Single Image Reflection Separation via Dual-Stream Interactive Transformers

Authors: qiming  hu
hainuo  wang
xiaojie  guo
Venue: NeurIPS_cc_2024_Conference
Published: 2024-05-03 13:04:42

Abstract: Despite satisfactory results on ``easy'' cases of single image reflection separation, prior dual-stream methods still suffer from considerable performance degradation when facing complex ones, i.e, the transmission layer is densely entangled with the reflection having a wide distribution of spatial intensity. The main reasons come from the lack of concern on the feature correlation during interaction, and the limited receptive field. To remedy these deficiencies, this paper presents a Dual-Stream Interactive Transformer (DSIT) design. Specifically, we devise a dual-attention interactive structure that embraces a dual-stream self-attention and a layer-aware dual-stream cross-attention mechanism to simultaneously capture intra-layer and inter-layer feature correlations. Meanwhile, the introduction of attention mechanisms can also mitigate the receptive field limitation. We modulate single-stream pre-trained Transformer embeddings with dual-stream convolutional features through cross-architecture interactions to provide richer semantic priors, thereby further relieving the ill-posedness of the problem. Extensive experimental results reveal the merits of the proposed DSIT over other state-of-the-art alternatives. Our code is publicly available at https://github.com/mingcv/DSIT.

Main paper: 
 introduction: 
 When images are captured through glass-like mediums (semi-reflectors), the reflected scenes appear together with the transmitted ones to different degrees, influenced by many factors such as the material of medium and the illumination of both scenes, among others [42, 62] . This phenomenon poses significant challenges in various fields, like multi-view stereo imaging, mobile photography, security surveillance, and autonomous driving [49, 43] . Therefore, successfully separating the superimposed layers can, on the one hand, enhance the capability of models to serve downstream applications. On the other hand, it paves the way for tackling a broader spectrum of layer-decomposition tasks, such as image denoising and watermark/obstacle removal [51, 20] .As a long-standing blind source separation problem, single image reflection separation (SIRS) has always been challenging, due to the severe ill-posedness of disentangling two natural image signals. Generally, the superimposed images I can be formulated as follows:EQUATIONIt consists of an additive combination of target transmission and reflection layers (T and R, respectively) and a residual component, denoted by the mapping C of the two layers. Note that C is used to describe the non-linear/linear attenuation of the two layers, thus representing a group of reflection models [23] . In the literature, two main routes of approaches have been delivered. One tendency (single-stream) is to treat the reflection layer as noise/degradation, merely modeling the transmission layer. Alternatively, the other rising trend (dual-stream) pays attention to the reconstruction quality Figure 1 : Schematic illustration of dual-stream interactive behaviors, including YTMT [22] , MuGI [23] , and our proposed Dual-Attention Interaction (DAI) mechanisms, where F T and F R represent the feature flows of transmission layer and reflection layer respectively. The superscript l of the feature flows denotes the number of building blocks traversed to derive the flows.of both the transmission and reflection layers. This work follows the latter principle because the reflection layer may also contain valuable information [43] and imposing constraints from both perspectives can better regularize the decomposition [22, 23] .The dual-stream schemes, with IBCLN [29] , YTMT [22] , and DSRNet [23] as representatives, attempt to estimate both of the two layers with Siamese networks that employ two sub-networks with identical architecture and shared weights. Particularly, YTMT and DSRNet advocated dual-stream feature interactions to facilitate the information flow between the streams and finally reconstruct the decoupled layers. Though being effective, the interaction mechanisms used in these two methods do NOT explicitly assess the correlation