Rethinking the One-shot Object Detection: Cross-Domain Object Search

Venue: acmmm_org_ACMMM_2024_Conference
Published: 2024-04-01 04:26:22

Abstract: One-shot object detection (OSOD) uses a query patch to identify the same category of object in a target image. As the OSOD setting, the target images are required to contain the object category of the query patch, and the image styles (domains) of the query patch and target images are always similar. However, in practical application, the above requirements are not commonly satisfied. 
Therefore, we propose a new problem namely Cross-Domain Object Search (CDOS), where the object categories of the query patch and target image are decoupled, and the image styles between them may also be significantly different. For this problem, we develop a new method, which incorporates both foreground-background contrastive learning heads and a domain-generalized feature augmentation technique. This makes our method effectively handle the object category gap and domain distribution gap, between the query patch and target image in the training and testing datasets. We further build a new benchmark for the proposed CDOS problem, on which our method shows significant performance improvements over the comparison methods.

Main paper: 
 introduction: 
 Object detection is one of the fundamental problems in computer vision. The conventional approach relies on training with a large volume of annotated data and then testing on specified categories, constituting a closed-classes task that is difficult to extend to novel categories. In contrast, One-shot Object Detection (OSOD) tasks [2, 12, 32, 36] aim to break the limitation of this closed-classes setting and make object detection into an open-classes task. The inspiration for OSOD comes from humans' remarkable ability to recognize and locate objects by providing them with an example object that they have never seen before. In other words, humans can rapidly learn new concepts and features from a single example and then identify the same category of objects in other scenes.As the setting of the OSOD task, given a query patch, and a target image containing objects of the same category as the query patch, the goal is to identify the objects (in the target image) of that category. We have a question about this setting: Does the target image necessarily contain objects of the same category as the query patch? In real-world applications, a common scene may be identifying the objects of interest from a image gallery (no matter whether containing the query patch), but not manually selecting the images with the desired detection objects as input. Further, it is also important that the image gallery should be broad, potentially containing data from various image styles (domains).From the above two perspectives, in this work, we propose to study a new problem, i.e., Cross-Domain Object Search (CDOS). Different from the classical OSOD, we do not require all the target images in the gallery to contain the object category of the query patch. We also allow the target images to have various styles, which can be different from the query patch, and even have not been seen in the training dataset, as depicted in Figure 1 .The proposed CDOS is a very challenging problem. The first challenge comes from the object (category) similarity measurement. In OSOD, the main problem is to find the most similar object to the query patch in a target image. In CDOS, we have to first judge whether the target contains the desired object. The second challenge comes from the domain gap. In our problem, we allow the target images within various domains, e.g., a cartoon-style image, which has the domain gap with the query patch. Moreover, following the domain generalization problem [30] , we propose to use all the natural images as the training set, while the style images during testing are not seen during training.To address these two challenges, we present a new approach for CDOS. Specifically, we first propose a new module for foregroundbackground contrastive learning. This module is composed of two parts. The foreground contrastive learning head aims at improving the network's ability to classify foreground objects, thereby reducing category confusion. The background contrastive learning head is designed to bolster the network's capacity to distinguish background, thus mitigating background misjudgment (false positives). Besides, to address the challenge of open-domain issues, we implement a random feature perturbation augmentation on the feature extraction network during training. This data augmentation strategy diversifies the training scenarios by altering the statistical properties of images, which enhances the model's robustness when encountering data from unknown domains. In summary, the main contributions of this work are:• We extend the one-shot object detection task and propose a new and practical problem, i.e., Cross-Domain Object Search (CDOS), which enables a convenient search of objects of interest in the category-free and domain-various image galleries. • We develop the first baseline method for CDOS, which constructs an abundant number of foreground and background samples for contrastive learning, to enhance the model's feature discrimination capabilities. Furthermore, a feature augmenter module is applied to narrow the domain gap between the query and the target images, also the training and testing data.  
