Stochastic Context Consistency Reasoning for Domain Adaptive Object Detection

Venue: acmmm_org_ACMMM_2024_Conference
Published: 2024-03-23 05:07:54

Abstract: Domain Adaptive Object Detection (DAOD) aims to improve the adaptation of the detector for the unlabeled target domain by the labeled source domain. Recent advances leverage a self-training framework to enable a student model to learn the target domain knowledge using pseudo labels generated by a teacher model. Despite great successes, such category-level consistency supervision suffers from poor quality of pseudo labels. To mitigate the problem, we propose a stochastic context consistency reasoning (SOCCER) network with the self-training framework. Firstly, we introduce a stochastic complementary masking module (SCM) to generate complementary masked images thus preventing the network from over-relying on specific visual clues. Secondly, we design an inter-changeable context consistency reasoning module (Inter-CCR), which constructs an inter-context consistency paradigm to capture the texture and contour details in the target domain by aligning the predictions of the student model for complementary masked images. Meanwhile, we develop an intra-changeable context consistency reasoning module (Intra-CCR), which constructs an intra-context consistency paradigm to strengthen the utilization of context relations by utilizing pseudo labels to supervise the predictions of the student model. Experimental results on three DAOD benchmarks demonstrate our method outperforms current state-of-the-art methods by a large margin. Code is released in supplementary materials.

Main paper: 
 related work: 
 Object detection. Object detection can be divided into two categories: one-stage (e.g., YOLO [40] , FCOS [47] and Deformable DETR [64] ) and two-stage detectors (e.g., Faster RCNN [41] and Fast RCNN [14] ). One-stage detectors directly predict object bounding boxes and their associated class probabilities for the entire image without the need for explicit region proposals. The two-stage detectors first generate some candidate proposals by the region proposal network (RPN) [41] and then refine these candidates to give the final bounding boxes and categories. We employ Faster RCNN as the base detector for its outstanding performance and expansibility. [3, 7, 22, 30, 52, 63] to explore different aspects of feature alignment. For example, context-aware alignment [3] , multi-level alignment [22, 24, 52, 63] , token/query-based alignment [50, 56] ,and multi-scale alignment [7] .Recently, the idea of Mean Teacher [46] is extended from semisupervised object detection to DAOD by MTOR [1] and achieves many remarkable works [10, 23, 29, 36, 39, 56] .Consistency learning for DAOD. Consistency learning for DAOD aims to train a robust detector by making similar predictions about different perturbations. Jeong et al. [26] propose CSD to constrain the prediction consistency of the model by feeding the original image and the horizontally flipped image. Xie et al. [51] propose a pretext task for pixel-level consistency. Recently, researchers combine consistency learning with the self-training framework to address domain adaptation in object detection. Kennerley et al. [29] introduce a two-phase consistency network to enhance the consistency of prediction between the teacher and the student model. Hoyer et al. [23] propose MIC to strengthen the reasoning ability for context by constraining the consistency of the prediction between the original image and the mask image. Different from the above approaches: firstly, our method models both teacher-student and student-student consistency constraints, while previous works only focus on the consistency of the teacher-student aspect and thus heavily rely on pseudo labels. Secondly, our proposed context consistency reasoning enforces constraints from both classification and localization perspectives, ensuring the model learns both contextual semantic information and spatial information.  