between dual-stream features during interactions, as illustrated in Fig. 1 (a) and (b). More concretely, assuming we have dual-stream features at a certain stage in the networks, previous interaction strategies directly pass the undesired information at the current stage (may be required again at subsequent stages) from one branch to the other without checking if the passed information is needed by the sibling. Actually, in intermediate blocks, some information is very likely delivered back and forth, making the separation process ineffective and inefficient. In contrast, the attention mechanisms in Transformers assign small weights to token pairs with low similarities, which are further suppressed by the Softmax function. In other words, employing a cross-attention mechanism seems to have the potential for improving dual-stream interaction. Furthermore, due to the presence of correlated scenes in reflection superposition phenomena across entire images, the task demands a keen perception of global information in both streams. Motivated by the above analysis, we propose a strategy, called dual-attention interaction, consisting of a dual-stream self-attention and a dual-stream cross-attention, as depicted in Fig. 1 (c), to extract both the intra-layer and inter-layer feature correlations explicitly.In addition, most of recent state-of-the-art methods [60, 48, 22, 23] adopted networks (e.g., Hyper-Column [60, 48, 22] and Feature Pyramid [23] ) pre-trained on high-semantic tasks to assist feature extraction. In this work, we argue that Transformers, thanks to their generalization and selective attention characteristics, should be more powerful to the target task. However, pre-trained Transformer models typically have trouble in dense prediction due to the lack of inductive biases [8] . To make Transformers suitable, we develop a Dual-Architecture Interactive Encoder (DAIE), which enables the interaction between semantically rich features extracted by a pre-trained Transformer and local dual-stream features extracted by a CNN. By this means, the Cross-Architecture Interactions (CAI) can balance the global and local perspectives and combine high-semantic priors with the low-semantic demands of the reflection separation.In summary, our primary contributions are as follows:• We propose a novel Dual-Attention Interaction (DAI) mechanism to energize Dual-Stream Interactive Transformers. DAI introduces the explicit correlation assessment within dual streams to effectively address the challenge of reflection separation;• We customize a bridge, namely the Dual-Architecture Interactive Encoder (DAIE), to connect the pre-trained Transformer model with the task of layer decomposition, which alleviates the inherent ill-posedness of the problem; • Through extensive experiments on multiple datasets, we demonstrate the efficacy of our design with superior performance over other SOTA competitors, both quantitatively and qualitatively. Moreover, the better generalizability compared to previous methods is also verified. 
related work: 
 Low-level Vision Transformers. Building upon the attention mechanism [3] , Transformers were initially developed by the community of natural language processing [41] , and soon became popular across various domains because of their remarkable modeling [2, 17] , scaling [24, 56, 36] , and transferring [10, 6, 4] abilities. Introduced by [12] , Vision Transformers (ViTs) have shown advantages on a large number of visual tasks [56, 34, 66, 57, 50, 8] .For low-level purposes, IPT [6] was developed to handle multiple restoration tasks with a shared standard Transformer body, which required a large number of parameters for good performance without suitable task-related priors. Swin Transformer [35] introduced the shifted window attention mechanism, which reduced the computational cost of attention while incorporating inductive biases for images, inspiring a series of subsequent works. SwinIR [33] equipped residual-in-residual structures [46, 61] with the Swin Transformer block, exploring its capability in low-level vision tasks. ELAN [59] performed multiple window attentions of varying sizes in parallel and fused them, using grouped four-directional offset convolution layers for local feature extraction and cross-window association. Chen