familiar with the literature of this topic.)", "was_revised": true, "final_justification": "I read the response and other reviews, thanks for the response, stick to previous score.", "rebuttal": "Thank you for your thoughtful review and for recognizing the merits of our work. We have carefully addressed the concerns raised and hope that our answers meet your requirements. We kindly ask you to consider raising the score if possible . Thank you!\n\n**Q1: Interestingly, it looks Yolov8 is already a very strong baseline for VisDrone, if you directly compare the results from Table 3 with Table 1. One potential weakness of the paper is the novelty, overall, it is more like an okay, incremental work for the UAV object detection.**\n\nA: We would like to summarize the significance and novelty of this work again (please also see our responses to reviewer nLTh):\n(1) We agree that YOLOv8 is a strong baseline on the VisDrone dataset, which makes further improvements on top of it particularly challenging. Nevertheless, our method enhances this baseline with causal reasoning and a lightweight architecture. This combination helps to effectively reduce inter-class inconsistency caused by complex scenes and scale variation. As shown in Table 1, our approach achieves significant improvements in both detection accuracy and inference speed.\n\n(2) We would like to take this opportunity to reiterate the key innovations of our work. Unlike incremental improvements that simply build upon existing methods, our approach introduces a novel causality-aware reasoning framework tailored specifically for the unique challenges in UAV object detection, such as inter-class inconsistency caused by complex scenes and scale variations. This fundamental shift in methodology, combined with lightweight design choices, distinguishes our work form prior efforts and leads to significant gains in both accuracy and efficiency. Therefore, we believe our contribution goes beyond a mere incremental step.\n\n\n**Q2: One question is - how is the importance of the pretrained CLIP model in proposed method? if changing the CLIP models to a different pretrained models, since two parts of the framework depend on CLIP text encoder.**\n\nA: The proposed method does not strongly depend on CLIP itself. In our framework, CLIP serves mainly as a vision-language embedding tool to construct the confounder dictionary for causal reasoning. We chose CLIP for its wide adoption and strong generation, but our framework remains compatible with other VLMs, provided they can generate aligned text-image embeddings. We replaced CLIP with OpenCLIP[1] in our experiments, and as shown in Table 1, the results confirm that our method remains effective with alternative VLMs, demonstrating its flexibility and generalizability.\n\nTable 1. Comparison of using CLIP and OPENCLIP.\n|Methods|AP|AP50|AP75|\n|-|-|-|-|\n|YOLOv8|42.2|64.7|44.5|\n|MCR-UOD(CLIP)|44.6|67.3|47.5|\n|MCR-UOD(OPENCLIP)|44.3|67.3|47.2|\n\n**Q3: What is the difference between Yolov8 in Table 3 and UAV-YOLOv8 in Table 1?**\n\nA：Sorry for the confusion. UAV-YOLOv8 [2] is an enhanced version of YOLOv8 specifically tailored for UAV-based object detection. Unlike the original YOLOv8 baseline reported in Table 3, UAV-YOLOv8 (Table 1) integrates three key improvements: a more robust regression loss (WIoU v3), an attention mechanism and a five-scale detection head with FFNB. \n\n\n**Q4: Limitations: No limitation section for the submission.**\n\nA: Thank you for the comment. We would like to clarify that the previous version already contains a detailed discussion of the limitations in the appendix (lines 128–135), which may have been overlooked. We will add a concise summary of this section to the main text for better visibility.\n\n\n**References**\n\n[1] Cherti M,et al. “Reproducible scaling laws for contrastive language-image learning”,CVPR2023.\n\n[2] Wang G, et al. “UAV-YOLOv8: A small-object-detection model based on improved YOLOv8 for UAV aerial photography scenarios”. Sensors, 2023."}]

Author rebuttal:

Meta-review: This paper investigates multimodal casual reasoning for UAV object detection. Initially, some reviewers have questions regarding the motivation and experimental analysis of this work. The authors provide a good rebuttal addressing most of the concerns and questions. After rebuttal, all reviewers are positive on this work. There is no reason to overturn the reviewers’ comment. The AC recommends accepting this work. The authors should interest their response in rebuttal in the final revision.
Decision: Accept (poster)