self-training framework: 
 We use SADA [7] as the student model S, which is a two-stage Faster RCNN [41] object detector combined with image-level and instance-level adversarial learning. Domain discriminators D are placed after the student module to predict the domain label of the features. The adversarial optimization function is formulated:EQUATIONwhere L 𝐵𝐶𝐸 denotes the BCE loss of domain classification at the image/instance-level and we use Gradient Reverse Layers(GRL) [12] for min-max optimization.The teacher model and the student model share the same structure in the self-training framework. The student model acquires the source domain knowledge from source data by supervised training:EQUATIONwhere 𝐶 ′ 𝑠 and 𝐵 ′ 𝑠 denote categories and bounding boxes in 𝑋 𝑠 predicted by the student model. By forcing the student model to imitate the deep representations of the teacher model, the student model can learn the knowledge of the target domain from the pseudo labels ( B, Ĉ):EQUATIONwhere 𝐶 ′ 𝑡 and 𝐵 ′ 𝑡 denote categories and bounding boxes in 𝑋 𝑡 predicted by the student model, Ĉ and B denote the categories and bounding boxes of objects in a pseudo label. In addition, to filter out the noisy pseudo labels generated by the teacher model, we apply a confidence threshold 𝛿 to filter low-confidence bounding boxes and remove duplicate bounding boxes using non-maximum suppression (NMS) [15] for each class. The teacher model is updated by Exponential Moving Average (EMA) from the weights of the student model without gradient accumulation:EQUATIONwhere 𝜃 𝑡 and 𝜃 𝑠 denote the model parameters of teacher and student respectively, and 𝛼 is an update hyper-parameter. 
methodology 4.1 overview: 
 We propose the SOCCER network for DAOD, which is composed of SCM, Inter-CCR, and Intra-CCR, as illustrated in Figure 2 . SOC-CER is built on the self-training framework as introduced in Section 3.2. Concretely, SCM generates a pair of interchangeable views {V 1 , V 2 } by stochastically masking a part of local visual features in target images. Then, Inter-CCR is designed to conduct consistency reasoning between predictions of {V 1 , V 2 } from the student model, thus representing more domain-specific clues (i.e., appearance, texture). Simultaneously, in Intra-CCR, the teacher model generates pseudo labels from the original view V 0 (target image 𝑋 𝑡 ) with complete context information and supervises the reasoning of the student model for interchangeable views {V 1 , V 2 } with the incomplete complementary context information. Therefore, both teacher and student models in this network acquire a powerful ability of contextual correlation.To enhance the context correlation ability of the network on the target domain, we employ stochastic complementary mask pairs EQUATIONwhere 𝑢 is sampled from a uniform distribution U (0, 1), 𝑏 is the spatial size of a masked patch, 𝑟 is the mask ratio in (0, 1), ℎ ∈ [0, . . . , 𝐻 𝑏 -1], 𝑤 ∈ [0, . . . , 𝑊 𝑏 -1] are the masked patch indices, and H, W are the height and width of the input image. Then, the complementary mask M is obtained as:EQUATIONwhere 1 represents a all-ones matrix with the same dimensions as M. The interchangeable target views are obtained by element-wise multiplication between the complementary masks and the target domain images:EQUATIONSCM explicitly constructs views {V 1 , V 2 } with inter-context relationships, and forms intra-context relationships with the original view V 0 . It also achieves a better balance between deletion and preservation of regional information in images compared to other methods [5, 11, 61] .Previous self-training methods mainly construct the category-level consistency constraint but suffer from the poor quality of pseudo labels. To mitigate the reliance on pseudo-labels, we design the Inter-CCR module, which