et al. [7] introduced an overlapping attention mechanism in their HAT model to establish cross-window connections and employed same-task pre-training for better performance. Zhang et al. proposed ART [58] , which utilized a sparse window attention mechanism akin to dilated convolution, alternating it with window attention, thereby replacing the shifted window mechanism. UFormer [47] embedded window attention modules into a U-shaped network, which captured crosswindow associations beyond the current scale. Moreover, Restormer [55] introduced a transposed attention mechanism, resembling channel attention. While faster, this approach somewhat neglected spatial correlations. Retinexformer [5] employed this design for low-light image enhancement. DAT [9] alternated between window self-attention and window transposed self-attention to address spatial correlation deficiencies. Overall, these methods mostly opt to validate their designs on tasks like image super-resolution, which focus on reconstructing a single component, overlooking the intrinsic advantages of attention mechanisms in component decomposition tasks.Single Image Reflection Separation. Single image reflection separation, with looser data assumptions, relies more heavily on priors to alleviate its inherent ill-posedness. Traditional methods developed priors like edge sparsity [28, 27] , manual annotation [26] , or relative smoothness [31] , some of which were further leveraged by deep learning methods. Although multiple-image solutions [37, 1, 16, 39, 30, 18, 38, 51, 53, 19] have shown satisfactory performance facing weaker ill-posedness, these methods typically rely on sequences of images captured with rotating polarizers or moving cameras, which limits their applicability.In deep learning methods, CEILNet [14] applied the relative smoothness assumption to data synthesis and used an edge detection network to emphasize edge information. Zhang et al. [60] proposed a gradient mutual exclusion loss to promote edge sparsity and introduced the HyperColumn and perceptual loss to incorporate high-level semantics. ERRNet [48] aimed to expand the receptive field and utilized non-aligned images for data augmentation. These methods employed end-to-end singlebranch networks for estimation, but the lack of interaction between layers led to inefficiency and untidy separations. In another way, BDN [54] alternated between estimating transmission and reflection layers, considering their mutual dependency. RAGNet [32] estimated reflection first and then used its features to modulate the estimation of the transmission layer and mask. Dong et al. [11] emphasized the importance of the reflection layer, using multi-scale Laplacian features with LSTM for iterative estimation. RRW [25] utilized a cascaded reflection detector and remover and proposed a more reasonable data acquisition scheme. Song et al. [40] proposed a robust SIRR model based on a multiscale Transformer architecture, but it only learned to restore the transmission layer, and the usage of the Transformer was not well motivated. IBCLN [29] introduced a convolutional LSTM network, using a dual-branch structure for reflection and transmission reconstruction but lacking interaction between branches for cross-verifying the accuracy of decoupling. Hu and Guo proposed a dual-stream interactive reflection separation strategy [22] , called YTMT, assessing information utility through activation functions and exchanging low-value information, facilitating information retention and efficient interaction in the high-dimensional feature space. Their subsequent DSRNet [23] used MuGI for efficient interaction and introduced a non-linear residual component to estimate the overexposure during the layer superimposition, achieving state-of-the-art performance. However, neither the YTMT nor the MuGI mechanism explicitly assessed the correlation of exchanged information, which can introduce errors and lower the decoupling efficiency. To further alleviate the ill-posedness of the problem, a recent concurrent work [44] utilized wavelet priors and diffusion models to guide frequency-domain-based reflection removal. The other work [64] used natural language prompts for reflection separation, which, however, required accurate paired prompts, incurring additional costs. 