related work 2.1 one-shot object detection: 
 In the domain of computer vision, One-shot Object Detection (OSOD) presents a unique object detection task. The objective is to detect objects within an image that are of the same class as a presented query patch after having only a single instance of the object to reference, without further fine-tuning for new categories. This differs from conventional object detection methods, such as Faster-RCNN [24] , which generally require extensive labeled data to train models for recognizing specific object classes. Works like [2, 12, 32, 36] have made notable contributions to OSOD by employing feature fusion to obtain directly transferable meta-knowledge, thereby generalizing this meta-knowledge to new categories. Among these, BHRL [32] introduced an innovative Instance Hierarchical Relation (IHR) module that achieves superior results. In machine learning, such capabilities are particularly critical for scenarios where collecting or annotating vast amounts of data is challenging. The OSOD task is akin to our proposed object search task, but OSOD evaluations focus on paired query patches and target images and do not consider searching for objects within an image gallery, leading to a high rate of false positives in images without the objects of interest. We have developed a novel method for the object search task that mitigates these issues.In this study, we delve into Domain Generalization (DG), which aims to learn a robust model from multiple source domains that can be effectively generalized to any unseen target domain. Single Domain Generalization (SDG) [30] represents the more challenging extreme of DG, where only one source domain dataset is used for training with the goal of adapting to multiple unseen target domains. To address this challenge, various data augmentation algorithms have been designed to enhance the diversity of training data, as proposed in [19, 23, 30] . A domain augmentation module for synthesizing images was introduced in [30] , while [19] incorporated synthetic feature statistics to simulate the uncertainty of domain shift during training. To standardize SDG training, [5] incorporated various visual impairments as augmentations and devised a novel attention consistency loss. A new image meta-convolutional network capturing more domain-generalizable features was developed in [29] . There are also approaches specific to SDG for Object Detection. For instance, [31] proposed a cycle-consistent disentangled self-distillation method that disentangles from domainspecific representations without domain-related annotations (e.g., domain labels), and [28] employed pre-trained visual-language models to introduce semantic domain concepts through text prompts.In the context of CDOS, similar challenges to SDG are evident, such as training with real-world images and, during testing, the query patch and target image may appear in any domain. Particularly challenging is when the styles of the query patch and target image differ, for which we have applied feature augmentation in the twin branches of the Siamese network [3] to implement feature random augmentation.Most existing machine learning models are trained based on the closed-world assumption, where it is assumed that the test data will share the same distribution as the training data, known as In-Distribution (ID). However, when models are deployed in openworld scenarios [1] , the test samples can be Out-Of-Distribution (OOD), significantly increasing the difficulty of recognition. Distribution shifts can be caused by semantic shifts (e.g., samples from different categories) or covariate shifts (e.g., samples from different domains) [33] . Generalizing models to OOD data is a natural capability of humans that is challenging to replicate in machines.In the context of CDOS, both query patches and target images may originate from out-of-domain data (novel categories and unseen domains). Overall, the CDOS task can also be considered a form of Out-of-distribution Detection, capable of searching for objects in novel categories as well as unseen domains. 
msosb: a multi-style object search benchmark: 
 Datasets containing a variety of style data can significantly enhance the evaluation of a model's domain generalization capabilities. Thus, for CDOS methods, conducting assessments on datasets with a wide range of image styles is essential. As shown in Table 1, existing multi-style object detection datasets can be categorized as follows: (1) General objects: Clipart [14] , Watercolor [14] , Comic [14] ; (2) Traffic scenes: KITTI [9] , Cityscapes [4] , BDD100K [35] , FoggyCityscapes [26] , UFDD [22] , RTTS [17] , Sim10K [15] ; (3) Face detection: WIDER FACE [34] . The datasets Clipart [14] , Watercolor [14] ,and Comic [14] include abstract, artistic, and comic images, respectively. Understanding these types of abstract imagery allows for direct investigation into how models infer high-level semantic information. The Clipart [14] comprises approximately 1K images across 20 categories, aligned with Pascal VOC [7] . The Watercolor and Comic datasets contain around 1K training images and 1K testing images across six