leverages self-supervision signals from the student model to bidirectionally align features, thereby enhancing features' representation capability without the supervision of pseudo-labels. Specifically, the interchangeable views {V 1 , V 2 } are first fed into the student model to generate corresponding predictions. Subsequently, the classification consistency reasoning L 𝑐𝑙𝑠 𝐼𝑛𝑡𝑒𝑟 and regression consistency reasoning L 𝑟𝑒𝑔 𝐼𝑛𝑡𝑒𝑟 are conducted to the predictions of interchangeable views. To avoid mismatched consistency reasoning, we set a large IoU matching threshold 𝜏 as 0.75. Here, we calculate the IoU matrix for 𝐵 𝑀 𝑡 and 𝐵 𝑀 𝑡 , and select the maximum value along the longest side as their matching score. If this score exceeds the 𝜏, the bounding boxes are considered successfully matched. More details in Section 5.4 and Appendix.For the classification consistency reasoning, we bidirectionally align the category distribution between category probability vectorsEQUATIONwhere 𝐾 is the number of matched bounding box pairs between 𝐵 M 𝑡 and 𝐵 M 𝑡 , 𝑝 M 𝑖 is the category probability vector of the 𝑖 𝑡ℎ bounding box in 𝐵 M 𝑡 , 𝑐 M 𝑗 denotes the corresponding category prediction in 𝐵 M 𝑡 , and 𝑤 𝑗 is the confidence of 𝑐 M 𝑗 in (0, 1). For the regression consistency reasoning, we introduce the Huber loss to bidirectionally align the matched bounding boxes in 𝐵 M 𝑡 and 𝐵 M 𝑡 :EQUATIONwhere 𝐵 M 𝑡 , 𝐵 M 𝑡 denote the predicted bounding boxes and the Huber loss is defined as:EQUATIONto select a more appropriate transition point 𝜎, instead of using a hard threshold, we dynamically adjust 𝜎, which is set to the average difference between 𝑏 M 𝑛 and 𝑏 M 𝑛 during training, and locks below a certain value. It reduces the sensitivity of the model to outliers during initial training and adaptively increases the punishment. More details are discussed in Section 5.4.The total Inter-CCR loss is defined as:EQUATIONWe introduce the Intra-CCR to construct a self-training framework with intra-context consistency, in which the teacher model encourages the student model to explore extra target contextual correlation thus reasoning the masked regions. During the teacher-student mutual learning phase, this contextual correlation ability can be updated to the teacher model through EMA, thereby improving the quality of pseudo-labels and creating a virtuous cycle. Concretely, the teacher model obtains the complete information of the original view V 0 to generate pseudo labels ( B, Ĉ). Afterward, we utilize pseudo labels to supervise predictions of the student model from {V 1 , V 2 } by classification and regression context consistency reasoning losses: L 𝑐𝑙𝑠 𝐼𝑛𝑡𝑟𝑎 and L 𝑟𝑒𝑔 𝐼𝑛𝑡𝑟𝑎 . The classification consistency reasoning optimizes the student model to reason contextual semantic information from masked views with:EQUATIONwhere 𝑁 and 𝑀 represent the number of objects detected by the student model while reasoning V 1 and V 2 , ĉ𝑖 denotes the corresponding category prediction in the 𝑖 𝑡ℎ bounding box 𝐵 M 𝑡 , and ŵ𝑖 = 𝑚𝑎𝑥 (𝑠𝑜 𝑓 𝑡𝑚𝑎𝑥 (𝑝 M 𝑖 )) is the confidence of ĉ𝑖 . As the actual number of objects in {V 1 , V 2 } is different, we set 𝜆 = 𝑁 𝑁 +𝑀 , 𝜇 = 𝑀 𝑁 +𝑀 to balance the contributions of two loss branches of Intra-CCR. We also discuss another weight setting in Appendix. Meanwhile, the regression consistency reasoning enhances the space perception capability of the student by reasoning the location of masked objects in {V 1 , V 2 }:EQUATIONThe total Intra-CCR loss is defined as:EQUATIONThe overall objective of SOCCER is:EQUATION) where L 𝑠𝑢𝑝 is the supervised loss from Eq. 2, L 𝐼𝑛𝑡𝑒𝑟 𝑐𝑜𝑛𝑠𝑖𝑠 is Inter-CCR loss, L 𝐼𝑛𝑡𝑟𝑎 𝑐𝑜𝑛𝑠𝑖𝑠 is Intra-CCR loss, and L 𝑎𝑑𝑣 is adversarial loss given by Eq. 1, 𝜆 0-4 are weights of different loss. We present the detailed pseudo-code of the training pipeline in Appendix. 
