Dr. RAW: Towards General High-Level Vision from RAW with Efficient Task Conditioning
Authors: Wenjun Huang
Ziteng Cui
Yinqiang Zheng
Yirui He
Tatsuya Harada
Mohsen Imani
Venue: NeurIPS 2025 poster ()
Keywords: Computational Photography
Image Signal Processor (ISP)
Primary area: applications

Abstract: We introduce Dr. RAW, a unified and tuning-efficient framework for high-level computer vision tasks directly operating on camera RAW data. Unlike previous approaches that optimize image signal processing (ISP) pipelines and fully fine-tune networks for each task, Dr. RAW achieves state-of-the-art performance with minimal parameter updates. At the input stage, we apply lightweight pre-processing modules, sensor and illumination mapping, followed by re-mosaicing, to mitigate data inconsistencies stemming from sensor variation and lighting. At the network level, we introduce task-specific adaptation through two modules: Sensor Prior Prompts (SPP) and Low-Rank Adaptation (LoRA). SPP injects sensor-aware conditioning into the network via learnable prompts derived from imaging priors, while LoRA enables efficient task-specific tuning by updating only low-rank matrices in key backbone layers. Despite minimal tuning, our method delivers superior results across four RAW-based tasks (object detection, semantic segmentation, instance segmentation, and pose estimation) on nine datasets encompassing low-light and over-exposed conditions. By harnessing the intrinsic physical cues of RAW data alongside parameter-efficient techniques, our method advances RAW-based vision systems, achieving both high accuracy and computational economy. We will release our source code.

Tatsuya Harada Dr. RAW: Towards General High-Level Vision from RAW with Efficient Task Conditioning
Wenjun Huang" University of California, Irvine
Ziteng Cui The University of Tokyo
Yinqiang Zheng The University of Tokyo
Yirui He University of California, Irvine
The University Tokyo, RIKEN AIP
Mohsen Imani
University of California, Irvine
Abstract
We introduce Dr. RAW, unified and tuning-efficient framework for high-level computer vision tasks directly operating camera RAW data. Unlike previous approaches that optimize image signal processing (ISP) pipelines and fully fine- tune networks for each task, Dr. RAW achieves state-of-the-ar performance with minimal parameter updates and frozen backbone weights. At the input stage, we apply lightweight pre-processing steps, including sensor and illumination mapping, along with re- re-mosaicing, to mitigate data inconsistencies stemming from sensor variations and lighting conditions. At the network level, introduce task-specific adaptation through two modules: Sensor Prior Prompts (SPP) and task-specific Low-Rank Adaptation (LoRA). SPP injects sensor-aware conditioning into the network via learnable prompts derived from RAW pixel distribution priors, while LoRA enables efficient task specifpeiiiic tuning by updating only low-rank matrices key backbone layers. Despite minimal tuning, Dr. RAW delivers superior results across four RAW-based tasks (object detection, semantic segmentation, instance segmentation, and pose estimation) nine datasets encompassing various light conditions. By harnessing the intrinsic physical cues of RAW alongside parameter- efficient techniques, Dr. RAW advances RAW-based vision systems, achieving both high accuracy and computational economy. The source code is available here.
Introduction
Photos recorded in RAW format are increasingly adopted in computer vision tasks due to their captured minimally processed sensor responses [40; 25]. Meanwhile, compared with commonly used sRGB (Fig. 2())), RAW data maintains higher bit depth and preserves the intrinsic physical information. These advantages, combined with their linear relationship to scene radiance, allow RAW outperform sRGB images in various downstream visual tasks under real-world complex lighting conditions, including object detection [37: 20; 54; 58], semantic and instance segmentation [15; 12]. tracking [49], pose estimation [30] and so on.
To leverage camera RAW images for high-level visual perception, early works often skipped the image signal processor (ISP) stage, directly using RAW as input for downstream visual tasks [36; 64; 8), which failed to consider the gap between camera RAW images and sRGB pre-trained
"Equal contribution.
Conresponding author. email: m.imani uci.edu
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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weights. Since then, various approaches have been proposed to better improve task-specific perfor- mance (Fig. 2(b)), including dynamic ISP parameter tuning [59; 57: 50], additional RAW-to-sRGB encoder networks [16; 54], knowledge distillation [33], and visual adapter tuning [15]. However, existing methods mostly focus on optimizing full ISP & model weights for a single downstream task, ignoring efficient tuning and generalizable intrinsic across diverse real-world scenarios and tasks. Consequently, this gap leads to two key challenges: data inconsistency and tuning inefficiency.
Regarding data inconsistency, datasets for dif- ferent tasks are typically acquired using distinct camera sensors, while camera manufacturers adopt sensors with varying color response char- acteristics [40; I; 38]. At the same time, light- ing characteristics and environmental conditions during photography can also cause variations in scene illumination [20; 42]. Formally, the cap- tured camera RAW data can be represented as the following equation [5]:
DD RAW (Durg DRAW Adapter Diiry Flxc BrwSS ti Karaimer et
100 (0AP
72.1
PASCAL Normal (nA)
37.0
38 LIS (MAP by
PASCAL SAW Darc InsP S0)
0
41.2
89.7
60.8
ADEOCK RAW
PASIALL RAW
Cueeeep (WACU
RAW = { -(x,, A(x,A) I(A) dx, (1)
88.5
Durk jniCU %
Normal bee 99
where w denotes the visible light spectrum (380~720 nm), p the illuminant spectral power distribution, L the censor-dependent spectral re- sponse, and A the scene response. In practice, even for the same scene, the captured RAW would vary due to differences in and L. For perception, the inconsistency in data can make the downstream task challenging [47; 34], and current R.W-based perception models may further exacerbate inconsistency due to their task-oriented neural ISPs [15; 50].
Objject
Semantic Segmentation
Instance Segmentation
Pose Estimation
Detection
Figure I: Radar chart demonstrating the superior overall performance of our proposed Dr. RAW.
Meanwhile, luning inefficiency arises due to current RAW-based perception models normally fully- tuned for specific task with a single dataset (e.g., object detection [37: 20]). Both the ISP and backbone parameters are optimized to maximize task-specific performance (Fig. 2(b)). When switching tasks or datasets, training both the ISP and downstream network parameters is typically required. Otherwise, the cross-task performance tends to cause catastrophic degradation in existing RAW-based high-level frameworks (Fig. 2(d)). This critical observation underscores the necessity for developing tuning-efin luning-efficient RAW processing systems that eliminate the requirement for extensive network parameter adjustments.
In this work, we propose Dr. RAW, a training-efffcient unified solution that addresses the above chal- lenges. Unlike previous methods that require optimizing large number of ISP modules and training full backbone networks, Dr.RAW applies only two lightweight and task-relevant preprocessing steps, sensor & illumination mapping and re-mosaicing, while omitting other heavy ISP operations. At the network level, we maximize the utilization reducing trainable parameters, achieved by introducing additional ~2% of the backbone's 's parameters and freezing the majority of network weights during adaptation. (Fig. 2(d)).
IRGBBppetraiine knowledge while substantially
Our contributions could be summarized as follows:
We propose Dr. RAW, framework for RAW-based vision that achieves strong per- formance across tasks without end-to-end fine-tuning, instead leveraging a frozen RAW. pretrained backbone and lightweight, modular adaptation.
Pre-processing blocks and task-specific adapters enable Dr. RAW to perform optimally with high flexibility, while effectively mitigating the biases inherent in camera RAW data.
We demonstrate the effectiveness of Dr. RAW across 4 representative RAW-based high-level vision tasks under a total of diverse conditions (see Fig. 1). Including object detection [37: 20J. semantic segmentation [15], instance segmentation [12] and pose estimation [30]. Our method not only outperforms previous SOTA approaches in accuracy but also achieves superior training efficiency.

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RAW
sRGB
Backbone
RAW
+
Backbone
ISP
Sactbone ?
Seg
Sackioone 2
Backbone n
ISP Networt
Backbone 2
(a). sRGB-based visual system
(b). Current RAW- visual system
but -tt
Paramn Dntribution
RAW Adayte
RAW
Det
-2%
Backbone +
Utb b t
Tusking
Curs
Sansor Olumination Mapping
Adiatter
000:
Task-specfic Adapeer
params
(c). Our Proposed Method
(d). Efficiency Performance
Figure 2: (a). Diagram of sRGB-based visual system. (b). Diagram of the current RAW-based visual system. (c). Our proposed pipeline freezes the backbone parameters and tunes only few adapter parameters for different tasks. (d). Parameters distribution and tuning freeze backbone detection performance [20] compare with previous state-of-the of-the-art (SOTA) solution RAW-Adapter [15].
2 Related Works
2.1 RAW-based Computer Vision Tasks
In recent years, the advantages of camera RAW data have been extensively exploited for various low- level vision tasks such as image denoising [7: 61], super-resolution [56; 62; 29], demoiréing [60; 55], low-light imaging [9; 24), and reflection removal [27]. The rich details in camera RAW images, along with their structured noise distribution, have significantly advanced low-level vision and improved fine-detail reconstruction. Beyond the achievements in image quality improvement, recent advancements have also demonstrated that camera RAW data continues to play an increasingly important role in various high-level machine vision applications.
For RAW-based high-level vision tasks, mainstream approaches either optimize ISP structures and parameters for specific downstream tasks [59; 57; 45; 50; 39] refine selected intermediate ISP processes [54; 15; 36; 6; 52] (e.g., color correction matrices, look-up tables). For example, ReconfigISP [59] introduces an ISP module pool, then adopts neural architecture search (NAS) to select optimal ISP parameters. AdaptivelSP [50] further enhances this approach by using deep reinforcement learning to adaptively select key ISP parameters. Meanwhile, RAW-Adapter [15] leverages attention mechanisms to optimize parameters and enhance model-level connectivity.
Departing jointly tuning explicit ISP, research like Dirty-Pixels [16] replaces the ISP with stack of residual UNets encoder [41]. Chen et al. [12] removes the ISP part and additionally adds denoising blocks on the feature map to assist RAW-based instance segmentation. While Li et al. [33] distills an inverse ISP pipeline into new model to improve downstream perception. Despite advancements in RAW-based high-level vision models, existing methods are often fully tuned and overfit for specific downstream task, lacking the consideration of parameter-effict task transfer for different tasks.
2.2 Parameter-Efficient Fine- Sine-Tuning
Parameter-efficient fine-tuning (PEFT) focuses on freezing pre-trained models and either fine-tuning
only subset of network parameters [21; 31; 28] or adding extra parameters for training [10; 23; 22; 26; 46). Visual prompt tuning (VPT) [23] extends the concept of prompt tuning from natural language processing to computer vision, enabling efficient adaptation of pre-trained models without modifying their core architecture. Instead of altering the parameters in the model, learnable prompts guide the model adapt to new tasks while preserving its pre-trained knowledge. While [23] introduces task- specific modifications at the input feature level, low-rank adaptation (LoRA) [22] injects trainable low-rank decomposition matrices into pre-trained weights, optimizing internal weight updates in
low-rank manner. Inspired by these works, extend such techniques to RAW-based applications.