categories. Overall, the existing style datasets for common objects are modest in size and offer a limited array of categories, falling short of the needs for assessing domain generalization models in generic object detection tasks. Consequently, we have developed MSOSB: a multi-style object search benchmark. We will detail MSOSB's key aspects in the following sections. We aim to construct a multi-style benchmark for the CDOS task, sharing categories with the MS COCO [21] . This benchmark will encompass styles such as cartoon, oil, watercolor, pencil, and art.To curate images in these styles, we initially selected suitable images for object detection tasks from the image style classification datasets PACS [18] and OfficeHome [27] . Subsequently, we scoured the internet for images in these styles, aiming to represent each category adequately. Given the impressive capabilities of current generative models and their proliferating presence online, bringing generated images ever closer to our daily lives, we utilized the stable diffusion model [25] to generate images for each category and style, followed by a meticulous selection process. The images were then annotated and verified meticulously, following the annotation scheme of MS COCO [21] , ensuring the benchmark's versatility and reliability. For the Clipart [14] , Watercolor [14] , and Comic [14] datasets, we conducted a selection and reorganization process before integrating them into our dataset. Through t-SNE analysis, we observed that the distributions of Clipart [14] and Comic [14] were nearly identical, leading us to merge these datasets into the cartoon style. To further diversify the dataset's style, we applied BaiDu's style conversion API to the validation set of MS COCO [21] and all data from Pascal VOC [7] , generating pencil and art-styled data. In summary, as depicted in Figure 2 and the t-SNE visualization distribution in Figure 3 , we have constructed a benchmark comprising 80 categories. This benchmark includes 999 images from Clipart [14] , 1905 images from Watercolor [14] , 1905 images from Comic [14] , 264 images from PACS [18] , 170 images from Office-Home [27] , 1004 images sourced from the internet, 37624 images generated by stable diffusion [25] , and 32275 images produced via BaiDu's style conversion API, culminating in a total of 75146 images in the MSOSB.For the existing datasets Clipart [14] , Watercolor [14] , Comic [14] , PACS [18] , and OfficeHome [27] , we assigned three researchers to screen and adjust the annotations for the Clipart [14] , Watercolor [14] , and Comic [14] images, with the principle that the images must be readable and clear, and any misaligned annotations corrected. Three researchers were tasked with selecting images from the PACS [18] and OfficeHome [27] classification datasets, as well as collecting high-quality images of similar styles from the internet. The selection criteria were that the images should belong to the cartoon, oil, or watercolor styles, and be suitable for object For each style and category, 500 images were generated with varying sizes, ranging from 512 to 2048 pixels. Given the variable quality of some generated images, a rigorous selection and annotation process was necessary. Since the MS COCO [21] validation set and all Pascal VOC [7] data are already annotated, we utilized BaiDu's style conversion API for stylization, randomly generating pencil and art-style images. It's noteworthy that this stylization does not alter the content of the images, hence the original annotations can still be used. Finally, we standardized the annotation files into Pascal VOC [7] and MS COCO [21] formats. The first two digits of the image names indicate the style, for example, "01" for cartoon, followed by a sequence number representing the image's order within its style category, such as "01000000002" for the second image in cartoon style.The MSOSB introduces a series of challenging settings that rigorously test the limits of CDOS models, with challenges arising from:Variability in object size and aspect ratio. The MSOSB dataset includes objects of different sizes and shapes. This requires models to have strong abilities to recognize and distinguish objects in various visual situations.Complex Backgrounds and Overlapping Objects. Images within the MSOSB dataset typically exhibit complex backgrounds and overlapping objects, reflecting the conditions of cluttered environments around the objects in the real world.Multi-category multi-object scenes. Each image in the MSOSB may contain multiple objects with different categories to measure the models' ability of robust object classification/localization.Cross-Domain Variability. Since MSOSB shares categories with MS COCO [21] , it can be combined with MS COCO [21] to use together in six styles, further challenging the domain generalization capabilities of CDOS models. These challenging settings are meticulously planned to simulate complex real-world scenes, thereby providing a comprehensive benchmark for advancing CDOS techniques. 
proposed method: 