experiments 5.1 datasets: 
 We use the mean Average Precision (mAP) with a threshold of 0.5 to evaluate the detection performance on target domains and evaluate the effectiveness of the SOCCER on four public datasets, including BDD100k [55] (B), Sim10k [28] (S), Cityscapes [8] (C), and Foggy Cityscapes [42] (F). Cityscapes to BDD100k: BDD100k [55] is a large-scale driving dataset. Following methods [10, 20, 35, 52] , we extract the daytime subset of BDD100k as the target domain, containing 36, 728 training images and 5, 258 validation images. The subset of Cityscapes with seven shared categories is adopted as the source domain. Sim10k to Cityscapes: Sim10k [28] is a synthetic dataset from GTA-V game engine containing 10,000 images with 58,071 annotations of the car. We use Sim10k as the source domain and a subset of Cityscapes with only the "car" category as the target domain. Cityscapes to Foggy Cityscapes: Cityscapes [8] is collected from 50 urban scenes of normal weather and contains 2,975 images for training and 500 images for validation. Foggy Cityscapes [42] is synthesized from the Cityscapes to simulate foggy weather. We use Cityscapes as the source domain and Foggy Cityscapes with the most dense fog (0.02) as the target domain.For a fair comparison with SOTA methods, we adopt the Faster RCNN object detector [41] with the ResNet-50 backbone [19] and FPN [37] as the detection model. We scale all images by resizing the shorter side of the image to 800 pixels while maintaining the image ratios. We set the size of masked patches 𝑏 = 32, the mask ratio 𝑟 = 0.5, the EMA update ratio 𝛼 = 0.9, and the confidence threshold 𝛿 = 0.8. We copy the weight of the student model to the teacher model from the start without a burn-in stage, and mutual learning for 60k iterations with an initial learning rate of 0.0025, and a weight decay of 0.0001. All experiments are implemented using Detectron [16] and conducted on a single RTX 4090 GPU with a batch size of 2 (i.e., 1 source image and 1 target image).In this section, we compare the SOCCER with other SOTA in three DAOD benchmarks, including Cityscapes to BDD100k ( Table 1 ), Sim10k to Cityscapes (Table 2 ), and Cityscapes to Foggy Cityscapes (Table 3 ). "Source" denotes the base Faster RCNN only trained by source data as the performance lower bound. Cityscapes to BDD100k: In Table 1 , SOCCER achieves 41.8% mAP, surpassing all comparison models by an average significant margin of 6.4% mAP over the best-performing Faster RCNN detector MIC [23] , the FCOS detector SIGMA [34] , and the Deformable DETR detector MTM [50] . And SOCCER achieves the best performance in almost all categories, especially in rare categories such as "truck", "mcycle" and "bicycle", as well as confusing categories like "person" and "rider", which are also reflected in Figure 7 . Benefiting from the Inter/Intra-CCR to enforce context consistency between multiview, SOCCER gains an average 7.2% mAP than other self-training methods, such as PT [4] , MIC, and MTTrans [56] . Sim10k to Cityscapes: Table 2 validates the adaptation ability of SOCCER from synthetic to natural images with a large domain gap. SOCCER achieves 63.8% AP, outperforming the second-best method OADA [54] by 4.6% AP. We also notice that SOCCER exceeds the SOTA consistency learning based method MIC with 4.9% AP, which demonstrates the advantage of the stochastic complementary masking strategy. Cityscapes to Foggy Cityscapes: In Table 3 , SOCCER achieves 51.1% mAP, the best performance of all other SOTA. The comparison with the latest self-training methods CMT [2] , MIC, and MTM demonstrates the effectiveness of SOCCER. The detection results of "car", "rider", "mcycle", and "bicycle" is higher than other methods, which demonstrates that SOCCER enhances the ability of context reasoning in correlated categories (as exampled in the detection results of "rider" and "bicycle" in Figure 1 (c) and Figure 5 𝑟𝑜𝑤1 ) and intensive categories (as shown in Figure 5 𝑟𝑜𝑤2 ).Ablation Studies: We conduct experiments on different variants of our model to evaluate the effectiveness of each component, and all ablation studies are performed on three benchmarks, as shown in Table 4 . Comparison between row A (baseline) and row B shows that self-training and masking strategy in SCM bring obvious improvement. With our SCM (row C), adopting the dual branches strategy can achieve an average gain of 0.4% mAP compared