methodology: 
 Our overall architecture is illustrated in Fig. 2 ). These components are detailed in the following subsections.As depicted in Fig. 2 (b), our proposed DAIB embraces a dual-stream design, taking both transmission and reflection feature flows (F IN T and F IN R , respectively) as inputs. After that, a layer normalization and two parallel attention mechanisms, namely dual-stream self-attention (DS-SA) and dual-stream crossattention (DS-CA) are applied to the feature flows, capturing both inter-and intra-layer correlations. Subsequently, we derive the output feature flows F OT T and F OT R after passing the features through a layer normalization and a feed-forward network in the form of the dual-stream locality-preserving block (DSLP Block). The detailed computation procedure is displayed in Alg. 1. We provide a detailed explanation of the dual attention mechanism in the remainder of this subsection.Efficient Dual-Stream Cross-Attention Mechanism. We present a simple yet effective crossattention mechanism for dual-stream Transformer models via an extension of the self-attention mechanism. Given the feature streams of transmission layer F T ∈ R N ×C and reflection layer F R ∈ R N ×C , we concatenate them along the token dimension to form the input matrixX CA ∈ R 2N ×C = F T F R .We then compute the query Q CA , key K CA , and value V CA matrices for crossattention by applying the linear transformations:EQUATIONwhere W q , W k , W v ∈ R C×D denote the weight matrices that project the input features from Cdimensional channels into D-dimensional hidden representations. The cross-attention score matrix A CA ∈ R 2N ×2N are computed as:EQUATIONwhere the intra-layer termsF T W q W ⊤ k F ⊤ T and F R W q W ⊤ k F ⊤ Rrepresent interactions within the transmission stream F T and the reflection stream F R , respectively. The inter-layer termsF T W q W ⊤ k F ⊤ R and F R W q W ⊤ k F ⊤T indicate interactions between F T and F R . By denoting the Softmax function with a scaling factor 1 √ D as S(•), the output matrix Y CA is then calculated as:EQUATIONWe further simplify the form of Y CA by introducingG(Z 1 , Z 2 ) = S(Z 1 W q W T k Z ⊤ 2 )Z 2 W v , where Z 1 ∈ R N ×C and Z 2 ∈ R N ×Ccan be chosen between F T and F R , yielding the follows:EQUATIONWe finally obtain the output of the dual-stream cross-attention asF CA T = G(F T , F T ) + G(F T , F R ) and F CA R = G(F R , F T ) + G(F R , F R ), which are the combined effects of intra-layer and inter-layer interactions. Meanwhile, if we concatenate the dual-stream features along the batch dimension, obtaining the input matrix for the dual-stream self-attention mechanism X SA ∈ R 2×N ×C , we can further boost the parallelism of our model.Dual-Attention Design. Based on the above analysis, we can define the following dual-attention mechanism:EQUATIONwhere Q SA , K SA , and V SA are derived as in the DS-CA. Note that, the number of tokens is doubled in DS-CA compared to DS-SA. To reduce the computation burden, we employ a window-based attention mechanism for our dual-attention design. In this way,Q SA , K SA , V SA ∈ R 2N T ×N W ×D , Q CA , K CA , V CA ∈ R N T ×2N W ×D, where N T denotes the total number of windows, N W stands for the window size. B SA ∈ R N W ×N W represents the relative position bias [35] , which provides the same bias values with respect to the same distance between two tokens in a window. It is obtained by indexing a learnable lookup table B LUT SA ∈ R (2B LUT CA ∈ R (2 √ N W -1)×(2 √ N W -1)×3 by the layered relative indexes U CA ∈ R 2N W ×2N W . Each element is mapped by subtracting two 3-D points u ′ ij = t ′ (v i -v j ). v is a 3-D point in a layered window, with an additional dimension representing to which layer the token belongs, and t ′ (•) maps 3-D locations into single indexes.Dual-Stream Locality-Preserving Block. Since reflection separation is a dense prediction task, a primary consideration during architecture design is to maintain the local information. Therefore, we introduce the DSLP Block in our DSIT structure, which can be any convolutional dual-stream network modules. To focus on the enhanced interaction capabilities achieved by our dual-attention design, we avoid introducing additional novel local modules, opting instead to employ the MuGI Block [23] as the implementation of the DSLP Block. This approach isolates the performance gains attributed solely to the dual-attention mechanism, as evidenced in comparisons with models like DSRNet. One could, of course, substitute our design with alternative specialized dual-stream modules, potentially achieving even better model performance.As depicted in Fig. 2 (a), our proposed DAIE integrates both global and local prior extractors. The single-stream global features modulate the dual-stream local features hierarchically