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Tunable Weights
Backbone
x4
Frozen Weights
W d Kipkea
Tokemization
Det Head
Sog Head
Mapped Image
14
Layer
Head
Dr. RAW Layer ė
Me-Mosacing Block
Pre-processing Blocks
R
L
H mulipiictioon
Muminiaton
Mapping Bock
Sensor Mapping Block
(b)
Up
LoRAA
28.45%
RGB-uv Histogram
1.14M
4:4
Vieigree
We 2
Input raw-RGB data
(a)
LOFA
(d)
Hird
Exthor
(c)
Figure 3: Overview of Dr. RAW. (a). The input RAW is processed by pre-proces pre-processing blocks and passed to downstream module with task-specifer adapters. (b) Re-mosaicing block in the pre- processing stage. (c) Task-specific adapter design. (d) Parameter distribution across Dr. RAW.
3 Method
The overall pipeline of our method is illustrated in Fig. 3 (a). Dr. RAW incorporates pre- prepprocsssing blocks that map RAW data from various conditions and handle data inconsistency. The mapped RAW data is then processed by a versatile backbone augmented with sensor-prion prompts and fine-tuned using LoRA [22] to effectively adapt to downstream tasks. In Sec. 3.1, we detail the design of the pre-processing blocks. In Sec. 3.2, we describe the sensor prior fine-tuning process.
3.1 Pre-processing Blocks
Real-world camera imaging systems subject continuous variability in both sensor differences and illumination conditions (Eq. 1), which introduces significant changes in pixel distribution, thereby further complicating model optimization across different datasets and tasks [34]. Even data collected by the same camera exhibit considerable variations, as shown by the blue dots in Fig. 4.
To alleviate these variances, incorporate sen- sor & illumination mapping blocks followed by lightweight ce-mosaicing block process in- put RAW data. Motivated by the white balance design in [2], which first estimates 3x3 matrix M to eliminate sensor differences and then a 1 1x3 matrix L for illumination estimation, adopt a similar two-stage approach. As illus- trated in Fig.3 (a) left, we first extract the RGB- uv histogram [18] from the input demosaiced RAW data to get its pixel distribution. The his- togram is then fed into sensor mapping block estimate the matrix Max3, which is then mul- tiplied with RAW data. The transformed image subsequently passed through an illumination mapping block to estimate the illumination ma- trix Lix3- Both sensor mapping and illumination mapping blocks are composed of three simple convolution blocks.
RAW Images
Mapped Images
Covariance Bligse RAN Images Cousttence Ellis Mapped
Component:2 2 (5.4%)
Component (74.3%)
Figure 4: PCA projection of RGB-uv histogram of RAW images [20] and mapped images I.

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After that, a re-mosaicing block (Fig.3(b)) R is further added to alleviate sensor- and scene- deeenddepend artifacts, which is also motivated by findings that reveal unequal contributions of the color channels in camera RAW data [52]. As shown in Fig. 3(b), we design the re-mosaicing block R. as a U-shaped network (details see App. A). We adopt a gating operation, a lightweight nonlinear interaction mechanism that replaces conventional activations. Specifically, the feature map is evenly split along the channel dimension into two halves, and an element-wise product is computed between them. Here, max-pooling is used for down-sampling while pixel-shuffle [43] is employed for up-sampling at each stage of the U-shaped architecture in a learnable way. The generation of mapped image is shown as follows:
I = (RAW & Maxa @ f1x))
(2)
We show the PCA projection of histograms in input RAW images and mapped images in Fig. 4, it shows that the pre-processing blocks help to reduce the spread of the data distribution during training.
3.2 Sensor Prior Efficient Tuning
For the downstream module, current RAW-based visual systems [16; 15; 50] typically require full tuning to avoid performance degradation (see Fig. 2(d)). However, this heavy reliance on updating backbone parameters presents bottleneck for training efficiency (see Fig. 3(d), backbone accounts for over 70% of the total parameters). To this end, we introduce two simple and effective components that inject task-related knowledge into the backbone an efficient way.
Taking into account the sensor difference and illumination condition, we propose a sensor prior prompt (SPP) tuning. Specifically, we adopt set of learnable prompts P (px E Rd E N, S k < K) to convey the knowledge gained from the pre-processing block to the backbone of the downstream module. P is generated by projecting the concatenation of the sensor mapping matrix M and the illumination mapping matrix L into a few d-dimensional embeddings:
P = FFF L1x3))
During training, we only fine-tune the P while keeping the weights in the backbone frozen. Depending on the backbone architecture, we integrate SPP in different ways, which is discussed in App. B.3.
After enabling SPP, the core operation in the transformer, self-attention (SA) (see Eq. 10 in Appendix) in each layer becomes:
"'KK'') V*) = softmax QKK Attn'(Q' (Q','',
(4)
Vd
where E is the image patch embeddings, and Q' = [P, K' [PEEWKK V = EJW. Eq. 4 can therefore be decoupled as:
PPP(PW PWo y"1 EWo(PWR)) (EW x JT|
[PW7] EWv
Attn' (Q', K", V') = softmax
(5)
The off- off-diagonal terms in the attention matrix (i.c., PWQ the SPPs interact with the original image path embeddings in the attention computation, and the top-left term PWq(PWx) )" provides sensor-specifie influence the attention. On the other hand, the term PWv represents the influence imposed on the original image patch embeddings by SPPs.
and EWo
mean that
In addition to SPP, further apply LoRA [22] to selected layers of the backbone to enhance task adaptability while preserving efficiency. As illustrated in Fig. 3(c), LoRA injects trainable rank- decomposed matrices WA, WB into the attention without modifying the original weights, enabling us to achieve effective fine-tuming with minimal parameter overhead.
W Wt, + AW Wt, + W ,h (Q.K.V
(6)
To stabilize the tuning, AAj is initialized with Gaussian distribution, and W is initialized with all zeros. By jointly optimizing LoRA and SPP, we retain the benefits of strong pretrained backbone while introducing task-specific adaptability in lightweight manner. This hybrid strategy significantly reduces the number of trainable parameters (see Fig. 3()) and facilitates fast adaptation to diverse downstream tasks with limited computation. Please find App. B.4 for more details.
5

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Experiments
4.1 Experimental Setup
Datasets. We conducted experiments semantic segmentation, object detection, instance seg- mentation, and pose estimation, utilizing a combination of various synthetic and real-world RAW image datasets. For object detection, we adopted real-world datasets, PASCAL RAW [37: 15] and LOD [20]. For semantic segmentation, we utilized ADE20K RAW [15]. For instance segmentation, we utilized LIS [12]. As for pose estimation, we used ExLP [30]. We only used the low-light images of LIS and ExLPose, consistent with other tasks. Refer to App. C for more details.
Implementation Details. Dr. RAW is built the open-source computer vision toolboxes: mmdetec- tion [1I], mmsegmentation [13], and mimoos [14]. We conducted comparative experiments with the current SOTA methods. All comparison methods adopt the same data augmentation, mainly including random crop, random flip, multi-scale test, etc. We use mean Intersection over Union (mloU) to evaluate semantic segmentation, and mean Average Precision (mAP) evaluate instance segmentation, object detection, and pose estimation performance. The backbone of Dr. RAW is a Swin Transformer tiny (Swin-T) [35]. Since most widely-used backbones are pretrained on RGB images, this introduces a domain gap when applied to RAW images and impacts the performance of downstream tasks. To address this issue, we pretrain the backbone on the large-scale RAW dataset, i.c., AED20K RAW. Once pretrained, the backbone is frozen and transferred to other tasks, Fig. 3(d) presents statistical breakdown of the parameter count for each component of Dr. RAW.
Refer to App. D for more details. Due to page constraints, we present only the primary results for each task here. Additional results can be found in the corresponding subsections in App. E.
4.2 Semantic Segmentation
Tab. provides comparison of semantic segmentation performance across multiple methods, along- side the parameter efficiency of each model. Traditional ISP-based methods, such as Demosaicing [37] and Karaimer et al. [25] show relatively consistent performance under normal and over-exposed conditions, but their performance drops significantly in dark-light conditions. InvISP [53], while competitive in well-lit scenes, deteriorates drastically in the dark, underscoring its sensitivity illumi- nation variations. Similarly, SID [9] and DNF [24] are designed primarily for low-light conditions and thus only report results for the dark scenario. Among the sharming-bused alternatives, Dirty- Pixel [16] and RAW-Adapter [15] show improved robustness across lighting conditions. RAW-Adapter, in partic- ular, yields the highest mloU under normal illumination. However, both methods come with relatively high parameter costs, with RAW-Adapter using 45.16 million parameters, all of which are tunable.
Dr. RAW achieves the best overall performance under challenging illu- mination. It attains SOTA mIoU in both over-exposed and dark set- tings, while maintaining competitive results in normal lighting. Notably, Dr. RAW requires fewer tunable pa- rameters-onn -nlyy corresponding to tunable ratio of 42.9%, which is signif- icantly lower than other competitive approaches. This demonstrates the ef- fectiveness of Dr. RAW's parameter- efficient design, striking superior balance between segmentation accu- racy and model compactness, particu- larly under diverse lighting conditions.
mIoU over-exp
Method
of Ma)ams((M)*
normal
dark
Demosacing [37]
46.18
45.03
34.97
Karaimer et al. [25]
46.91
42.15 15
20.95
InvISP [53]
46.08
44.06
5.02
SID [9]
44.64 (44.64/ 100%)
27.18
DNF [24]
35.86
ROD [54]
46.03
42.92
37.80
Dirty-Pixel [16]
48.92 (48.927 100%
46.19
44,13
36.93
RAW-Adapter [15]
45.16 /6..111611 100%)
46.57
44.19
37.62
Dr. RAW
47.74 (20.51/ 42.9 %
46.29
45.28
38.46
The number parameters reported in the format: =(y/z). where x is total, y is tunable, and = = ы/u.
Table 1: Semantic segmentation results on ADE20K RAW. Best results are bolded and second-best underlined.
Fig. 5 visualizes some examples under various illuminations.
4.3 Object Detection
Tab. 2 presents object detection performance the RASCAL-RAW dataset, while also accounting for model efficiency terms of tunable parameters. We compare three tuning strategies: frozen, where only the detection head is trained with a fixed fully- where both backbone

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RAW Image
Ground Truth
Dr. RAW (Our)
Dirty Pixel
RAW-Adapter
Karaimer at.
InviSP
SID
DNF
LitelSP
RAW Image
Ground Truth
Dr. RAW (Ours)
Dirty Pixel
RAW-Adapter
COF
Karaimer al.
InviSP
SID
DNF
LitelSP
comum
cotumn MEnt
Figure 5: Qualitative comparison of semantic segmentation under different illuminations (low-light y and over-exposure
mAP over-exp
Method
Setting, No. of params(M) J.
normal
dark
Method
Setting
mAP
Default ISP
tuley- 39.62 (39.62/ 100%)
Frozen, 39.62 (10.93 / 28%)
$1.4
86.7
Default ISP
fully-tuned
frozen
65.6
46.8
Karaimer af. [25]
fully-tuned, 39.62
frozen, 39.62 (10.93 / 28%)
100%)
89.3
90.1
87.6
89.1
83.1
Direct (RAW)
fully-tuned
frozen
47.5
67.2
Demosacing [37]
fully-
frozen, 39.62 (10.93 / 28%%
frozen, 39.62 (10.93 / 228%)
100%)
88.1
88.7
89.3
89.4
90.1
86.5
72.3
Karaimer et al. [25]
fully-tuned
frozen
40.6
62.5
InvISP [53]
fully-tuned. 39.62 (39.62/ 100%)
89.6
89.8
78.5
Dirty-Pixel [16]
fully-tuned
61.6
Dirty-Pixel [16]
tully-tumed, 39.42 (39.42/ 100%))
89.7
89.0
83.6
RAW-Adapier [15]
fully-tuned
62.1
RAW-Adapter [15]
fully-tuned. 37.11 (37.11 100%)
89.7
89.5
86.6
Dr. RAW
adapter
72.1
Dr. RAW
adapter, 38.67 (1 1.36 / %)
90.4
90.3
89.7
Table 3: Object detection per-
Refer Tab. for the format of No. params. Table 2: Object detection performance across different meth- ods on RASCAL-RAW (normal / over-exp/ dark).
formance on LOD.
and head are trained; and adapter, our proposed task-conditioned tuning that trains lightweight adapter modules and the detection head while keeping the backbone frozen. Dr. RAW consistently outperforms traditional ISP-based pipelines and recent learning- based methods across all lighting conditions. It maintains a clear advantage over fully-tuned versions of other RAW processing pipelines, particularly exhibiting enhanced robustness in the challenging dark environment where methods like InvISP [53] show marked performance degradation. The baseline Default ISP yields the lowest scores, highlighting the efficacy of specialized RAW domain adaptation. Beyond accuracy, the table provides insights into computational efficiency, specifically focusing on the number of tunable parameters. While possessing total parameter count (38.677 (7.) comparable to Dirty-Pixel [16] (39.42M) and RAW-Adapter [ [15] (37.11M), Dr. RAW employs an dlaater-based strategy requiring only 11.36M parameters (29% of the total) to be tuned. This contrasts sharply with the others, both of which necessitate tuning 100% of their parameters. Tab. 3 presents the results on LOD. Among all evaluated methods, Dr. RAW achieves the highest mAP. Specifically, Dr. RAW surpasses the strongest fully-tuned baseline by a substantial margin of +4.9% while only adapting fraction of the model parameters. Qualitative comparison shown in Fig. 6(a). These results collectively demonstrate that Dr. RAW not only eliminates the need for an ISP and full model tuning but also achieves new SOTA. The proposed components are both effective and efficient, enabling substantial gains even in the absence of paired supervision intensive parameter updates.
7