to the single branch (row B). Compared with row C, L 𝑟𝑒𝑔 𝐼𝑛𝑡𝑟𝑎 (row D) enhances the localization ability, obtaining an average gain of 2.0% mAP, especially a gain of 4.1% mAP on C → B. With L 𝑟𝑒𝑔 𝐼𝑛𝑡𝑒𝑟 and L 𝑐𝑙𝑠 𝐼𝑛𝑡𝑒𝑟 (row E), the performance on average increase by 2.0% than row C, which proves the effectiveness of Inter-CCR. The variant in row E is boosted by L 𝑟𝑒𝑔 𝐼𝑛𝑡𝑟𝑎 (row H ) with a performance average gain of 2.2% mAP on three benchmarks, which indicates that Intra-CCR can benefit from localization ability. Further, we remove L 𝑟𝑒𝑔 𝐼𝑛𝑡𝑒𝑟 (row F) and L 𝑐𝑙𝑠 𝐼𝑛𝑡𝑒𝑟 (row G) separately resulting in a performance decline over all three benchmarks, which proves that the single type of context consistency reasoning cannot align features well, thus reducing representation ability of features for spatial and semantic information. Note that, since L 𝑐𝑙𝑠 𝐼𝑛𝑡𝑟𝑎 is the basic loss of the self-training framework, we don't remove this. Parameter Sensitivity:(1) Masked patch size: Figure 3 (a) demonstrates the influence of different masked patch sizes 𝑏 for SOCCER. From the results, we find that the medium value (b=32 and b=40) achieves superior performance, and the too small and large patches with the same mask ratio (𝑟 = 0.5) achieve inferior results, especially in C → F. The small patches can not mask sufficient regions, and the student model can get the correct detection result only by simple reasoning. In contrast, the large patches mask too many regions, even the whole object, thus the student model is difficult to reason the masked visual clues and gets caught in the sub-optimal solution.( 𝜎 ≥ 1, it is fixed directly without dynamic adjustment, and avg denotes the average difference of bounding boxes is not fixed. Overall, the mAP of all hyper-parameter settings floats within 1.5% and the best performance is achieved when 𝜎 = 0.6. We set 𝜎 = 0.6 for balancing the penalty strength.(3) IoU matching threshold: In Figure 3 (c), we explore the effect of different matching thresholds 𝜏 in Inter-CCR on the performance. We set four groups 𝜏 on three benchmarks, and find that either too harsh or too loose matching thresholds are detrimental to Inter-CCR training. Too small 𝜏 leads to incorrect matches, while too large 𝜏 overlooks the intended matches. It is optimal only if 𝜏 = 0.75 to avoid inconsistent matching objects. (4) Mask ratio: In Figure 3 (d) , we adjust the mask ratio 𝑟 in Eq. 5 to explore the influence of a pair of mask images with complementary mask degrees on SOCCER (i.e., when V 1 ' mask ratio is 𝑟 , V 2 ' mask ratio is 1 -𝑟 ). We find that when the mask ratio difference between a pair of mask images is too large, the performance of the network will be more unfavorable. Only when 𝑟 = 0.5 can it provide a fairer context consistency reasoning training for Inter/Intra-CCR. Different Masking Mode: Although we find the optimal value of 𝑏, we wonder if random mask patch sizes are more beneficial to the performance of SOCCER. As illustrated in Figure 4 (a), we conduct two group experiments, one selects random sizes from the entire range (blue bars), and another selects random sizes from (24, 32, 40) (dark blue bars), which is empirically validated as the optimal values in Figure 3 (a). The latter is significantly better than the former but still averages 1.2% lower than the fixed size of 𝑏 = 32. We also explore more possibilities for mask image pairs in SCM module. Specifically, we turn the stochastic complementary masking strategy into a dual stochastic masking strategy (i.e., the masks of V 1 and V 2 are generated respectively). But the result (red bars) is still lower than the complementary strategy. This mainly results from that: 1) Random masking brings some overlap of vision clues, so it is easier for the student model to make consistent reasoning based on shortcuts to the same visible regions. Compare with Different Masking Out Augmentation: Masking out augmentation [5, 11, 61] is widely employed for its effectiveness and efficiency. By deleting parts of regions, networks can learn originally less sensitive but important information thus increasing the perception field. Since the similarity, we further compare them with our SCMasking strategy. Note that we conduct the above masking out augmentation in dual branches of SCM respectively, same with the setting of DSMasking. In Table 5 , the single mask region methods Cutout and Random Erasing are unfavorable for