 In this section, we first define the setup for the CDOS problem and then elaborate on how the key components of our proposed method (CrDoOS) address the CDOS task.Similar to OSOD, object catogories are divided into base catogories 𝐵 and novel catogories 𝑁 , where 𝐵 ∩ 𝑁 = ∅. The domains of the data are split into seen domains 𝑆 and unseen domains 𝑈 , with 𝑆 ∩ 𝑈 = ∅. Given an arbitrary query patch, CDOS aims to detect targets within an image gallery, which contains a large and complex assortment of unknown images, that match the category of the query patch. Like OSOD, the CDOS task is trained using data from the base catogories and seen domains. After training, it can generalize to directly search for objects in an image gallery using just a single query patch, where the image gallery includes images from both base and novel catogories, as well as images from various domains. Unlike the OSOD task, the images in image gallery does not necessarily contain objects of the same category as the query patch in CDOS, and the styles of the target image and query patch may not match. This significantly increases the difficulty and aligns more closely with real-world scenarios.We introduce a CDOS framework inspired by BHRL [32] , with its overall architecture depicted in Figure 4 . This structure utilizes a two-stage object detection paradigm and employs a Siamese network [3] to extract features from both the query patch and the target image. Subsequently, a feature fusion module aggregates the features of the query patch and proposals of target image for classification and regression tasks. Building on this foundation, we have incorporated a foreground-background contrastive learning heads, enabling the network to fully leverage the features of objects across various categories and their background for contrastive learning. This addition enhances the network's discriminative capability regarding features without increasing inference parameters and computation time. To address the domain discrepancy between the query patch and the target image, we have incorporated a Feature Random Augmenter into both branches of the Siamese network [3] . This augmenter mainly operates by randomly perturbing the mean and variance of shallow features within the Siamese network [3] to achieve feature augmentation, thereby exposing the network to a broader range of domains and enhancing its ability to search for objects in images of various styles. Overall, our method presents two primary contributions: firstly, we introduced a foreground-background contrastive learning heads (see Subsection 4.3), and secondly, we implemented a feature random augmenter within the Siamese network [3] (see Subsection 4.4).A classical method-MoCo [10] , by employing momentum update mechanisms for feature comparison, has exerted a beneficial and profound impact on self-supervised learning. This approach demonstrates that utilizing a large set of features for contrastive learning enhances the backbone's capabilities in feature extraction and discrimination. Given the severe foreground confusion and background misclassification encountered when directly applying OSOD methods to object search tasks, we introduced a momentum-updated foreground-background contrastive learning heads. This addition improves the backbone's discriminative ability, thus mitigating issues of foreground confusion and background misclassification. The heads are divided into two parts. Foreground Contrastive Learning. We use a foreground feature contrastive learning strategy, which aims to resolve issues of foreground object ambiguity. Specifically, as shown in Figure 4 , the features of the target images are extracted using the Backbone_k, which employs momentum-updated parameters in combination with the Feature Pyramid Network (FPN) [20] , as described in following𝜃 𝐵𝑎𝑐𝑘𝑏𝑜𝑛𝑒_𝑘 (𝑡 ) = 𝑚 • 𝜃 𝐵𝑎𝑐𝑘𝑏𝑜𝑛𝑒_𝑘 (𝑡 -1) + (1 -𝑚) • 𝜃 𝐵𝑎𝑐𝑘𝑏𝑜𝑛𝑒_𝑞 (𝑡 ) (1)where 𝜃 𝐵𝑎𝑐𝑘𝑏𝑜𝑛𝑒_𝑘 denotes the parameters of the Backbone_k, and 𝜃 𝐵𝑎𝑐𝑘𝑏𝑜𝑛𝑒_𝑞 represents the parameters of Backbone_q, 𝑡 denotes the number of iterations, and 𝑚 stands for the momentum update parameter.For the supervision of this branch, the foreground features are cropped from the image using Ground Truth (GT) boxes of base categories and processed through a projector layer. These features are stored and updated in a categorized queue group, as illustrated in Figure 5 (a) .During this process, the features of objects from the same category as the query patch are considered positive samples, while those from different categories serve as negative samples. With the positive/negative samples, we apply the following loss function in Equation ( 2) [16] to train the network by contrastive learningEQUATIONwhere 𝐿 Foreg denotes the foreground contrastive learning loss, 𝑇 ∈ R + is a scalar temperature parameter, 𝑧 𝑝 is a sample of the same category as 𝑧 𝑖 , and 𝑧 𝑎 represents samples from other categories, 𝐴(𝑖) represent the set of samples excluding 𝑖.Through the above strategy, extensive contrastive learning among base category features enhances the discriminative capability of the Backbone_q, thereby alleviating foreground confusion during inference. It is important to note that in our design of the foreground feature queue group, we employ queues of the same length for different categories. This ensures that the number of features per category is consistent, thereby facilitating a more balanced learning process across the network.Background Contrastive Learning. We then consider the background contrastive learning head. Our basic