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Object Detection
(Ours)
Karalmer RAW-edapter
(b). Pose Estimation
Ground Truth
RAW (Ours)
Keraimer
SID
InvISP
(c). Instance Segmentation
Ground Truth
(Ours)
Karaimer
Direct(RAW)
Default ISP
Figure 6: Qualitative comparison of (a). object detection on the PASCAL-RAW [37] dataset under
low-light J and over-exposure * conditions, (b). pose estimation on the ExlPose [30] dataset and (c). instance segmentation on the LIS dataset [12].
4.4 Pose Estimation
The quantitative results presented in Tab. report the mAP across several low-light testsets (LL- N, LL-H, LL-E, LL-A), comparing Dr. RAW against relevant prior methods. Operating under the constraint utilizing only dark RAW images, Dr. RAW consistently establishes new SOTA benchmark. It achieves superior mAP scores compared to all fully-tuned baselines within this category, including Direct (RAW), Karaimer et al. [25], and InvISP [53], across the evaluated low- light testsets. This uniform outperformance underscores the robustness and efficacy of Dr. RAW in extracting salient pose information directly from RAW sensor data, irrespective of the specific low-light challenge. Qualitative comparison is visualized in Fig. 6(b). Additional experimental results against methods leveraging paired RAW-dark and RAW-normal supervision can be found in App. E.
4.5 Instance Segmentation
Tab. 5 summarizes the instance segmentation performance of our proposed method, Dr. RAW, against a variety of methods. Within the category trained only on RAW-dark images, Dr. RAW demonstrates compelling performance. The substantial gain in mAP7s. which demands higher localization accuracy. highlights the quality of the instance masks predicted by our method even under challenging low- light conditions using only dark RAW input. performance within the unpaired setting using an efficient adapter-based tuning strategy, rather than requiring full end-to-end fine-tuning like the Direct and Karaimer el al. baselines. This underscores the efficacy of our proposed components in Dr. RAW. Fig, 6(c) illustrates representative examples of
noteworthy that Dr. RAW achieves this SOTA

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instance segmentation results. Additional results against methods leveraging paired RAW-dark and RAW-normal supervision can be found in App. E.
Mathod
Setting
LL-H
Method
Setting
mAP
mAASo
mAPps
IAAP
mAP'S
IAP""
Direes (RAW)
fully suned
Liozen
12.11
36.1
26.9
7.5
18.5
3.6
29.9
8.3
Default
fully tuned
frowen
23.3
36.1
42.3
58.4
37.6
23.0
41.9
25.8
67.7
22.9
44.1
Karsimer of. (25)
fully suned
Bizzn
35.4
9.7
29.2
6.9
19.1
3.6
30.1
7.2
Direct (RAW)
fully unedd
frosen
40.
27.6
67.4
614
27.4
11.2
44.9
30.1
56.2
20.1
27.7
48.6
IndIPP (53)
Fully suned
Bizzn
30.5
6.4
17.7
3.3
2.0
8.2
19.9
4.4
Karaimer er at.
Enooon fully-tuned
54.6
18.7
55.1
34.7
35.5
18.8
20.9
59.7
63.9
43.1
42.4
17.4
Dr. RAW
adapter
37.11
30.9
19.6
38.4
Dr. RAW
adapter
41.3
63,0
42,9
63.6
70.3
48.1
Table 4: Pose estimation performance across different methods trained solely on RAW-dark images.
Table 5: Instance segmentation performance across differ- ent methods trained solely on RAW-dark images.
4.6
Ablation Study
STP
LoRA
Proepoonniing
LOD
PASUAL HAW (ecmma))
PASCAL RAW tweressg
PASUAL RAW
(derk)
Buckbone
Dussse
LOD
PASCAL RAW
PASCAL RAW esp)
PASCAL RAW
(dark)
8144
55.11
Swis-T (RAW)
8899
969
19.5
Swin-T (milk)
6788
50.6
60.8
ML3
9011
2014
VIT (inlk)
65.9
20.4
Table 7: Effectiveness of RAW pretraining and gen- eralizability across backbone architectures.
Table 6: Component-win wise ablation.
Tab. presents component-wise ablation study evaluating the impact of the pre pre-processing block, SPP, and LoRA across the object detection datasets. Without any of these components, performance is significantly lower, particularly under challenging lighting (e.g., 43.6 mAP LOD and 59.9 on PASCAL RAW (dark)). Introducing the pre-processing block alone yields substantial gains, especially under dark conditions (+21.2), highlighting its effectiveness in stabilizing illumination. Adding LoRA alone also improves results across all datasets, particularly for dark scenes (from 59.9 89.3 5). demonstrating its capacity for efficient adaptation. Combining the pre-processing block and LoRA provides further improvement, especially on LOD (426.2 over baseline), confirming their complementarity. Finally, integrating VPT with the pre-processing block and LoRA achieves the best results across all datasets, with 72.1 mAP on LOD and over 90 mAP on all PASCAL RAW variants. This indicates that our full model benefits from both robust pre-processing and parameter-efficient tuning mechanisms, achieving consistent gains under diverse lighting conditions.
In addition, we evaluate the performance using three different backbones: Swin-T pretrained on the large-scale RAW dataset (denoted as Swin-T (RAW)). Swin-T pretrained on ImageNet- 1 (Swin- T (inlk)), and Vision Transformer pretrained ImageNet- 1k (VIT (inlk)). As shown in Tab. 7, Dr. RAW with Swin-T (RAW) consistently achieves the best performance across all evaluation settings, including LOD and various PASCAL RAW conditions. The substantial performance gap between Swin-T (RAW) and Swin-T (in k) highlights the importance of pretraining on RAW data, which preserves richer visual information compared standard RGB inputs. Furthermore, when our techniques are applied to the ViT (inlk) backbone, the model still achieves strong results, demonstrating the generalizability of our proposed components beyond a specific architecture.
mAP PASCAL RAW exp)
Method
PASCAL RAW (sormal)
PASCAL RAW (dak)
LOD
Koraimer
(froen,
$9.3
87.6
83,1
40.6
Downstream Task Dataset
Backbone Pre-training Dataset
(ally--tuned,
Kasainer
98.0
90.1
87.9
LOD
ADENIK (RAW. 20210 images)
72.1
(froeen
Direst
$8.1
$9.4
86.5
47.5
LOD
LOD
PASCAL RAW 4259 images)
in
images)
(fally-tunedd inlk)
Dires
90.0
90.1
87.9
67.2
LIS
LJS
PASCAL-RAM RAW (RAW. 4259 images)
ADEZOK (RAW. 20210 images)
41.2
37.8
(adapoes,
DeR.
89.7
88.4
67.8
138
in (RGB, 1281 167 images)
35.9
(adapter, RAW/
Dr..AW
90.4
90.3
89.7
72.1
Table 9: Effect of backbone pre-training dataset on
D.RAW
downstream tasks.
(fally-tunedd inlkk)
90.6
00.2
73.8
Table 8: Effectiveness of adapter tuning strategy across pretraining datasets.

---

Considering the large-scale RAW pretraining is not available for our baselines, we trained Dr. RAW using full fine-tuning on inlK to eliminate the domain mismatch that may bias the results. As shown in Tab. 8, when all methods are fully fine-tuned and initialized with K pretrained weights, Dr. RAW significantly outperforms the baseline (Direct RAW). For example, on the LOO dataset, the performance improves from 67.2 to 73.8 (+6.6). In addition, the results also demonstrate that RAW-based pretraining can substantially improve performance. For instance, Dr. RAW improves from 67.8 (in KnlK-preed K-pretrain) to 7 (RAW-pretrain), achieving gain of +4.3. Moreover, compared to fully fine-tuning with In pretraining, Dr. RAW reduces the number of trainable parameters by approximately 71%, yet the performance drops by only -1.7 (from 73.8 to 72.1).
The availability of large-scale RAW datasets is a practical consideration. Therefore, we conducted an experiment with the scenario where large-scale synthetic RAW data like ADE20K-RAN is not available, as shown in Tab. 9. We pre-trained a new backbone using only the much smaller PASCAL- RAW dataset (4259 images). We then evaluated this backbone on the LOD and LIS tasks. This study shows that while large-scale pre-training yields the best results, our method still achieves strong performance when pre-trained on smaller, more accessible RAW dataset, attributing the performance gains come from both the RAW pre-training and our adaptation modules.
To directly evaluate the generalization to unseen sensors, we conducted a zero-shot experiment. We took Dr. RAW trained on the PASCAL RAW dataset (captured with a Nikon DSLR camera) and tested it without any fine-tuning, on RAW images of the same scenes captured with two unseen sensors, iPhone X and Samsung (we adopt [51] to do RAW-to-RAW mapping). The model achieves 88.3 mAP and 88.8 mAP, respectively. Compare with original performance 90.4 on Nikon, the results show only a marginal drop when tested on unseen sensors, demonstrating Dr. RAW's generalization.
4.7 Impact of Pre-processing Block
To assess the effectiveness, we analyzed RGB-uv histograms of RAW and mapped images. The RGB-uv histogram captures color distributions in log-chromaticity space [17], where each image represented by high-dimensional vector formed by concatenating 2D histograms of the R, G, and B channels over the (u, v) plane [3]. We visualize these histograms using PCA, as shown in Fig. 4. Each point denotes one image's 's chromaticity distribution, and ellipses illustrate group-wise covariance in the projected space. RAW images exhibit long, curved spread with large and anisotropic covariance ellipse, reflecting significant chromatic variability. In contrast, the mapped images form tight, centered cluster with reduced and more isotropic covariance, demonstrating improved consistency in chromaticity. Quantitatively, the average intra-class distance decreases from 0.380 (RAW) to 0.259 (mapped images), indicating reduced sample variability. The centroids of the two groups are separated by 0.32 in Euclidean distance, confirming a noticeable shift in color representation. The first two principal components explain 74.5% and 5.4% of the variance, capturing the dominant structure of chromatic change. These improvements simplify downstream learning by reducing feature noise and enabling more stable, efficient optimization.
5 Conclusion
We present Dr. RAW, unified and parameter-efficient framework for high-level vision tasks operating directly on RAW images. By combining lightweight sensor-aware pre-processing with modular adapter-based tuning strategies, Dr. RAW achieves SOTA performance across object detection, semantic segmentation, instance segmentation, and pose estimation under diverse lighting conditions. Notably, Dr. RAW minimizes task-specific parameter updates while maintaining robustness and generalizability. Extensive experiments across nine RAW datasets confirm that Dr. RAW effectively bridges the gap between efficient adaptation and gegh-performacce perception in RAW domains.
Acknowledgements
This work was supported in part by the DARPA Young Faculty Award, the National Science Founda- tion (NSF) under Grants #2127780, #2319198, #23218 1840, #2312517, and #2235472, the Semicon- ductor Research Corporation (SRC), the Office of Naval Research through the Young Investigator Program Award, the U.S. Army Combat Capabilities Development Command (DEVCOM) Army Research Laboratory under Support Agreement No. USMA 21050, and Grants #N0001 1000014-1111-2225 and N00014-22-1-2667 Additionally, support was provided by the Air Force Office of Scientific Research under Award #FA9550-22- 1-0253, along with generous gifts from Xilinx and Cisco.
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A Re-mosaicing Block Architecture
The re-mosaicing block (Fig. 3(b) serves as the fundamental building unit of our model, following minimalist design philosophy that balances computational efficiency and representational power. Unlike traditional convolutional blocks that heavily rely on complex attention mechanisms or deep non-linearities, the re-mosaicing block adopts ages simple convolutional operations, channel-wise normalization, and efficient feature modulation strategies extract and refine features.
streamlined yet effective architecture. It lever-
core component in the re-mosaicing block is the use of a simple gating mechanism (SG), a lightweight nonlinear interaction mechanism that replaces conventional activations such as ReLU or GELU. It is inspired by findings that color channels contribute unequally across tasks and lighting conditions [52]. Specifically, the input feature map is evenly split along the channel dimension into two halves, and an element-wise product is computed between them:
SG(x)
I2, where [x1,
This operation enables direct and efficient channel-wiss interaction without introducing additional parameters or computational overhead. By operating in-place and avoiding expensive non- non-linear func- tions, SG significantly reduces memory access costs while still enabling expressive transformations, making it particularly well-suited for real-time and resource-constrainee applications.
In the decoding path, the block integrates PixelShuffle [44] for upsampling, which has become preferred alternative to transposed convolutions due to its artifact-free nature and computational simplicity. PixelShuffle rearranges a tensor of shape (C y² H, W) into higher-resolution tensor of shape (C,r r,, rW), where r is the upscaling factor. This deterministic rearrangement avoids the checkerboard artifacts often introduced by transposed convolutions and preserves fine spatial detail, which is crucial for high-fidelity image enhancement.
The overall structure of the re-mosaicing block is symmetric and modular, consisting of two sequential convolutional segments separated by normalization and nonlinear interactions. This design, free from transformer-styy self-attention heavy MLPs, allows to be deeply stacked without overfitting vanishing gradients, making it highly scalable.
B
Vision Transformer and Swin Transformer
B.1
Vision Transformer
For plain vision transformer (ViT) with N layers, an image is divided into m fixed-sized patches {4 E g N, S S m). h, w are the height and the width of the image patches. Each patch is then first projected to a d-dimensional embedding with positional encoding:
d = Embed(I,)
ej E
IRd " 1,2, m
(8)
We denote the collection of image patch embeddings E, E fe/ RD E N,1 the (i + 1)-th transformer layer (L++1). The VIT formulated as:
I m), as inputs
E, (E,1))
i == 1,2, 1=2,....,A
(9)
Each layer L, consists of multi-head self attention (MSA) [48] and feed-forward networks (FFN) [4] together with LayerNorm and residual connections [19].
The attention function is computed on the embeddings E, packed together into query matrix Q E, Wo, key matrix K E,Wx, and value matrix V E,Wy, where Wo, WK, W, Wy E Rdxd We compute the matrix of outputs as:
QKT Vd
Attn Q, K,V) = softmax
V
(10)
In addition to MSA sub-layers, each of the layers contains a FFN, which is applied to each position separately and identically. It consists of two linear transformations with ReLU activation in between.
14