context consistency training, reducing 11.1% and 10.6% mAP compared to SCMasking. While GridMask uses structured discard regions, random mask sizes introduce more uncertainty (we prove in Figure 4 (a) that random mask sizes are adverse to performance), and regularly visible regions reduce the initiative of the network to learn context information.Table 5 : Results on Cityscapes → BDD100k with different masking out methods in SCM module. DSMasking denotes the dual stochastic masking and SCMasking denotes our stochastic complementary masking. We compare three methods: Cutout [11] , Random Erasing [61] , and GridMask [5] .Selected Method mAP 0.5 mAP 0.75 mAP 0.5:0.95 Cutout [11] 30 Qualitative Results Visualization: Figure 5 visualizes some detection results of Source-only [41] , MIC [23] , CMT [2] and SOCCER, along with the ground truth for comparison. Our SOCCER can accurately localize and classify most objects indicating that Inter-CCR enhances the feature's representation to distinguish confused categories (such as "person" and "rider" in 𝑟𝑜𝑤1) and Intra-CCR models the context correlation to detect objects in dense scenes (𝑟𝑜𝑤2). CAM Visualization: We further conduct Class-Activation-Map (CAM) [43] for a visual explanation of how our network uses contextual information to reason. As observed in Figure 6 (a), SOCCER can still use visible contextual clues to reason the occluded target even when the object is occluded (red boxes). ComparisonSince there is no teacher model in the baseline, for a fair comparison, we take the baseline's output with a confidence threshold 𝛿 = 0.8 to filter unreliable predictions. 
: 
 The example of our pseudo label quality comparison with MIC [23] . Benefiting from Inter-CCR and Intra-CCR modules, the network extracts more discriminative features to distinguish confusing categories (i.e., "rider" and "person") and utilizes more context clues to build relations between objects (i.e., "rider" and "bicycle").Adaptive Object Detection (DAOD), intending to design a detector that can transfer the knowledge learned from the labeled source domain to the unlabeled target domain [6, 34, 63] . Unlike traditional object detection, the critical challenge of DAOD is to learn the domain-invariant feature for bridging the domain gap while preserving the domain-specific characteristic of the target domain for facilitating detection. The mainstream approaches try to align feature distributions between source and target domains through pixel-level [24, 30, 63] , instance-level [18, 45, 63] , and image-level [6, 31] adversarial learning. Other methods attempt to mitigate domain shifts by modeling prototypes as category centers to minimize each distance [48, 53, 59] , using graph matching theory [13, 32, 34, 38, 56] , and employing image-to-image translation to generate target-like images [9, 21, 44, 57] . However, the above methods focus on extracting domain-invariant features, and this might ignore domain-specific features that reflect the discriminatory information of objects in the target domain. Additionally, they neglect the context correlation of 1) between objects and 2) between objects and the background, which can provide powerful discriminatory clues for object detection, particularly under a large domain gap.Recently, some DAOD methods [9, 21, 23, 27, 39, 50] introduce the self-training framework and achieve significant adaptation gains. The teacher model predicts the unlabeled target images to obtain pseudo labels so as to guide the student model to generate target features near the support of the source domain. Further, AT [36] adopts adversarial learning to mitigate the domain shifts toward the source domain in the student model. CMT [2] integrates contrastive learning to fully exploit the ability of the teacher model to characterize the knowledge of the target domain. The existing self-training frameworks heavily rely on pseudo labels of the teacher model to implement the category-level consistency constraint, as illustrated in Figure 1 (a). However, the quality of pseudo labels is usually poor, and such single category-level consistency constraint limits the domain adaptation ability of the student model thus leading to sub-optimal learning.To address the above problems, we propose a stochastic context consistency reasoning (SOCCER) network to learn the context correlation knowledge of the target domain by the multi-view context consistency (as shown in Figure 1 (b) ). Firstly, we introduce a stochastic complementary masking module (SCM) to generate a pair of complementary interchangeable views by stochastically masking a part of local visual clues. This complementary masking strategy prevents the student model from taking shortcuts with overlapping visual clues to truly learn the relevance of neighboring contextual regions. Secondly, we design an inter-changeable context consistency reasoning module (Inter-CCR) to model the inter-context consistency between the student model's predictions from interchangeable views. Enforcing inter-context consistency between two views enhances bidirectional feature alignment and improves the representation ability of extracted features for texture and appearance attributes. Meanwhile, we develop an interchangeable context consistency reasoning module (Intra-CCR) to impose intra-context consistency for multi-view between pseudo-labels generated by the teacher model with a complete view and predictions of the student model for incomplete views. This fully strengthens the potential of the network for contextual correlation, thereby improving both the cross-domain adaptability and the quality of pseudo-labels, as shown in Figure 1 (c) . Extensive experiments on three cross-domain benchmarks demonstrate the extraordinary adaptation ability of SOCCER and the effectiveness of each component.The contributions of this paper are summarized as follows:• We propose a stochastic context consistency reasoning network to learn the context correlation knowledge of the target domain for modeling the underlying contextual relationship of the target domain between objects and environments.  
 
 Appendix:  


Decision: This paper proposes a novel approach to address key challenges in DAOD by enhancing context correlation and robustness. Reviewers have noted the paper's strong theoretical framework, comprehensive experiments, and practical implications for applications. Although some concerns were raised regarding the technical details; The overall innovation and technical contribution of the proposed method, warrant acceptance. Therefore, I recommend accepting this paper and hope the authors can carefully revise the manuscript according to the reviewers' comments in the camera-ready version.

Reviews: review: paper_summary: paper_summary: Domain Adaptive Object Detection (DAOD) aims to improve the detector's adaptivity to unlabeled target domains by labeling the source domain. Recent advances have utilized self-training frameworks that enable student models to learn target domain knowledge using pseudo-labels generated by teacher models. A stochastic contextually consistent reasoning (SOCCER) network with a self-training framework is proposed.
strength_weakness: strength_weakness: The overall framework of the paper is good.
The work on the paper is very good and representative of the type.
limitations: limitations: 1、Insufficient introduction to the context in the introductory section
2、The structure and logic of the article is not made very clear.
paper_summary: paper_summary: This article presents further research on the task of Domain Adaptive Object Detection (DAOD) by proposing a method based on stochastic mask consistency. It introduces complementary masks and two types of loss calculation to achieve content consistency learning, resulting in state-of-the-art detection performance on three benchmarks. The method provides a more efficient approach for adapting a labeled source domain object detection network to an unlabeled target domain.
strength_weakness: strength_weakness: 1. The method of complementary masking (Stochastic Complementary Masking) is interesting. It allows the network to share the same set of labels across different augmented images.
   
2. The experiments conducted in the paper are comprehensive. In addition to ablation experiments, they compare different types of masks, analyze parameter sensitivity, and provide quantitative visualization analysis.
   
3. The paper is well-structured and easy to understand.
limitations: limitations: 1. The necessity of complementary masks is uncertain. In the experiment of Different Masking Out Augmentation, the results of DSMasking and SCMasking seem to be comparable within the margin of error.It is unclear whether complementary masks have a distinct advantage in terms of contextual connections or content understanding.