through crossarchitecture interactions (CAI), which are implemented with our proposed dual-attention interactive blocks. Formally, we have DAIB(F GP , F LP T ) and DAIB(F GP , F LP R ), where F GP and F LP T , F LP R represent global and local features respectively. F LP T denotes the transmission information flow and F LP R signifies the reflection stream. In an effort to provide an intuitive understanding of our DAIE design, we illustrate the feature visualization of DSIT in Fig. 3 . As shown, the dual-stream local priors focus on different components of the inputs but lack precise layer-specific attention. After being modulated by the global priors F GP via our proposed CAI and aggregated with the lower stream, we obtain F CAI T and F CAI R , which are significantly separated. Furthermore, the modulated dual-stream features are fed into a group of DAIBs, resulting in feature separations F DAIB T , F DAIB R of higher quality.Pixel reconstruction loss. To compel the consistency of the restored layers T and R in the spatial domain with their ground-truth scenes T and R and the layer superimposition modeling, we introduce the following loss function:EQUATIONwhere E denotes a learnable term to constitute the reflection superposition. ∥ • ∥ 2 and ∥ • ∥ 1 represent the ℓ 2 and ℓ 1 norms, respectively. α is a hyperparameter to balance the intra-layer and inter-layer fidelity. By enforcing the reconstruction loss with a learnable residual term, the restored layers appear to be cleaner and completed. Gradient reconstruction loss. Considering the gradient independence, as a traditional prior in blind-source decomposition, we simultaneously encourage the models to restore the ground-truth gradient and penalize the intersected gradient as follows:EQUATIONwhere ∇ denotes the difference operator of images. T↓n , R↓n are 2 n down-sampled version of T and R. ξ 1 and ξ 2 are normalization factors. The exclusion term, introduced by [60] , ensures the multi-scale exclusion of the two layers in the gradient domain.Feature reconstruction Loss. To promote the perceived quality of decoupled layers, we harness the following feature reconstruction loss:EQUATIONwhere ϕ i (•) represents the intermediate feature of the pre-trained VGG-19 model, where i ∈ {2, 7, 12, 21, 30} tells the layer id. ω i balance the weights of hierarchies.Total Loss. The full training objectives L total is defined as follows:EQUATIONwhere λ 1 = 1, λ 2 = 1, and λ 3 = 0.01 are coefficients for balancing different loss terms. 
implementation details: 
 Datasets. Our training datasets include both synthetic and real-world images. Following [23] , we design two data settings for fair comparison: I. 7,643 synthesized pairs randomly sampled from the Input Zhang et al. [60] ERRNet [48] IBCLN [29] YTMT [22] Dong et al. [11] DSRNet [23] RRW [65] Ours GT Quantitative comparison. As shown in Tabs. 1 and 2, we make a comparison between ours and state-of-the-art methods on five real-world datasets, including Real20 [60] , Nature20 [29] and three subsets of the SIR 2 Dataset [42] . It is noteworthy that our models trained on both data settings show superior performance over the alternatives on most testing sets, including those that involve extra real-world data [65] and language prompts [64] . The superiority is attributed not only to the improved generalizability afforded by the hybrid Transformer architecture but also to the dualattention interactive design that directly assesses intra-layer and inter-layer correlations, which shows impressive efficiency on SIRS tasks and has a high potential for other decomposition tasks.Qualitative comparison. To evaluate our proposed model aesthetically, we first present a visual comparison of estimated transmission layers in Fig. 4 . The two superimposed input images are Additionally, we specifically captured several in-the-wild test cases, as illustrated in Fig. 5 . Unlike standard benchmarks that often incorporate artificial glass plates, these cases utilize real-world reflective surfaces, including reflections from accumulated water-conditions entirely absent from the training set. The superior performance of our approach, in comparison to previous state-ofthe-art methods, underscores its robust generalization capacity and practical applicability. We further compare the reflection predictions between dual-stream reflection separation models in Fig. 6 . Notably, our DSIT model yields significantly more plausible results, exhibiting superior content fidelity and color accuracy, attributed to the enhanced information selection capabilities of our design.Model selection for GPE. Generally speaking, the design of our DAIE allows different choices of global prior extractors, which aim to provide semantic priors and/or non-local information. To elaborate on