idea is to build an individual discrimination task from the vast image backgrounds.This way, our background contrastive learning head involves contrasting positive sample pairs against a large pool of negative samples. Specifically, we randomly crop features from the background areas of the target image to populate the background feature queue (ensuring that the cropped features are not too small to maintain rich background information), as illustrated in Figure 5(b) . By storing these background features in an extensive queue, we enrich the diversity of background samples, effectively leveraging the advantages of individual discrimination tasks and contrastive learning. The features of the query patch and its augmented counterpart serve as positive samples, while all features in the background feature queue serve as negative samples.Finally, similar to the foreground head, the loss function for this background contrastive learning is detailed in the following Equation (3) .EQUATIONwhere 𝐿 Backg denotes the background contrastive learning loss, 𝑧 𝑗 (𝑖 ) is the augmented sample of 𝑧 𝑖 , and 𝑧 𝑏 represents the background sample, 𝐵(𝑖) represent the set of samples excluding 𝑖.Considering the domain gap between the query patch and the target image, and the training and testing datasets, we further develop a feature random augmenter to narrow the domain gap.As discussed in previous work [13] , the statistics of image feature channels (mean and standard deviation) are closely related to image style. Altering these channel statistics can be seen as an implicit way to change the style of the input image, as in Equation ( 4)EQUATION)where 𝑥 represents the original sample and Sty (𝑥 ) denotes the generated sample with image style transformation. Here 𝜇 (𝑥 ) denotes the mean of sample 𝑥, 𝜎 (𝑥 ) represents the variance of sample 𝑥, 𝜇 (𝑦) denotes the mean of the augmented sample 𝑦, and 𝜎 (𝑦) represents the variance of the augmented sample 𝑦.Inspired by [8] , we employ Normalization Perturbation (NP) to disturb the image features, as represented by Equation ( 5). The above NP operation effectively synthesizes various potential styles by perturbing the channel statistics in the shallow CNN layers of source domain images, without altering the shape or position of the objects within the images, which is suitable for the object search tasks in this work.We next present how to integrate the NP into our framework. Specifically, as shown in Figure 6 of the Siamese network, setting an activation probability of 0.5. It is noteworthy that the augmenters in both branches of the Siamese network operate independently, enabling the network to generate various augmentation patterns during training. This independence allows each branch to process different domains separately during training, learning domain-invariant feature representations. Based on this, the trained models are more universal across different domains and generalize well even without data from the target domain. Consequently, this aids in better matching the objects across different styles.We trained our model using the Stochastic Gradient Descent (SGD) optimizer for 15 epochs, employing a weight decay of 1e-4. The training was performed on 4 NVIDIA 3090 GPUs with a batch size of 16. The initial learning rate was set at 0.02 and reduced by a factor of ten after the seventh epoch. Our backbone, a ResNet 50 [11] , was pretrained on a reduced version of ImageNet [6] , ensuring that our model was not exposed to novel classes. The momentum value 𝑚 in Equation ( 1) was set to 0.999. For the feature queues in the foreground-background contrastive learning heads, we standardized the length of each category's foreground queue to 128, while the background feature queue was set to 65,536. 
experiments 5.1 setup: 
 Benchmark. For a fair comparison, we adhere to the division method previously established in works such as [2, 12, 32] , spliting the 80 categories of MS COCO [21] into four distinct combinations (split1-split4). For the testing experiments of the CDOS task, we meticulously reference the setup from image retrieval experiments, specifically choosing Split1 as our designated experimental setup.Metrics. Considering the distinct setup of the CDOS task from the OSOD task, the metric for the CDOS task should calculate the search precision for each category and then compute an average across all categories. Ultimately, we utilize the AP50 metric from MS COCO [21] to evaluate the outcomes of the CDOS task. This approach ensures that the evaluation accurately reflects the model's performance across all categories, adhering to academic standards while enhancing the clarity and comprehensibility of our findings.Comparison methods. As a new problem, we can not find a method to handle the CDOS problem directly. We try to include more methods for comparison. First, given the similar dataset configurations and training processes between CDOS and OSOD, we have selected several stare-of-the-art OSOD methods CoAE [12] , AiT [2] and BHRL [32] as the comparison method. Additionally, in CDOS problem, a key problem comes from the domain gap from different image styles. This way, to evaluate the effectiveness of our method for pen-domain scenarios, we also include two mainstream domain augmentation modules Learning to Diversify [30] and ACVC [5] into our approach, thereby investigating the crossdomain robustness of our model.As discussed above, based on OSOD, this work makes the following two extensions. Category extension: we expand the search space from the specific target images into the query-free retrieval gallery. Doamin extension we expand the image styles of the gallery from the natural image into a variety of styles. For comprehensive testing, we develop three protocols for evaluation.Protocol I: Fixed category in each domain. We randomly selected 10 images from each style and category to serve as query patches, which were then used to search for objects within an image gallery. The gallery is built upon all the images with the same style and category of the query in the whole dataset.Protocol II: Fixed category in various domains. We randomly selected 10 images from each style and category to serve as queries, which were then used to search for objects within an image gallery of the same category but in all styles in the dataset.Protocol III: Free category in various domains. We randomly selected 10 images from each style and category to serve as query patches. We select the images from the dataset to form a complex gallery, without considering the style and category consistency with the query. This can be regarded as the most typical and generic CDOS setup, which is also the most challenging. Note that, without the limitation of both category and style, the gallery should have been the whole dataset, which is too large for implementation. This way, in our experiments, we randomly select 2k images as the gallery.Table 2 display the experimental results under the three protocols. It is evident that our method has achieved an increase of 2.6% in AP50 for base categories and 1.7% in AP50 for novel categories, resulting in an overall improvement of 2.4% in the protocol I. In the protocol II, there was an enhancement of 5.3% in AP50 for base categories and 5.0% in AP50 for novel categories, leading to a total elevation of 5.2% across the test set. In the most stringent protocol III, our approach realized a significant uplift of 4.2% in AP50 for base categories and 2.3% in AP50 for novel categories, culminating in an overall boost of 3.8% across the entire test dataset. Overall, the improvements are notably substantial.Additionally, we also follow the previous experimental setup of the classical one-shot object detection (OSOD) and conduct the experimental evaluation on MS COCO dataset [21] . The results, displayed in Table 3 , show that we exclusively used the foregroundbackground contrastive learning heads. The data indicate that our method achieved an approximate 0.1% increase in AP50 for both base and novel categories. Although this improvement may seem modest for the OSOD task, our method showed more significant enhancements in the CDOS task, suggesting that it is particularly effective for the challenges unique to CDOS.In this section, we conducted extensive ablation experiments to analyze the impact of each component of our proposed CrDoOS. We used AP50 as the primary performance metric.Component Analysis. We conducted experiments based on Protocol III to verify the effectiveness of the proposed contrastive learning module and feature random augmenter, summarizing the average precision across all categories on the test dataset in Table 4 . The method in the first row adopts the BHRL [32] . As shown in the second to fourth rows, the application of the contrastive learning for foreground features, background features, and the combined effect of both, resulted in improvements of 1.4%, 2.2%, and 2.7% AP50 on the test dataset, respectively. This benefit is attributed to the contrastive learning heads, which enhance the discriminability of the network-generated foreground and background features. As illustrated in the fifth row of the table, the feature random augmenter improved the BHRL [32] method by 1.1% AP50. This indicates that the feature random augmenter can increase the network's robustness across multi-domain datasets by introducing more style variations of training samples through perturbations of shallow features. The Impact of Different Random Augmenter Module. We explored the integration of different Single-Source Domain Generalization methods into our method and their effects. For instance, the feature augmentation parts of Learning to Diversify [30] and ACVC [5] could be seamlessly incorporated into our network, with the final results presented in Table 5 . It is evident that the methods of Learning to Diversify [30] and NP [8] , which directly perturb image features, are more conducive to enhancing the model's domain generalization capability. In contrast, the fixed form of feature augmentation in ACVC [5] is slightly less effective than the first two augmentation methods. This suggests that random domain augmentation can expose the model to a wider variety of styles, thereby strengthening its domain generalization ability. Method Learning to Diversify [30] ACVC [5] NP [8] AP 50 14.4 14.1 14.7In Figure 7 , we visualize the test results of BHRL [32] and CrDoOS on the MSOSB. It is evident that our CrDoOS can accurately detect targets of interest categories. Compared to OSOD methods, our proposed CrDoOS generates fewer false detections.  
 