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(e).
cis
Patch Mergling
W-MSA SWin
Encoder Layer #
Patch Merging 2
W-MSA 2win-MSA
Encoder Layer +
cls
Patch Merging
W-MSA
Encoder Layer
E
cto
Figure 7: SPP integration. (a). For ViT, embed the prompts between the cls token and the image embeddings. (b). For Swin Transformer, we insert the visual prompts before Window Multi-Head Atttentionn--MSSSS and Shifted Window Multi-Head Self-AttentionnSinn-Mee removing during the patch merging stage.
B.2 Swin Transformer
Swin transformer [35] built by replacing the standard MSA module in a transformer block by module based on shifted windows, with other layers kept the same. Swin transformer computes self-attention within local windows. The windows are arranged to evenly partition the image in non-overlapping manner. The window-based self-attention module lacks connections across windows, which limits modeling power. introduce cross- woos-w connections while maintaining the efficient computation of non-overlapping windows, swin transformer utilizes a shifted window parti- tioning approach that alternates between two partitioning configurations consecutive transformer blocks. The first module uses regular window partitioning strategy that starts from the top-left pixel, and the feature map is evenly partitioned into windows of size W. Then, the next module adopts a windowing configuration that is shifted from that of the preceding layer. by displacing the windows 2 1 |) pixels from the regularly partitioned windows. The shifted window partitioning
by (1 W W approach introduces connections between neighboring non-overlapping windows in the previous layer and is found to be effective in image classification, object detection, and semantic segmentation.
B.3 Sensor Prior Prompt Integration
We adopt set of learnable sensor prior prompts (SPP) P = {pr E R & to convey the sensor prior knowledge from sensor-independent illumination mapping to the backbone. Here, K denotes the number of SPP adopted in the backbone. During the tuning, only the SPPs are being updated, while the backbone kept frozen. Each query p generated by projecting the concatenation of the sensor mapping matrix and the illumination mapping matrix into a few d-dimensional embeddings
p = FFF Laxa])
(11)
They are inserted in the embeddings after the Embed layer (Eq. 8).
For ViT-based backbone, as shown in Fig. 7(a), we integrate the SPPs between the cls token and image embeddings. The process for the 7th layer is formulated as:
Jels, E L, Pi, E,)
(12)
where denotes removing the token at the position corresponding to p in the output of the 5th layer, followed by inserting P++1 before feeding 1,4 into the (i + 1 layer.
For the Swin Transformer-based backbone, we incorporate the SPPs within local windows, excluding them during patch merging, shown in Fig. 7(b).
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B.4 Low-Rank Adaptation
typical transformer-base backbone contains many dense layers that perform matrix multiplication (App. B), and the weights in the layers usually have full rank. To efficiently adapt it to a new task, LoRA constrains the update of the weight matrix Wn E Ra by representing with a low-rank decomposition, i.e.,
WE = W + AW = W + W
(13)
where W E Rddr W. E R and r < d. During training, W,, is frozen and does not receive gradient updates, while W and W contain trainable parameters. Both WA, and AW are multiplied with the same input, and their respective output vectors are summed coordinate-win consider a matrix multiplication h = e in a well-trained backbone, where is an embedding, the adapted forward pass yields:
Therefore,
= WAe + AWe Whe + W,,W,
(14)
W.A. is initialized with Gaussian distribution, and W is initialized with all zeros, leading to A WW 0 at the beginning of the update, and stabilizing LORA.
In Dr. RAW, we solve the training inefficiency by introducing LoRA into the backbone. Specifically, insert low rank matrices into We, WK K, Wy in Eq. 10, and the weights in FFN. For each task, we train a set of compatible low-rank matrices. Therefore, we can explicitly compute W = Wn. + W W for task & during inference. When need to switch to another downstream task 1,, we can replace W.A.A and W with W and W a quick operation with very little memory overhead. Importantly, we do not introduce any additional latency during inference compared to fine-tuned model by construction.
C Datasets
We conducted experiments on object detection, semantic segmentation, instance segmentation, and pose estimation, utilizing combination of various synthetic and weal-world RAW image datasets. For object detection, we adopted 2 open-source real-world datasets, PASCAL RAW [37] and LOD [20]. LOD is a weal-world dataset consisting of 2230 low-light condition RAW images taken by a Canon EOS 5D Mark IV camera with 8 object classes. We took 1800 images as the training set and the other 430 images as the test set. PASCAL RAW is a normal-light condition dataset with 4259 RAW images, taken by Nikon D3200 DSLR camera with 3 object classes. Following [15], two synthesized datasets PASCAL RAW (dark) and PASCAL RAW (over-exp) are additionally adopted to verify the generalization capability of Dr. RAW across various lighting conditions. For the semantic segmentation task, we utilized the widely used sRGB dataset ADE20K [63] to generate the RAW dataset with various lighting conditions, namely ADE20K RAW (dark), ADE20K RAW (normal), and ADE20K RAW (over-exp), similar to [15]. The training and test split of ADE20K RAW the same ADE20K. For the instance segmentation task, we utilized LIS, containing more than two thousand pairs of low/normal-light images, covering various real-world indoor/outdoor low-light scenes. It includes precise instance-level pixel-wise labels, with total of 10504 labeled instances across 8 common object classes: bicycle, car, motorcycle, bus, bottle, chair, dining table, and TV. As for pose estimation, we used ExLPose [30], which collected 2556 images of 251 scenes; 2,065 of 201 scenes are used for training, and the remaining 491 of 50 scenes are kept for testing. We only used the low-light images of ExLPose to make the pose estimation consistent with other tasks. Each annotation contains bounding box and 14 body joints following CrowdPose [32]. An overview of each dataset is presented in Tab. 10.
D Evaluation Metrics
To evaluate the effectiveness of segmentation models, mean Intersection over Union (mIoU) has become one of the standard metries due to its robustness and interpretability. Intersection over Union (IoU) quantifies the overlap between predicted and ground truth regions for a given class. It is formally defined the ratio between the intersection and the union of the predicted and ground truth masks.
Prediction O Ground Truth] IoU-
(15)
Prediction U Ground Truth
16

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Info.
Task
Image Numher
Type
Sensor
Dataset
PASCAL RAW [37]
Object
4259
real-world & synthesis
Nikon D3200 DSLR
(normal/ dark / over-exp)
Detection
LOD [20]
2230
real-worid
Canon EOS 5D Mark TV
ADE20K [15]
Semantic
27574
synthesis
(normal/ dark over-exp)
Sogmentation
Instance
LIS [12]
2230
real-world
Canon EOS Mark IV
Segmentation
Basler 1aA1 160ue (with Sony IMX392 CMOS)
Ex]Pose [30]
Pose Estimation
2556
real-world
Table 10: Overview of the datasets in our experiments.
This formulation penalizes both false positives and false negatives, thus providing a stringent assess- ment of segmentation quality. Unlike pixel accuracy, which may be overly optimistic in imbalanced datasets, IoU offers more reliable measure of the model's spatial prediction fidelity. To evaluate performance across multiple semantic categories, IoU is computed for each class individually and then averaged to produce the mean loU.
C Σ C 1=1
mloU
loU;
(16)
This approach ensures that all classes contribute equally to the final score, thereby mitigating the dominance of frequent classes and enabling fairer evaluation in datasets with long-tail distributions.
On the other hand, mean Average Precision (mAP), another widely adopted metric, captures both the precision-recall trade-off and the localization accuracy of model predictions. Unlike accuracy-based metrics, mAP rewards high precision high recall and penalizes false positives and missed detections, making it a rigorous standard for assessing performance across a range tasks. For object detection, Average Precision (AP) is computed for each class by integrating the precision-recall curve derived from ranked predictions. A detection is considered correct if it has the correct label and its predicted bounding box achieves an IoU with the ground truth box above a certain threshold. Given set of predictions sorted by confidence, the precision and recall values are computed at each rank, and the AP for class is calculated as:
AP. = L. Precision,(x)ddr
(17)
The mean Average Precision (mAP) is then computed as the average over all classes:
MAP 1 Σ AP,
(18)
In instance segmentation, mAP is extended by replacing bounding boxes with pixel-level masks. The IoU is thus calculated between predicted and ground truth masks rather than boxes. Accordingly, two metrics are often reported: mApboz (based on bounding boxes) and mApmask (based on instance masks).
For human pose estimation, mAP is computed using the Object Keypoint Similarity (OKS) metric. which measures the similarity between predicted and ground truth keypoints. Unlike IoU, OKS accounts for keypoint visibility and object scale. It is defined as:
Σ, exp 25 )6 1)
OKS
(19)
Σ, 5, 11
where d; is the Euclidean distance between the predicted and ground truth keypoints, is the object scale, k; is keypoint-specifee constant controlling falloff, and is the visibility flag. Similar to mAP in detection, AP is computed multiple OKS thresholds (e.g., 0.50-0.95), and the final pose mAP is the mean across these thresholds and keypoints.
17