   
2. The advancement of the proposed method. In the experiment from Cityscapes to Foggy Cityscapes, the results of SCM appear to have both advantages and disadvantages compared to MIC and CMT, making it difficult to claim a significant improvement.
   
3. The novelty of the paper. This is not the first proposal of a mask method, and the Inter-CCR and Intra-CCR modules can be considered somewhat ordinary for this work.
paper_summary: paper_summary: The paper titled "Stochastic Context Consistency Reasoning for Domain Adaptive Object Detection" presents a novel approach to improving Domain Adaptive Object Detection (DAOD). DAOD aims to enhance the performance of object detectors on unlabeled target domains by leveraging labeled source domain data. This paper proposes a stochastic context consistency reasoning (SOCCER) network for DAOD, aiming to address the sub-optimal learning issue caused by category-level consistency learning in existing literature. Stochastic complementary masking is introduced to enhance the context correlation ability of the network on the target domain. Inter-CCR and Intra-CCR modules are designed to enable the student network to learn from self-supervised signals that ensure context consistency across different views, as well as from pseudo labels that ensure context consistency with the teacher network that obtains the complete view.
strength_weakness: strength_weakness: 1. **Novelty**: The introduction of the Stochastic Complementary Masking (SCM) and the dual context consistency reasoning modules (Inter-CCR and Intra-CCR) are innovative contributions to the field of DAOD. These components address the critical issue of context understanding.
2. **Theoretical Approach**: The paper presents a sound theoretical framework that combines self-training with stochastic masking and context consistency reasoning. This multi-view consistency approach is well-justified and enhances the learning process by providing robust contextual cues.
3. **Technical Correctness**: The proposed method is technically robust, with clear definitions and justifications for the SCM, Inter-CCR, and Intra-CCR modules. The mathematical formulations and algorithmic steps are well-detailed, ensuring reproducibility and understanding.
4. **Adequate Evaluation**: The paper conducts extensive experiments on three DAOD benchmarks, demonstrating significant performance improvements over existing state-of-the-art methods. The results are supported by comprehensive ablation studies that validate the contributions of each module.
5. **Clarity**: The paper is well-structured and clearly written, with detailed explanations of the proposed method, its components, and the experimental setup. Figures and tables are effectively used to illustrate key concepts and results.
6. **Applications**: The proposed SOCCER network has practical implications for various applications, such as autonomous driving, intelligent surveillance, and industrial automation, where robust object detection across different domains is critical.

In summary, this paper presents a satisfactory level of innovation and the quality of the manuscript is very high. The experiment was carefully conducted, and the subsequent analysis was comprehensive, effectively confirming the effectiveness of the proposed method.
limitations: limitations: **No clear explanations of the contributions to multimedia/multimodal research**: The most import thing may be that as a submission for ACM MM, the authors could provide a full discussion on the research significance, prospects, and feasible next steps of the proposed method in the fields of multimedia/multimodal research. This is of significance for promoting related research.
paper_summary: paper_summary: This paper explores the significance of mask methods and consistency learning for domain adaptive object detection. It proposes a stochastic context consistency reasoning network to improve domain adaptive performance.
strength_weakness: strength_weakness: This paper is well written, and fully discusses the impact of mask and consistency learning on domain adaptive object detection. The experimentation is comprehensive.
limitations: limitations: Lack of novelty and technical contribution. The paper is more like a simple extension of *MIC: Masked Image Consistency for Context-Enhanced Domain Adaptation*. 
As problem raised in the abstract *such category-level consistency supervision suffers from poor quality of pseudo labels*, the proposed methods in this paper do not seem to address such issue.

There are still some detailed concerns: 
1. The SOTA results did not include *Harmonious Teacher for Cross-domain Object Detection (CVPR2023)*. Please compare with it.
2. Is the *red bars* in line 796 should be *orange bars*?
3. Doubt the significance of proposed *SCM*. Did the authors try to constructs more inter-changeable views $\{V_1, V_2, V_3, ...\}$ from one target images for *inter-changeable context consistency reasoning*?