 Appendix:  


Decision: This paper has received one WA, three BAs, one WR and one BR as initial scores. 

Pros:
The proposed dataset provides a benchmark to facilitate the training and testing of CDOS problems.
The proposed method achieved good performance.
The paper is easy to read. 

Cons:
Reviewers s4S4, fDBn, and fF3Q concern about the experiments. 
Reviewer 4urF highlights the lack of novelty. 
Reviewers fF3Q and 4nUK find many spelling mistakes.

The authors provide a rebuttal which addresses the reviewers' concerns. The AC agrees with the majority of reviewers that the paper should be accepted.

Reviews: review: paper_summary: paper_summary: - The paper is based on the one-shot object detection (OSOD) to propose a new benchmark, Cross-Domain Object Search, where the object category and the query may differ significantly (real and sketch). To solve the new problem and OSOD, the authors apply feature augmentation and contrastive learning in both foreground and background. The experiments are conducted in MS COCO and the new benchmark to demonstrate their performance.
strength_weakness: strength_weakness: - The new dataset seemed to be beneficial when one-shot object detection models should have the ability to detect objects belonging to a particular class in different domains.
limitations: limitations: - The paper needs to provide the implementation in detail. What is the fusion module and the value of hyper-parameter T in Equation (2)?

- Insufficient of related work. Please update the literature review with the latest related work. Some OSOD work like [32] performs the experiments on two standard datasets (PASCAL VOC, MS COCO) to demonstrate the effectiveness of their methods. The paper should conduct experiments on PASCAL VOC to provide comprehensive observations.

- Lack of ablation study. The contrastive learning for background (BG) and foreground (FG) significantly improves the model performance, and both are based on the memory bank with the key hyper-parameter being the length of the queue. These hyper-parameters for FG and BG are 128 and 65536, respectively. Does the performance of the model enhance when the length of FG increases?
paper_summary: paper_summary: The paper extends one-shot object detection (OSOD) by introducing Cross-Domain Object Search (CDOS), which addresses real-world scenarios where object categories and image styles differ significantly between the query patch and target images.
The authors' method employs foreground-background contrastive learning and domain-generalized feature augmentation to bridge these gaps effectively. 
They also introduce the Multi-Style Object Search Benchmark (MSOSB) to evaluate CDOS methods, demonstrating their approach's superior performance. 
Key contributions include defining the CDOS problem, developing a baseline method, and creating the MSOSB dataset for comprehensive evaluation.
strength_weakness: strength_weakness: The introduction of the Cross-Domain Object Search (CDOS) problem is both interesting and new, addressing practical scenarios where object categories and image styles between the query patch and target images can significantly differ.
The paper is well-written and easy to read.
The Multi-Style Object Search Benchmark (MSOSB) provides a new dataset and benchmark to facilitate the training, testing, and evaluation of CDOS methods
limitations: limitations: The novelty of the proposed method remains somewhat weak as it builds upon existing techniques like contrastive learning and domain generalization without distinctly highlighting how these implementations differ substantially from prior work
paper_summary: paper_summary: This paper proposes a new problem that advances over one shot object detection through what is termed cross-domain object search in which the style variation is high and the target object may or may not be in the target image. The authors propose a new method consisting of a new way to contrastively learn foreground vs background and a domain-generalized feature augmentation technique. They build a new benchmark that is better than competing methods. The authors present convincing experimental results.
strength_weakness: strength_weakness: 1. Well written paper.
2. Sound technical approach and interesting innovations.
3. Decent advance over state of the art methods for setting up benchmark.
4. Interesting new problem.
limitations: limitations: 1. While this work is interesting, with the advent of Large Vision-Language models, such work seems to be a bit behind the times. However, I am open to being convinced otherwise.
paper_summary: paper_summary: The paper introduces a novel approach to One-shot Object Detection (OSOD) by proposing a new problem termed Cross-Domain Object Search (CDOS). Unlike traditional OSOD, which assumes similar image styles between the query patch and target images, CDOS relaxes these constraints by allowing different image styles and decoupling object categories between the query patch and target images. The authors present a method incorporating foreground-background contrastive learning heads and a domain-generalized feature augmentation technique to address the object category gap and domain distribution gap. Additionally, they built a new benchmark dataset, the Multi-Style Object Search Benchmark (MSOSB), to evaluate their method, showing significant performance improvements over existing methods.
strength_weakness: strength_weakness: •	Novelty:
The introduction of CDOS addresses practical limitations in OSOD by allowing for varied image styles and categories, which is a significant and innovative extension of the OSOD framework.