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E Detailed Experimental Results
Owing space constraints, we unable to include the complete of experimental results in the main manuscript. Additional results are provided in the appendix. All experiments were conducted on server equipped with four NVIDIA RTX A6000 GPUs. The software environment includes Python 3.8, PyTorch 1.12, MMDetection 3.3.0, MMSegmentation 1.2.1, and MMPose 1.3.2.
E.1 Object Detection
Tab. 11 presents per-category AP under varying illumination conditions (i.e., normal, over-exposure (over-cxp), and dark), on the PASCAL RAW dataset. Our method, Dr. RAW, consistently achieves the highest AP across all object categories and lighting conditions, demonstrating its robustness to illumination changes. Under normal lighting. Dr. RAWachieves 90.8, 90.3, and 90.1 AP for car, person, and bicycle, respectively, surpassing all competing baselines. Notably, in the challenging dark setting. Dr. RAWoutperforms prior works by large margins, achieving 90.7 (car), 88.6 (person), and 89.7 (bicycle), while the closest rumner-up, RAW-Adapter drops significantly (e.g., only 85.7 for bicycle). While traditional pipelines such as Demosaicing and Karaimer et al. perform reasonably under normal and over-exposed conditions, their accuracy degrades in low light. In contrast, InvISP suffers substantial performance drops in the dark (e.g.. 74.6 for bicycle), indicating brittleness in extreme scenarios. These results underscore the illumination-invarte effectiveness in learning directly from RAW data without relying on handcrafted ISP operations.
capability of Dr. RAWand its
Normal Person
Over-Exp Person
Dark Person
Method
Car
Bicycle
Car
Bicycle
Car
Bicycle
Dr. RAW
90.8
90.3
90.1
90.6
90.1
90.1
90.7
88.6
89.7
Karaimer et al.
90.7
89.9
89.5
90.6
87.3
89.3
89.8
85.9
87.1
Demosaicing
90.7
89.9
89.6
90.7
89.6
90.0
89.7
86.8
87.3
InvISP
90.4
88.6
89.8
90.6
89.5
89.3
83.5
77.5
74.6
Dirty-Pixel
90.6
88.3
90.0
89.9
88.7
89.2
85.5
82.8
82.6
RAW-Adapter
90.3
88.9
89.9
90.6
88.0
89.8
89.3
84.6
85.7
Table 11: Per-category performance (mAP) across different illumination conditions and methods on PASCAL RAW.
Tab. 12 reports per-class AP on the LOD dataset. Dr. RAW achieves the best overall balance and outperforms competing methods in 5 out of 8 categories, including chair (81.5), dining table (52.8), and TV monitor (76.0), demonstrating its strong capability in modeling both structural and fine- grained texture details. While RAW-Adapter yields the highest AP on (91.9), underperforms on other classes such as bottle (42.5) and TV monitor (42.4), indicating limited generalization. ISP- based pipelines (e.g., Default ISP and Karaimer al.) perform reasonably in structured scenes but degrade on visually complex or texture-sensitive categories like motorbike and dining table. Notably, Direct(RAW) achieves strong results bottle (72.6) and bus (67.1), but its performance fluctuates due to the lack of the pre-processing block (Sec. 3.1).
In contrast, Dr. RAW not only delivers SOTA results in terms of average AP (72.7), but does so with remarkable parameter efficiency. Our model updates only 29% of the total parameters, significantly reducing storage overhead without sacrificing accuracy. This lightweight fine-tuning strategy proves especially effective in extracting discriminative features directly from RAW inputs while maintaining generalization across varied object types and scenes. These results affirm that approach successfully bridges the gap between efficiency and performance, setting a new standard for RAW-domain recognition.
E.2 Pose Estimation
Tab. reports the mAP across several low-light testsets (LL-N, LL-H, LL-E, LL-A), comparing Dr. RAW against relevant prior methods. A distinction is maintained between methods employing paired RwW-dark/RAW-nnmms supervision (9) and those restricted to unpaired training solely on
RAW-dark images ), the category encompassing Dr. RAW. We highlight the leading performance
18

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Class Dining Table
Method
Bicycle
Car
Motorbike
Chair
Bottle
TV Monitor
Bus
Dr. RAW
74.5
90.5
71,4
81.5
52.8
65.9
76.0
64.4
Dirty-Pixel
70.9
89.2
68.9
73.4
35.7
52.4
53.8
48.1
RAW-Adapter
70.4
91.9
70.6
77.9
38.0
42.5
42.4
63.4
Default ISP
76.3
90.2
63.4
79.1
41.0
63.6
51.3
59.8
Direct (RAW)
76.5
90.7
64.7
75.8
31.6
72.6
59.3
67.1
Karaimer al.
72.1
89.5
61.5
73.2
28.0
63.7
52.3
59.4
Table 12: Per-class performance (AP) across methods on LOD.
mAP by Testset LL-E
Method
Setting
LL-N
LL-H
LL-A
LCFI
fully-tuned
35.2
20.1
8.3
22.1
LIME+CPNO
fully-tuned
38.3
25.6
12.5
26.6
DANN AdvEn
fully-tuned
34.9
24.9
13.3
25.4
fully-tuned
35.6
23.5
8.8
23.8
Lee et al.
fully-tuned
42.3
34.0
18.6
32.7
freeze
12.0
7.5
3.6
8.3
Direct (RAW)
fully-tuned
36.1
26.9
18.5
29.9
freeze
9.7
6.9
3.6
7.2
Karaimer al.
fully-tuned
35.4
29.2
19.1
30.1
freeze
6.4
3.3
2.0
4.4
InvISP
fully-tuned
30.5
17.7
8.2
19.9
Dr. RAW
adapter
37.0
30.9
19.6
30.4
Trained with paired Hww-dark/RAW-nooee Trained solely on RAW-dark images.
Table 13: Pose estimation performance across different methods trained solely on RAW-dark images.
Best results are bolded and second-best underlined within each training category.
for each metric separately within each training paradigm. Specifically, the best- pest-performing method is indicated in bold, and the second-best is underlined. Operating under the constraint of utilizing only dark RAW images, Dr. RAW consistently establishes new SOTA benchmark. It achieves superior mAP scores compared to all fully-tuned baselines within this category, including Direct (RAW), Karaimer el al., and InvISP, across the evaluated low-light testsets. This uniform outperformance underscores the robustness and efficacy of Dr. RAW in extracting salient pose information directly from RAW sensor data via its adapter-based tuning strategy, irrespective of the specific low-light challenge. Moreover, comparative analysis against methods leveraging paired RAW-dark and RAW- normal supervision ()) reveals the striking competitiveness paired approach (Lee et al.) generally exhibits higher mAP, Dr. RAW substantially narrows the performance differential attributable to the supervision type. It surpasses several established paired- data techniques across all conditions. Critically, the particularly challenging LL-E test set, Dr. RAW's ' performance marginally exceeds that of Lee et al., suggesting exceptional resilience to extremely low-light scenarios that potentially mitigates the necessity for paired supervision in such demanding contexts.
Dr. RAW. While the top popperrforiing
E.3 Instance Segmentation
Tab. 14 summarizes the instance segmentation performance of our proposed method, Dr. RAW, against variety of methods. Our proposed method, Dr. RAW, achieves strong performance while operating the adapter-based setting, striking compelling balance between accuracy and parameter efficiency. A critical distinction lies in the training data utilized. Methods marked with leverage paired RAW-dark and RAW-normal images, providing direct supervision for low-light enhancement or domain translation integrated with the downstream task. In contrast, methods marked with
19

---

Method
Setting
mAP
mAP
mAP76
mApbux
mAPher
EnlightenGANN SGN o Zero-DCE SGN &
fully-tuned
37.1
60.2
37.4
44.5
67.0
48.6
fully-tuned
36.9
60.3
37.4
44.8
67.5
49.0
SID REDI
fully-tuned
37.8
60.0
38.3
44.7
66.6
46.9
fully-tuned
36.0
59.0
35.8
42.8
66.1
45.9
Chen et al.
fully-tuned
42.7
66.2
43.3
50.3
72.6
55.2
freeze
23.3
42.3
23.0
25.8
51.4
22.9
Default ISP
fully-tuned
36.1
58.4
37.6
41.9
67.7
44.1
freeze
27.6
47.4
27.4
30.1
56.2
27.7
Direct (RAW)
fully-tuned
40.2
61.4
41.2
44.9
70.1
48.6
freeze
18.7
34.7
18.8
20.9
43.1
17.4
Karaimer et al.
fully-tuned
34.6
55.1
35.5
39.7
63.9
42,4
Dr. RAW
adapter
41.2
63.0
42.9
43.6
70.3
48.1
model trained on the RAW-dark and normall image pairs.
The model is trained solely on the RAW-dark images.
Table 14: Instance segmentation performance across different methods trained solely on RAW-dark images. Best results are holded and second-best underlined within each training category.
including our Dr. RAW, are trained solely on RAW-dark images, representing a more challenging scenario where explicit normal-light guidance is absent during training. For fairness, we highlight best and second-best results in each training category separately: methods trained with paired RAW-normal supervision (9) and those trained solely on RAW-dark data (*). Within the category trained only on RAW-dark images, Dr. RAW demonstrates compelling performance. Specifically, Dr. RAW outperforms the strongest baseline in this category, Direct (RAW) fully-tuned, by 1% in mAP, 1.6% mAP50. and 1.7% in mAP75- The substantial gain in mAP73. which demands higher localization accuracy, highlights the quality of the instance masks predicted by method even under challenging low-light conditions using only dark RAW input. While the bounding box mAP (mAppoo of 43.6 is slightly below the fully-tuned Direct (RAW) method, metrics indicate superior segmentation accuracy. It is noteworthy that Dr. RAW achieves this SOTA performance within the unpaired setting using efficient adapter-bass tuning strategy, rather than requiring full end-to-end fine-tuning like the Direct and Karaimer el al. baselines. Compared methods trained with paired data (9), Dr. RAW is remarkably competitive. While Chen al., benefiting from paired supervision, achieves the highest overall score (42.7 mAP), Dr. RAW (41.2 mAP) significantly narrows the performance gap. It notably outperforms several paired-data methods like EnlightenGAN+SON
mask mAP
(36.9 mAP). SID (37.8 mAP), and REDI (36.0 mAP). This underscores the efficacy of Dr. RAW for robust instance segmentation directly from dark RAW images, achieving results comparable to methods requiring significantly more supervision the form of paired -ight images.
(37.1 mAP), Zero-DCE+3
Tab. 15 reports per-class performance on LIS for both object detection (APhox) and instance seg- mentation (Apmask) across the methods trained solely on RAW-dark images. Dr. RAW consistently outperforms prior approaches across nearly all categories, achieving the best Apbox in 6 out of classes and the best Apmask in out of 8 classes. These include significant gains in complex object categories such as motorbike (45.6 box / 50.8 mask), bottle (63.4/ 67.3), and chair (71.7 / 73.3). Compared Karaimer al. and the Default ISP pipeline. Dr. RAW demonstrates superior adaptabil- ity under real-world RAW distributions. While Direct(RAW) benefits from bypassing ISP artifacts, it lacks robustness in categories like TV monitor car, where subtle color and texture cues are essential. These gains can be attributed to two key components in our architecture. First, the pre- vrepprccessing block allows the model to normalize global color and exposure shifts across devices and scenes, improving resilience under diverse lighting. In addition, it enables high-fidelin feature extraction early in the pipeline. Second, the parameter-efficient strategies effectively task-specific information without altering the domain-genert general knowledge in the backbone. Together, these modules bridge the gap between low-level RAW signals and high-level recognition tasks, resulting in strong performance on both detection and segmentation tasks.
20