the efficacy of different GPE models, we compare the settings of using ResNet101 [21] , FocalNet-L [52] , PVTv2-b4 [45] , and Swin-L [35] models, which are all pre-trained on image classification tasks and finetuned on object detection tasks. As shown in Tab. 3, the ResNet101 as a CNN backbone provides limited global priors and poorly generalizes to the Real20 dataset. PVTv2 comprises few inductive biases, thus exhibiting inferior performance to the FocalNet and Swin Transformer, which preserve local dependency.Design of CAI operation. Our cross-architecture interaction mechanism is designed to exploit useful information from the priors extracted by GPE and LPE, and thus, various feature fusion operations can be taken. Here, we build a baseline "Add" that simply sums the information flow provided by the different prior extractors. Further, we propose two variants based on Cross Attention design ("CrossAttn") and DAIB ("DAIB") mechanisms, respectively. As Tab. 3 shown, cross-attention is not Input IBCLN [29] YTMT [22] DSRNet [23] Ours GT Figure 6 : Visual comparison of reflection layer predictions between previous state-of-the-arts and ours on the SIR 2 dataset. Our method shows significant superiority over previous dual-stream arts.capable of fusion cross-architecture information, leading to worse performance than simple addition, while our proposed DAIB bridges the local and global priors, obtaining superior results. Design of LRPB mechanism. LRPB provides an initial bias to each attention point according to the spatial and layer locations, injecting a layer-aware prior into the attention mechanism. Through the ablation study of whether or not to equip with LRPB shown in Tab. 3 "LRPB", we show its merit in handling the layer decomposition problem.The usage of RefMix. To evaluate the effectiveness of our proposed RefMix, a comparison of employing it or not is made in Tab. 3 "RefMix", showing that it aids the reflection separation.Due to the page limitation, more visual analyses are organized in the appendix. 
concluding remarks: 
 In this study, a dual-stream interactive Transformer has been designed to address the challenge of single image reflection separation. To harness high-quality priors from pre-trained Transformer models, we developed a dual-architecture interactive encoder, which can effectively fuse multi-source information with adaptive emphases. Additionally, we introduced a novel dual-attention interactive block that utilizes both an effective dual-stream self-attention mechanism and a layer-aware dualstream cross-attention module to separate the entangled features. Comprehensive experiments together with ablations have been conducted to verify the advances of our method. Looking forward, several interesting points deserve future exploration. For instance, larger vision foundation models can provide more substantial priors for low-level vision tasks. A relative position bias design tailored for a specific vision task will be also beneficial. Furthermore, our RefMix technique is likely to extend its benefits to other data-hungry visual tasks. These considerations may inspire future research on low-level Transformer designs. X SA 0 = DS_SA(X LN 0 )8:X CA 1 = DS_CA(X LN 1 ) 9: Split tokens back to dual streams: 10:F SA T , F SA R = Split(X SA 0 , dim=0) 11:F CA T , F CA R = Split(X CA 1 , dim=1) 12: Combine the dual-attention results with skip connections: F DA R = F IN R + F SA R + F CA√ N W -1)×(2 √ N W -1) through the predefined relative indexes U SA ∈ R N W ×N W . Each item of U SA is a mapped distance of two locations: u ij = t(p i -p j ).p is a 2-D point in a window, the coordinates of which fall between 0 and √ N W -1, and each 
 
 Appendix:  


Decision: The reviewers acknowledge the technical contribution of DAI mechanism and the strong performance advantage of this paper. The rebuttal was successful, resulting in an increased average score, with previous negative scores now becoming supportive. After careful discussion and consideration, we are pleased to inform you that your paper has been accepted. There is one issue from the AC-reviewer discussion regarding the distinction in the technical aspects compared to YTMT [22]. The ACs have investigated this matter and found the technical components to be distinguishable. Moreover, the performance of this submission is significantly superior to YTMT, both quantitatively on benchmark and visually in real cases. The authors are encouraged to provide a clearer distinction in the final version. Additionally, a common suggestion from the reviewers is that the writing can be improved. While the ACs believe this will not influence the acceptance of the paper, given its merits and strong performance, the authors are advised to revise the writing substantially to enhance clarity.