•	Technical Correctness:
The use of foreground-background contrastive learning heads enhances the network's ability to classify objects correctly and distinguish backgrounds, reducing false positives. The domain-generalized feature augmentation technique is another strong point, improving the model's robustness against unseen domains.

•	Adequate Evaluation:
The authors present a new benchmark dataset, MSOSB, which is crucial for evaluating CDOS. The extensive experiments demonstrate the effectiveness of their method, with significant performance improvements compared to state-of-the-art methods.

•	Clarity:
The paper is well-structured and clearly explains the motivation, methodology, and results. The use of figures and detailed descriptions helps in understanding the complex concepts introduced.

•	Applications:
The proposed CDOS has broad applicability in real-world scenarios where object categories and image styles are diverse, such as in search engines, surveillance, and autonomous driving.
limitations: limitations: •	Lack of Comparison with Broader Range of Methods: While the paper shows improvements over existing OSOD methods, a comparison with more diverse methods, including those from related fields like domain adaptation and generalized object detection, would strengthen the evaluation.

•	Scalability Concerns: The approach might face scalability issues when applied to extremely large datasets or real-time applications due to the complexity of contrastive learning and feature augmentation.

•	Benchmark Dataset Size and Variety: Although MSOSB is a significant contribution, its size and the variety of styles might still be limited compared to the vast diversity encountered in real-world applications. Expanding this dataset further could provide a more comprehensive evaluation.

•	Domain Generalization Challenges: The paper addresses domain gaps through feature augmentation, but the effectiveness of this approach in highly divergent domains (e.g., medical imaging vs. natural scenes) remains unclear.
paper_summary: paper_summary: This paper promotes a new task: cross-domain Object Search (CDOS). The author developed a benchmark dataset and a baseline. The proposed method is better than the compared methods.
strength_weakness: strength_weakness: The paper is straightforward and easy to read. 
The proposed dataset is good and seems can be used for some purposes.
The proposed method achieved good performance, outperforming the compared methods on the CDOS task
limitations: limitations: The link to the proposed benchmark in the paper is broken.
The writing should be revised to correct English writing.
Compared methods are old, such as CoAE (2019), AIT (2021), BHRL (2022).
paper_summary: paper_summary: The paper introduces a new problem termed Cross-Domain Object Search (CDOS), which evolves the conventional One-shot Object Detection (OSOD) to accommodate object searches across varying image styles and domains, without the requirement that the target image contains objects of the same category as the query patch. The authors develop a new method involving foreground-background contrastive learning and feature augmentation to address the challenges of category and domain gaps. They also create a benchmark dataset, the Multi-Style Object Search Benchmark (MSOSB), to facilitate the training and testing of CDOS methodologies.
strength_weakness: strength_weakness: 1. The paper introduces a new problem, CDOS, which expands the scope of one-shot object detection and aligns more closely with real-world scenarios.

2. The approach integrates foreground-background contrastive learning with domain-generalized feature augmentation, innovatively addressing the dual challenges of object category and domain disparities in object detection.

3. The paper build a new dataset namely Multi-Style Object Search Benchmark (MSOSB), which provides a benchmark to facilitate the training and testing of CDOS problems.
limitations: limitations: 1. The introduction section does not clearly explain the rationale behind the proposed approach and how it enables the detection of novel object categories.

2. The evaluation section lacks comprehensive comparisons with state-of-the-art OSOD methods, especially those published after 2022.

3. The lack of code and detailed implementation details makes it difficult to reproduce the proposed method and verify the reported results.

4. Many spelling mistakes have been made throughout the manuscript, such as "pen-domain," affecting the readability and clarity.

5. It is necessary to provide details regarding how to implement stable diffusion for the generation of images.

6. Can the authors provide examples of failure cases or challenging scenarios where the proposed method struggles, and discuss potential ways to address these limitations?