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Apbox
Method
Bicycle
Chair
Dining Table
Bottle
Motorbike
Car
TV Monitor
Bus
Karaimer al.
34.3
66.0
30.4
59.3
37.3
27.9
19.6
42.8
Direct (RAW)
41.2
71.6
38.8
62.8
44.5
32.3
21.8
46.5
Default ISP
38.0
67.3
34,9
57.8
39.7
30.4
22.1
45.2
Dr. RAW
39.7
71.7
39.2
63.4
45.6
32.0
24.8
47.0
A.mmsk Motorbike
Method
Bicycle
Chair
Dining Table
Bottle
Car
TV Monitor
Bus
Karaimer el al.
21.0
66.5
24.2
62.1
38.7
12.3
4.6
47.2
Direct (RAW)
26.1
72.7
33.1
65.9
47.6
17.5
6.3
52.6
Default ISP
22.7
67.3
29.5
62.2
38.7
13.4
5.1
50.0
RAW
25.8
73.3
34.2
67.3
18.6
53.7
Table 15: Per-class Apbox and A across methods on LIS.
E.4
Future Direction
For future research directions, believe it is feasible train foundation model based on RAW images that supports multi-task learning without the need for adaptation in each specific task. For example, could build multiple decoders on shared backbone address various RAW-based computer vision tasks. This approach is of crucial importance to real-world systems and downstream tasks such autonomous driving and wildlife monitoring.
21