Reviews: review: paper_summary: paper_summary: This paper introduces a Dual-Stream interactive transformer to tackle the single image reflection removal task. The motivation of the proposed method is based on the drawbacks of existing methods that the dual-stream methods cannot assess the effectiveness of the information flowing between the two streams. Based on this analysis, this paper proposed . The proposed method constructs the encoder and decoder in dual-stream structure and crossover the outputs of the two sub-networks. Comprehensive experiments show that the proposed method outperforms the baseline methods.
strength_weakness: strength_weakness: 1. The methodology section is not described clearly. First, in line 146, the superscript symbol k on feature parameter F_T^{k-1} is neither explained in the text nor in the figures. Does this number k indicate the network is running in recurrent manner?  And in line 149, the superscript is used to indicates layer normed features. Also in Figure 1, the superscript of feature F is using l instead.  Second, I would assume F_T represents transmission features and F_R represents reflection features.  Third, in line 151, the DS-SA  and DS-CA should be explained or at least come with citations. 
2. Line 155 and Figure 2, how does DSLP FNN extract local features? How are local features defined? What is the difference between the input of DSLP and the input of the Transformer block? 
3. In the introduction section, this paper criticizes the existing dual-stream methods about the insufficient assessment on the information flowing between the two streams. However, I could not see the assessment of the data or any experiments analyzing the information. To make the claim of the paper valid, the authors should show evidences that the proposed method is able to make the information positively effective for reflection separation.
questions: questions: Generally, the paper is not well written. The equations, symbols and definitions are not clearly described in the text and therefore the idea of the methodology becomes confusing. The authors are high advised to polish the presentation of the manuscript and re-submit it. In addition, the paper does not provide enough evidence to its claim. It lacks novelty in that sense.
limitations: limitations: The limitation is addressed in the appendix: the proposed method fails to deal with the regions with dominant reflections.
paper_summary: paper_summary: The authors propose the Dual-Stream Interactive Transformer (DSIT) for single image reflection separation. DSIT is a dual-stream method designed for complex scenarios. Specifically, the authors design the Dual-Attention Interaction (DAI) to achieve feature aggregation of different dimensions through dual-stream self-attention and layer-aware cross-attention. They also propose the Dual-Architecture Interactive Encoder (DAIE) to leverage pre-trained Transformer models. Experiments demonstrate that the proposed method outperforms comparative methods.
strength_weakness: strength_weakness: 1. The implementation of CAI is not clearly explained. In L194, CAI seems to be through DAIB(F^{GP}, F_T^{LP}) and DAIB(F^{GP}, F_R^{LP}), while the proposed DAIB in the method deals with F_T and F_R. It is recommended that this be clarified in Figure 2 and the text to improve clarity. Additionally, the implementation of CAI is overly complex. Considering the high computational complexity of DAIB, a simpler implementation should be considered, such as directly using F GP in DAIB(F_T^{LP}, F_R^{LP}).
2. The authors do not provide detailed model settings, such as N_T, N_W, and the detailed structure of DSLP.
3. There is a lack of comparison between FLOPs and Params. The proposed method uses multiple attentions to aggregate features, which introduces high computational complexity.
4. In Figure 2, some parts of the Dual-Stream Information Flow overlap excessively, reducing distinguishability.
questions: questions: 1. Clarify the implementation of CAI and other model settings, such as N_T, N_W, and the structure of DSLP.