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Reviews:
[{"review_id": "C5yI7fSsqq", "reviewer": "Reviewer_p2T5", "summary": "This paper introduces Dr. RAW, a unified and parameter-efficient framework for performing high-level computer vision tasks directly on RAW image data. The work addresses two key challenges in RAW-based vision: data inconsistency arising from different camera sensors and lighting conditions, and tuning inefficiency caused by the need to fully fine-tune entire models (both ISP and backbone) for each specific task.", "questions": "See weaknesses", "limitations": "See weaknesses", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\n- Comprehensive Evaluation: The experimental validation is extensive and a major strength. The authors evaluate their method on four different and representative high-level tasks, using a total of nine datasets that cover various lighting conditions (normal, dark, over-exposed). This thoroughness strongly supports the claims of generality and robustness.\n- State-of-the-Art Performance with High Efficiency: The core contribution—achieving superior or competitive performance while tuning only a tiny fraction of parameters—is highly compelling. The results, effectively summarized in the radar chart (Fig. 1) and detailed in Tables 1-5, show that Dr. RAW consistently outperforms prior methods. \nWeaknesses \n- Clarity on the Sensor Prior Prompt (SPP) Mechanism: The SPP module is a central piece of the proposed adaptation strategy, yet its mechanism could be explained more intuitively. The paper states that it projects the concatenation of the sensor and illumination matrices into embeddings (Eq. 3), but the intuition behind how this effectively injects \"sensor-aware conditioning\" could be elaborated. A small diagram illustrating how these low-dimensional prompts interact with image patch embeddings within the attention mechanism would be highly beneficial for the reader's understanding.\n- Dependency on Large-Scale RAW Pre-training: The best results are achieved with a Swin Transformer pre-trained on the ADE20K RAW dataset. This is a crucial detail and a potential practical limitation. The paper would be stronger if it discussed the implications of this dependency. For example: How does the method perform if such a large-scale RAW dataset is not available? How much of the performance gain is attributable to the RAW pre-training versus the proposed adaptation modules? While Table 7 compares to an ImageNet-1k pre-trained backbone, a more explicit discussion of this limitation and potential future work to mitigate it would be valuable.\n- More Granular Ablation of the Pre-processing Block: The pre-processing stage consists of three components: a sensor mapping block, an illumination mapping block, and a U-Net-like re-mosaicing block. The current ablation study (Table 6) treats this entire stage as a single unit. A more fine-grained ablation study that dissects the individual contribution of each of these three sub-components would provide deeper insights into which parts are most critical for handling data inconsistency. This would further strengthen the design choices presented.\n- Positioning of Novelty: The core adaptation techniques, LoRA and prompt tuning, are borrowed from the existing PEFT literature. The paper does a good job of citing these works. However, it could be slightly more explicit in the introduction or related work that its primary novelty lies not in inventing these PEFT techniques, but in the novel synergistic application and integration of these methods within a RAW-specific pipeline. This would help to precisely position the paper's contribution in the context of both RAW processing and efficient model adaptation.", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank the reviewer for the time and effort in providing a thorough and constructive review of our manuscript. The feedback has been invaluable in helping us identify areas for improvement, and we have addressed each point in detail below.\n\n**Weakness 1**: We appreciate the reviewer’s feedback on the clarity of the Sensor Prior Prompt (SPP) mechanism. We agree that a more intuitive explanation of how SPP injects \"sensor-aware conditioning\" benefits the paper, and we will include a more detailed figure in the final manuscript.\nThe idea behind SPP is partly inspired by VPT [1], and we are the first to apply this concept to RAW-based vision tasks. The goal of SPP is to adapt a large, pre-trained backbone to new tasks or data distributions with minimal changes to the model itself. Instead of fine-tuning all the weights, which is computationally expensive and requires storing a separate model for each task, SPP introduces a small set of learnable parameters called \"prompts\" that are prepended to the input data. The backbone remains frozen, and only these prompts and a task-specific head are trained.\n\nWe use the sensor and illumination matrices ($M$ and $L$) as a compact \"fingerprint\" of the capture conditions. This low-level fingerprint is translated into a high-level \"hint\" to guide the backbone, enabling it to adapt its processing in a sensor & lightness-aware manner.\n\nThe process involves two key steps: **Projection** and **Interaction**.\n1. Learnable Projection: As noted in Eq.3, we concatenate the embeddings projected from $M$ and $L$ using an FFN. The FFN acts as a learnable, non-linear mapping. It learns the optimal way to translate the matrix values into the high-dimensional embedding space that the transformer operates in. The FFN's weights are updated during training, and the model learns to create prompts that are maximally useful for the downstream task. It isn't a fixed projection; it's an optimized one that learns to emphasize the most important sensor characteristics for perception.\n\n2. Interaction within Self-Attention: Once generated, the prompts are injected into the backbone by prepending them to the sequence of image patch embeddings. (Fig. 7 in Appendix B.3 illustrates the interactions within self-attention). This is where the \"conditioning\" happens. The self-attention computes relationships between all tokens in its input sequence. By adding our prompts, we enable three critical interactions, as described in and derived from Eq. 5:\n    1. Prompts attend to Image Patches: The prompts can gather context from the entire image, allowing the model to understand how the global sensor properties relate to specific image content.\n    2. Image Patches attend to Prompts: Each image patch can \"query\" the sensor-specific information held in the prompts. This allows local features to be interpreted differently based on the global sensor fingerprint. For example, the model might learn to process a noisy blue channel in a patch differently if the prompts indicate it came from a sensor known to have such characteristics.\nPrompts attend to Prompts: The prompts interact with each other to form a coherent, internal representation of the sensor's properties before they influence the image patches.\n\n**Weakness 2**: Thanks for the advice, we agree that the dependency on large-scale RAW pre-training is an important practical consideration. Our Dr. RAW is robust enough to perform well even with smaller RAW pre-training datasets, and our adaptation modules provide significant gains on their own. We acknowledge that the availability of large-scale RAW datasets is a practical consideration, and we've conducted a new ablation study to directly address your questions. We experimented with the scenario where large-scale synthetic RAW data like ADE20K-RAW is not available. We pre-trained a new backbone using only the much smaller PASCAL-RAW dataset (4259 images). We then evaluated this backbone on the LOD and LIS tasks. The results are as follows:\n\n| Downstream Task Dataset | Backbone Pre-training Dataset             | mAP  |\n|-------------------------|-------------------------------------------|------|\n| LOD                     | ADE20K (RAW, 20210 images)                | 72.1 |\n| LOD                     | PASCAL-RAW (RAW, 4259 images)             | 69.3 |\n| LOD                     | in1K (RGB, 1281167 images)                | 67.8 |\n| LIS                     | ADE20K (RAW, 20210 images)                | 41.2 |\n| LIS                     | PASCAL-RAW (RAW, 4259 images)             | 37.8 |\n| LIS                     | in1K (RGB, 1281167 images)                | 35.9 |\n\nThis new study shows that while large-scale pre-training yields the best results, our method still achieves strong performance when pre-trained on a smaller, more accessible RAW dataset.\nAttributing Performance Gains: The performance gains come from both the RAW pre-training and our adaptation modules.\n1. Gain from RAW Pre-training: The new results show that pre-training on even a small RAW dataset (PASCAL-RAW) boosts performance to 69.3 mAP, better than using a standard ImageNet-1k (RGB) pre-trained backbone (67.8 mAP) shown in Table 7. Using a large-scale RAW dataset (ADE20K RAW) provides an additional lift to 72.1 mAP.\n2. Gain from Adaptation Modules: As shown in Table 6, our adaptation modules alone provide a substantial lift. Our method achieves 72.1 mAP on LOD, which is a massive improvement over a simple frozen baseline (43.6 mAP). This shows our modules are highly effective at bridging the domain gap.\n3. Future Work: We sincerely appreciate the advice and agree that this is a limitation and will add a discussion to the paper. A promising direction for future work is to explore self-supervised pre-training on large, unlabeled RAW image collections. This could further enhance performance and reduce the dependency on large-scale labeled RAW datasets.\n\n**Weakness 3**: \nThank you for pointing this out. We agree that a more granular ablation study provides more insights. We conducted new experiments, and the results are shown in the table:\n\n| Re-mosaicing | Sensor Mapping Block | Illumination Mapping Block | LOD  | PASCAL RAW (normal) | PASCAL RAW (over-exp) | PASCAL RAW (dark) |\n|--------------|----------------------|----------------------------|------|----------------------|------------------------|--------------------|\n|              |                      |                            | 43.6 | 81.4                 | 86.0                   | 59.9               |\n|              |                     |        √                    | 50.8 (+7.2) | 84.1  (+2.7)               | 87.5  (+1.5)                 | 66.6  (+6.7)             |\n|              |           √           |                            | 48.8 (+5.2) | 83.8  (+2.4)               | 87.7  (+1.7)                 | 68.3   (+8.4)            |\n|              | √                    |     √                       | 55.4 (+11.8) | 86.2  (+4.8)               | 88.1  (+2.1)                 | 74.7  (+14.8)             |\n|      √        |                     |                           | 53.2 (+9.6) | 84.9 (+3.5)                | 87.1  (+1.1)                 | 71.9   (+12.0)            |\n| √            | √                    | √                          | 57.9 (+14.3) | 88.6  (+7.2)               | 89.5  (+3.5)                 | 81.1  (+21.2)             |\n\nThis detailed analysis offers several key insights: Both the Sensor Mapping and Illumination Mapping blocks individually provide a significant performance improvement over the baseline. And their combined effect is synergistic, yielding a larger gain than either block alone, which validates the design of our mapping stage. The re-mosaicing block also offers an individual contribution, confirming its importance. The best performance is achieved when all three components are used together, demonstrating that each part of our pre-processing pipeline is essential for the final SOTA results.\n\n**Weakness 4**: Thank you for this constructive feedback. We agree that a more explicit framing of our contribution will strengthen the paper.\nWe will clarify our specific contributions in the revised manuscript as follows:\n1. A novel application of PEFT to the RAW Domain: To our knowledge, this is the first work to systematically apply and tailor modern PEFT techniques to address the challenges inherent in RAW processing, such as sensor and illumination variance. While prior RAW-based methods often rely on fully tuning dedicated ISP modules and the entire network backbone, we demonstrate a new, more efficient path. We show that a frozen, pre-trained backbone can be successfully adapted, which represents a significant methodological shift in this domain.\n2. The design of SPP: Our SPP module is a novel, domain-specific adaptation of VPT. Unlike general-purpose prompts, which are typically independent learnable parameters, SPP is uniquely designed to translate the physical properties of the camera into conditional \"hints\" for the model. This targeted injection of sensor-aware conditioning into a frozen backbone is a key novel component of our framework.\n3. Synergistic integration for SOTA Performance: We demonstrate that the synergy between our lightweight RAW pre-processing, the global conditioning from SPP, and the targeted, task-specific updates from LoRA leads to SOTA performance with remarkable efficiency. The SPP steers the model based on sensor characteristics, while LoRA fine-tunes key operations for the specific downstream task. This combined strategy allows us to outperform fully-tuned models while updating only a fraction of the parameters.\n\n\n\n\n[1]. Jia, Menglin, et al. \"Visual prompt tuning.\" European conference on computer vision. Cham: Springer Nature Switzerland, 2022."}, {"review_id": "xJQkUAIM9o", "reviewer": "Reviewer_uAEX", "summary": "This paper proposes Dr. RAW, a modular framework for performing high-level visual tasks (e.g., detection, segmentation, pose estimation) directly on RAW sensor data. Unlike prior approaches that rely on neural ISPs or per-task fine-tuning, Dr. RAW introduces a lightweight preprocessing block (comprising sensor and illumination mapping and re-mosaicing), followed by a frozen vision backbone enhanced with SPP and LoRA-based adapters. These modules together normalize domain shifts across sensors and environments and enable efficient per-task adaptation with minimal parameter updates. In experiments, the paper demonstrated both robustness and efficiency on various tasks/datsets.", "questions": "I would appreciate if authors could address some unclear points:\n\n- The paper introduces a re-mosaicing block with simple gating as a key component of the preprocessing pipeline. However, no ablation is provided to justify the necessity of re-mosaicing or gating. Could the authors include comparisons with simpler alternatives, such as direct demosaicing using fixed Bayer patterns, to isolate the contribution of this component?\n\n- One of the main claims is that the preprocessing block unifies sensor and illumination variation. Currently, this is supported mainly by qualitative visualizations (e.g., PCA of chromaticity). Could the authors provide quantitative measures (e.g., feature distribution divergence, domain classification accuracy) to substantiate this claim?\n\n- The paper implies that the preprocessing block is shared across tasks and does not need to be retrained for each new task. However, this block depends on scene illumination and sensor response. Could the authors clarify under what conditions the parameters remain reusable, and whether domain-specific fine-tuning is ever required?\n\n- The sensor prompt is generated from a pair of small matrices representing sensor and illuminant characteristics. This hand-crafted formulation seems restrictive. Have the authors explored learned or higher-dimensional embeddings for prompts? Is there a trade-off in interpretability or generalization?\n\n- While the model is tested on multiple datasets, it is unclear how well it generalizes to sensors or conditions not seen during training. Have the authors tested cross-domain adaptation (e.g., training on some sensors and evaluating on held-out ones)? If not, would they expect the system to remain robust under such scenarios?", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 4, "originality": 3, "strengths_and_weaknesses": "Strengths:\nI basically found this paper quite insightful because, \n- The paper addresses a practical problem how to use RAW sensor data directly for high-level tasks without relying on task-specific neural ISPs or full-model fine-tuning. By introducing a lightweight preprocessing module and efficient adaptation mechanisms (LoRA and prompt-based tuning), the proposed framework significantly reduces training and deployment costs.\n- One of the key strengths lies in the ability to handle multiple vision tasks—detection, segmentation, and pose estimation—within a single, unified framework. This versatility is achieved without retraining the entire backbone, which is an important contribution toward general-purpose visual perception from RAW inputs.\n- The experimental validation is thorough and well-structured. It spans several real and synthetic datasets with diverse sensor characteristics and lighting conditions. Results show strong performance and consistent improvements across tasks and exposure scenarios.\n- The system is modular and well-motivated, making it practical for integration into real-world applications. The separation between sensor-specific preprocessing and task-specific adaptation is conceptually clean and promotes reusability.\n- The paper follows good practices in architectural transparency and experimental design. The ablation studies are useful, and the authors are clear about the efficiency gains in terms of trainable parameters.\n\nWeakness:\nI have some minor concerns with regard to following points.\n- The technical novelty of the paper is somewhat limited. While the integration of existing ideas (e.g., LoRA, prompt tuning, lightweight U-Nets) into the RAW domain is thoughtful and effective, the algorithmic innovation is not clearly highlighted.\n- While the paper claims that the preprocessing module (sensor + illumination mapping and re-mosaicing) enables consistent representations across sensors, this claim is mostly supported by qualitative visualizations (e.g., PCA plots) rather than quantitative alignment metrics. A clearer evaluation of distribution alignment across sensor domains would strengthen the argument.\n-  re-mosaicing block and its use of simple gating are introduced as key components, but there is no ablation or comparison against more conventional alternatives (e.g., using fixed Bayer demosaicing). This makes it difficult to assess whether these design choices are essential.\n- Although the framework is task-agnostic in principle, each task still requires some adaptation via LoRA and SPP modules. The extent to which these components generalize across tasks or unseen sensor types is not deeply explored. In this sense the novelty in the paper sounds slightly overclaimed.", "was_revised": true, "final_justification": "I have read the authors' feedback and the discussion between the authors and other reviewers thoroughly, and I have found that most of my concerns have been resolved. Therefore, I recommend the acceptance of this paper.", "rebuttal": "We are very grateful for the insightful comments and valuable suggestions. The reviewer's feedback has provided us with a clear roadmap for strengthening the paper's clarity and impact. \n\n**Weakness 1**: We agree that the core novelty of Dr. RAW lies not in the invention of the individual PEFT techniques, but in their novel, synergistic integration to create a new, efficient, and high-performing paradigm for RAW-based vision tasks. As reviewer p2T5 astutely noted, our contribution is a complete, RAW-specific pipeline that thoughtfully combines these components to solve problems unique to this domain.\nOur specific novel contributions, which we will highlight more explicitly in the revised manuscript, are:\n1. A New Paradigm for the RAW Domain: Dr. RAW challenges the standard approach in RAW-based vision, which typically requires full fine-tuning of both the network and complex, hand-crafted, or learnable ISP pipelines. Our work is the first to systematically apply and tailor modern PEFT techniques to address the challenges inherent in RAW processing, such as sensor and illumination variance. We demonstrate that a frozen, general-purpose backbone can achieve SOTA results on diverse RAW datasets by using a combination of lightweight pre-processing and parameter-efficient adaptation, a significant methodological contribution.