2. Provide a comparison of FLOPs in Tables 2 and 3.
3. The proposed DSIT uses a Transformer as a pre-trained model and claims to introduce prior knowledge. How does the model perform without using a pre-trained model? Additionally, why does DSLP not use pre-trained models?
limitations: limitations: The authors discuss the method's limitations and societal impacts.
paper_summary: paper_summary: The paper proposes a transformer-based network for the single image reflection separation task. It focuses on the network architecture design, proposing several tailored designs, including dual-attention interactive block (DAIB), dual-stream self-attention (DS-SA), and dual-stream cross-attention (DS-CA).
strength_weakness: strength_weakness: 1.	The method section is poorly written, making it difficult to follow due to the extensive use of abbreviations, some of which are not explained upon first mention, such as DS-SA and DS-CA.
2.	Despite a thorough reading of the methods and implementation details, it is unclear where the authors have applied the pretrained transformer. Specifically, I mean "pretrained transformer." Additionally, the placement and role of the proposed CNN-based network within the DSIT framework are not clearly clarified.
3.	For Eq. (3), the authors should clarify where the 'cross' is present. The equation seems to suggest a self-attention mechanism, as the query, key, and value all derive from the same features.
4.	In Fig. 3, it is unclear if the increased intensity of red indicates regions of higher attention. If so, I cannot distinguish the differences between the local and global priors as revealed by the figure.
5.	I think the current setting of single image reflection separation does not align well with typical real-world scenarios. Reflections in real scenes typically occur in localized regions, such as the reflection of a building with a glass facade. Therefore, relying heavily on synthetic reflections with minimal real-scene data might not be practical. The authors should consider focusing on real-scene reflections.
Overall, I think the novelty of the paper is limited, and the writing is poor.
questions: questions: Please refer to the weaknesses for details.
limitations: limitations: Given the advancements in deep learning for computer vision, the proposed reflection separation task focuses on synthetic pairs. I suggest the authors shift their research focus towards reflections occurring in real-world scenes.
paper_summary: paper_summary: This work introduces ADI, a new interactive dual-stream transformer framework for single image reflection separation. It incorporates a dual-attention interaction to explicitly model the dual-stream correlation for improved reflection separation, achieving impressive performance compared to other SOTA methods.
strength_weakness: strength_weakness: 1. The design is quite plain, involving inter-patch/intra-patch attention.
2. The choice of using gradient and feature loss should be analyzed in ablation studies to verify their contributions.
3. Model complexity and inference time are missing in the main manuscript, making it difficult to determine whether the main contribution comes from the larger model capacity or the proposed algorithm.
questions: questions: see the weaknesses.
limitations: limitations: NA

Comments: title: rating
Comments on rebuttal
After Rebuttal
comment: Thank you for your efforts in reviewing our manuscript. If you have any further concerns, we welcome continued discussion and would be happy to address them.
Thanks for your response. I'd like to keep my rating as 'Borderline accept'.
Thank you for your professional review, which has been instrumental in helping us improve our paper. We will further enhance the presentation of our revised version to eliminate the writing problems.
The authors' rebuttal works have addressed most of the weaknesses raised in the initial review. Generally, the main concern is addressed and I've raised the score to 5.
We are honored that our response helped the reviewer better understand our paper. We sincerely appreciate the thoughtful review of the reviewer and will further refine our manuscript accordingly.
Thanks for the rebuttal. The authors clarify the design details and compare Params/FLOPs.
Overall, the authors address my concerns, so I raise my score to 6.
We are glad that our responses have helped you gain a better understanding of our work. We are committed to improving the clarity and readability of our manuscript in the revised version. Thank you for your professional review and timely feedback. The constructive comments have significantly contributed to polishing our paper.
I appreciate the authors' efforts in their rebuttal, providing clearer explanations of the details and additional experimental results. These revisions have significantly addressed my major concerns regarding the novelty and contribution of the work, as well as the experimental design.

Furthermore, the authors' commitment to refining the writing, particularly in Section 3, in the final version is commendable. Given these improvements, I feel positively about the manuscript and have decided to raise my final score to 5: Borderline Accept.
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-AC