\n2. Domain-Specific Prompt Engineering (SPP): Our Sensor Prior Prompts (SPP) are a novel, domain-specific application of prompt tuning. Unlike generic visual prompts, SPP is uniquely designed to translate physical camera properties (the sensor and illumination matrices) into learnable embeddings that condition the model. This targeted injection of sensor-aware conditioning into a frozen backbone is a key innovation for handling data inconsistency in RAW files.\n3. Synergistic integration for SOTA Performance: We demonstrate that the synergy between our lightweight RAW pre-processing, the global conditioning from SPP, and the targeted, task-specific updates from PEFT leads to SOTA performance with remarkable efficiency. The SPP steers the model based on sensor characteristics, while LoRA fine-tunes key operations for the specific downstream task. This combined strategy allows us to outperform fully-tuned models while updating only a very small fraction of the parameters.\n\nIt is this specific, multi-stage integration that allows Dr. RAW to outperform fully-tuned methods while updating as little as 2% of the backbone's parameters.\nFollowing this valuable feedback, we will revise the Introduction and Related Work sections to more explicitly frame our contribution in this context, ensuring the novelty of our integrated framework is clear.\n\n**Weakness 2**: We thank the reviewer for their valuable feedback and the suggestion to strengthen the quantitative evidence for our pre-processing module's effectiveness.\nWe would like to respectfully clarify that, in addition to the qualitative PCA visualizations, we include a quantitative analysis of the distribution alignment in Section 4.7, \"Impact of Pre-processing Block.\" We recognize this section may not have been prominent enough, and we will ensure it is highlighted more clearly in the revised manuscript.\n\nTo directly address the reviewer's point, our claim of achieving more consistent representations is supported by the following quantitative metric presented in the paper: We quantitatively measure the compactness of the chromaticity distribution before and after our pre-processing module, measured via reduced intra-class distance. The average intra-class distance decreases from 0.380 for the original RAW images to 0.259 for the mapped images. This 32% reduction is a direct, quantitative measure of improved distribution alignment, demonstrating that our module brings representations from varied sensor and lighting conditions into a tighter, more consistent cluster.\n\nThis quantitative reduction in sample variability is not merely a statistical observation. As shown in our ablation study (Table 6), introducing the pre-processing block alone yields substantial gains, boosting object detection performance on LOD from 43.6 to 57.9 mAP. This demonstrates that the quantitatively measured improvement in distribution alignment is crucial for enabling more stable and effective learning.\nIn the final version of the paper, we will create a dedicated table to present these quantitative metrics alongside the PCA plot to make the evidence for our claim more explicit and unmistakable. Thank you for helping us improve the clarity of our paper.\n\n**Weakness 3**: We thank the reviewer for this excellent point. While not explicitly labeled as an ablation, the comparisons against the \"Default ISP\" and \"Demosaicing\" baselines in our experimental tables (e.g., Table 2) are intended to serve this exact purpose.\n1. Conventional Alternatives: The \"Default ISP\" and \"Demosaicing\" methods represent the conventional approach. They use a standard, fixed demosaicing algorithm as the primary pre-processing step before feeding the data to a fully-tuned network.\n2. Our Learnable Approach: Dr. RAW replaces this with a more sophisticated, learnable pre-processing stage, where the re-mosaicing block is a key component for refining the image and correcting artifacts.\n\nThe performance gap across all conditions validates the effectiveness of our design. For example, on PASCAL-RAW dataset, our method achieves 90.4 mAP (normal) and 89.7 mAP (dark), substantially outperforming “Default ISP” (86.7 mAP on normal light) and \"Demosaicing\" baseline (88.1 mAP on normal light, 86.5 mAP on dark light). This demonstrates that our learnable re-mosaicing block is an essential component.\n\n\n**Weakness 4**: Thank you for this comment, which allows us to clarify the nature of our contribution. We agree that task-specific adaptation is required. In fact, enabling this adaptation in a very efficient manner is our core novelty. The \"task-agnostic\" aspect of our framework refers to the frozen backbone and the pre-processing architecture. Unlike prior methods that require a fully fine-tuned, task-specific network for each application, our approach utilizes a single, unified backbone for all tasks. The lightweight modules, LoRA and SPP, are not a limitation of this approach but rather the highly efficient mechanism that makes it possible, steering the powerful, general backbone for a specific task. \n\nTo directly evaluate the generalization to unseen sensors, we conducted a new zero-shot experiment. We took our model trained on the PASCAL RAW dataset (captured with a Nikon DSLR camera) and tested it without any fine-tuning, on RAW images of the same scenes captured with two unseen sensors, iPhone X and Samsung (we adopt methods [1] to do RAW-to-RAW mapping). The model achieves 88.3 mAP and 88.8 mAP, respectively. Compare with original performance 90.4 on Nikon, the results show only a marginal performance drop when tested on unseen sensors, demonstrating Dr.RAW's generalization. \n\n**Question 1**: Please refer to our response to **Weakness 3**.\n\n**Question 2**: We kindly refer the reviewer to our response to the above **Weakness 2**.\n\n**Question 3**: We thank the reviewer for this excellent question. The reviewer is correct that the pre-processing block's function is dependent on domain-specific characteristics like sensor response and illumination (though the zero-shot performance on unseen sensors is still outstanding, as shown in our response to **Weakness 4**). For this reason, the pre-processing block is not a fixed, universally shared component. To directly answer your questions: The pre-processing parameters are reusable for a specific trained task, but are fine-tuned for each new task. Domain-specific fine-tuning of this block is always part of our adaptation strategy for a new task. We will revise the manuscript to clarify which components are frozen versus which are fine-tuned per task.\n\n**Question 4**: Thank you for your feedback. This is an excellent question regarding why we chose this principled formulation over purely learned prompts.\nOur design is a deliberate trade-off aimed at maximizing generalization and efficiency while retaining a degree of interpretability. By grounding the prompts in the physical properties of the camera, we provide the model with a strong, structured prior. This helps the model generalize more effectively to new sensors and lighting conditions, as it doesn't need to learn these complex physical relationships from scratch. A purely random, learnable prompt would lack this physical grounding.\n\nTo quantitatively validate this design, we conducted a new ablation study comparing our SPP against a baseline using an equivalent number of purely random, learnable embeddings (as in standard VPT). As shown in the table below, our SPP consistently outperforms the purely learned prompts, confirming the benefit of our physically-grounded approach.\n| Method                   | Dataset             | mAP  |\n|--------------------------|----------------------|------|\n| Learned Prompts (Baseline) | LOD                | 70.6 |\n| SPP (Ours)               | LOD                  | 72.1 |\n| Learned Prompts (Baseline) | PASCAL RAW (dark) | 88.3 |\n| SPP (Ours)               | PASCAL RAW (dark)    | 89.7 |\n\nWe will add this discussion and the quantitative results to the final manuscript to make our design choices and their empirical validation perfectly clear. Thank you for helping us strengthen the paper.\n\n**Question 5**: We respectfully refer the reviewer to our response to **Weakness 4** above.\n\n[1]. Generalizing ISP Model by Unsupervised Raw-to-raw Mapping ACM MM 2024"}, {"review_id": "YDup9PEYGu", "reviewer": "Reviewer_cGmR", "summary": "This paper proposes an image processing method to improve the performance of computer vision tasks using RAW images. The approach consists of a preprocessing module with three main blocks and an adapter system. The adapter is a SwinT-based backbone for image processing and takes as input both the preprocessed image and prompts derived from parameters obtained from the preprocessing module. This backbone employs Low-Rank Adaptation (LoRA) techniques tailored to each specific CV task. The method enables learning task-adapted input images with a minimal number of trainable parameters, making the adapter efficient to train for various computer vision applications.", "questions": "1. Does the RAW-RGB used as input refer to demosaiced images (where a 1-channel Bayer pattern image has been reconstructed into 3 channels)? \n\n2. Is there potential role overlap between the re-mosaicing block, sensor mapping block, and illumination mapping block?\n\n3. What are the benefits of updating fewer parameters for each task?\n\n4. While the paper evaluates datasets for application scenarios requiring real-time processing, are the processing times actually practical for real-world use?", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 1, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths:  \n- The paper demonstrates high accuracy across multiple computer vision tasks on RAW images.  \n- The proposal of an integrated framework that directly applies RAW images to multiple CV tasks represents a novel approach.  \n- It shows the potential for task adaptation with a minimal number of trainable parameters.  \n- The authors have designed modules to address specific challenges that arise when working with RAW images.  \n- The approach of combining sensor characteristics with task-specific adaptations is interesting.  \n\nWeaknesses:  \nClarity:  \n\n1. Several important technical explanations are missing:  \n   - Please add architectural description of the re-mosaicing block\n   - Unclear usage of the SG block (only referenced in Fig. 3(b) without explanation)\n   - Please add explanation of FFN in Section 3\n\n2. Explanation of figures:  \n   - Fig. 2(d) lacks clear indication of what the graph represents\n   - Reference to Fig. 3(b) alone is inadequate for understanding the structure\n\n3. Please add explanation regarding the training process compromises reproducibility:  \n   - Please add description of the training process or optimizer\n   - Unclear which dataset was used to train W_h and details of LoRA training\n\nQuality:  \n\n4. Validation of the effectiveness of pre-processing module:  \n   - Ablation of the re-mosaicing block and mapping blocks is required to evaluate the design\n\n5. Missing comparisons:   \n   - The comparison of processing speed or memory efficiency with previous methods is required for application.\n   - The comparison of parameter counts in the preprocessing portion with prior work is required for application.\n\nSignificance:  \n\n6. Practical impact:  \n   - Task dependency in RAW image processing still requires additional learning\n   - Insufficient discussion of processing speed and memory efficiency\n\nMinor Comments:  \n(1. Introduction) Unclear description  \nThe statement \"Otherwise, the cross-task performance tends to cause catastrophic degradation in existing RAW-based high-level frameworks (Fig. 2(d)).\" is unclear about what this \"degradation\" refers to.\nIt seems the authors are likely pointing out that adapters tuned for each task may be inappropriate for other tasks (causing degradation).\n\n(Related works 2.2) Unclear description  \nThe explanation of LoRA is overly simplified. Please clarify what is being low-rank decomposed from the learned weights and how it is being injected into the model.", "was_revised": true, "final_justification": "The rebuttal explanations address my main concerns, and the authors have agreed to implement the requested revisions. \nI have therefore updated my recommendation accordingly.", "rebuttal": "We would like to thank the reviewer for the thoughtful and detailed feedback on our work. We have carefully considered all the comments and provide our point-by-point responses below.\n\n**Weakness 1**: We thank the reviewer for the suggestion and would like to clarify that a detailed breakdown of this component is provided in Appendix A of the submitted manuscript.\nWe summarize the key architectural details from the appendix for your convenience:\n1. Overall Structure: The re-mosaicing block is designed as a symmetric, U-shaped network that balances computational efficiency with representational power. \n2. Simple Gating (SG): SG is a lightweight and parameter-free nonlinear activation. It splits the input feature map along the channel dimension into two halves and computes their element-wise product without the overhead of conventional activation functions. This design is inspired by findings that color channels contribute unequally across tasks and lighting. It reduces memory costs, making it well-suited for our pipeline.\n3. PixelShuffle: In the decoding path, the block uses PixelShuffle for upsampling. It avoids checkerboard artifacts and better preserves fine spatial details.\n\nWe will ensure that when the re-mosaicing block is introduced in Section 3.1, we more explicitly point the reader to  Appendix A.\n\nWe will also add the following description to Section 3 to clarify the role and architecture of FFN:\n\nA transformer layer is composed of two main sub-layers: a Self-Attention (SA) and a Feed-Forward Network (FFN). While the SA is responsible for aggregating information across tokens, the FFN provides a non-linear transformation that is applied to each token's feature independently.\nThe FFN architecture is typically composed of two linear layers with a non-linear activation function in between. This design allows the model to process complex features and learn more expressive representations beyond the token-mixing performed by the SA.\n\n**Weakness 2**: We agree that a more explicit explanation, in addition to those in lines 67 - 69, is needed. \nFig.2(d) is designed to demonstrate the key advantage of Dr. RAW: achieving superior performance while being highly parameter-efficient. It compares Dr.RAW against the previous SOTA, RAW-Adapter, under two different training settings. \n1. The Y-axis represents object detection performance, measured in mAP on LOD.\n2. Each bar shows two experimental conditions:\n    1. \"Tuning Backbone\" represents the standard full fine-tuning, where the entire backbone is trained.\n    2. \"Freeze Backbone\" represents the backbone is frozen, and only lightweight adapters (~2% of total parameters) are trained (indicated by the pie chart).\n3. The red number highlights the performance drop when switching from a fully tuned backbone to a frozen one.\n\nRAW-Adapter suffers a performance drop of 40.0 mAP when its backbone is frozen. In contrast, Dr.RAW only has a marginal drop of 3.1 mAP, while still achieving a new SOTA. \n\nThe descriptive text for Fig.3(b) is already included in lines 142-150, and we will ensure it's positioned more clearly. It is a visualization of our re-mosaicing block. Please refer to our response to **Weakness 1** for details.\n\n**Weakness 3**: We've included the training details in Section 4.1 and will add the following specifics to the manuscript to ensure clarity.\n1. Training Process and Optimizer: We used common data augmentation strategies, including random crop, random flip, and multi-scale testing. The optimizer we use is AdamW with a learning rate of 0.0001, betas of (0.9, 0.999), and weight decay of 0.05. We will add a detailed table about the hyperparameters.\n2. Backbone Pre-training and LoRA Details: Please refer to our response to **Minor 2**.\n\n**Weakness 4**: We kindly refer the reviewer to our response to **Weakness 3** for reviewer **p2T5**.\n\n**Weakness 5**: We've benchmarked our Dr. RAW against the previous SOTA, RAW-Adapter, to evaluate its inference speed and memory usage.\n\n| Method         | Inference Speed | Peak GPU Memory |\n|----------------|------------------|------------------|\n| RAW-Adapter    | 17.5 FPS         | 286 MB           |\n| Dr. RAW (Ours) | 15.9 FPS         | 298 MB           |\n\nDr. RAW achieves a comparable inference speed and memory usage to the previous SOTA. The modest trade-off in inference speed is accompanied by significantly superior performance and generalization. The primary efficiency gain of Dr. RAW lies in its parameter and storage efficiency. We obtained these SOTA results while fine-tuning only a tiny fraction of the parameters, whereas other methods require tuning 100% of the parameters. This leads to a dramatic reduction in storage costs for deployment. For example, adapting to 5 different tasks would require storing 5 full models for RAW-Adapter, but only 1 shared backbone and 5 tiny adapters for Dr. RAW. This makes our approach far more scalable.\n\nOur pre-processing stage contains 1.14M parameters. The pre-processing module of RAW-Adapter contains 0.58M parameters. While our pre-processing module is larger, it's crucial to understand the different roles these components play in their respective frameworks.\nThe module in RAW-Adapter is designed to work in conjunction with a fully fine-tuned backbone. The heavy lifting of adaptation is performed by tuning the entire ~37M parameters. In contrast, ours is designed to create a highly normalized representation that a frozen backbone can effectively use. This leads to enormous overall savings in the total number of tunable parameters required for adaptation. \n\n**Weakness 6**: While Dr. RAW still requires task-specific training to achieve optimal performance, its primary advantage lies in its remarkable efficiency. \n1. Drastically Reduced Storage: Only lightweight adapters are stored per task.\n2. Greater Computational Efficiency: The training process requires less GPU memory because fewer gradients need to be computed and stored.\n3. Faster Adaptation: The model can be adapted to new tasks with significantly less overhead.\n\n**Minor 1**: Existing methods are designed with the assumption that the entire backbone will be fully fine-tuned for a given task. The \"catastrophic degradation\" refers to the severe performance drop that existing methods suffer when their backbone is frozen, not the misuse of adapters across different tasks. We will revise the sentence for clarity. \n\n**Minor 2**: We thank the reviewer for this comment. A full technical description is available in Appendix B.4, but we agree that a clearer summary in the main text would be beneficial.\n1. What is being low-rank decomposed?\nLoRA does not decompose the original, pre-trained weights ($W_h,h\\\\in\\\\{Q,K,V\\\\}$​). Instead, it constrains the weight update matrix ($\\\\Delta W$) that is learned during fine-tuning. It is represented as a low-rank decomposition, specifically as the product of two much smaller matrices: \n$\\\\Delta W=W_h^B * ​W_h^A​$. Here, $W_h$ remains frozen, and only $W_h^B$ and $​W_h^A$​ are trainable. Consider this example: A pre-trained weight matrix $W_h$​ has dimensions $d\\\\times d$, fully fine-tuning it would require updating $d^2$ parameters. By choosing a small rank  $r≪d$, we can represent the update $\\\\Delta W$ using two matrices:  $​W_h^A$​ of size $d\\times r$ and $W_h^B$ of size $r\\times d$. The total number of trainable parameters for this update is only $(d\\\\times r)+(r\\\\times d)=2dr$, which is significantly fewer than the $d^2$ parameters required for full fine-tuning.\nWe apply this to the key dense layers of the backbone: specifically, the weight matrices of the self-attention mechanism ($W_Q$​, $W_K$​, $W_V$​).\n2. How is it being injected into the model?\nIt is \"injected\" into the model as a parallel path during the forward pass. Given an input embedding $e$, the output is calculated by passing $e$ through $W_h$ and $\\\\Delta W_h$ simultaneously, and then summing their outputs.\nThe adapted forward pass is therefore:\n$h^\\prime=W_h ​e + \\\\Delta W e=W_h ​e + W_h^B ​W_h^A ​e$. This allows us to efficiently modify the behavior of a layer without altering the original weights. During inference, the learned update $\\\\Delta W$ can be merged with $W_h$​ by simple matrix addition, meaning we introduce no additional latency.\n\n**Question 1**: We thank you for the clarifying question. The term \"raw-RGB\" refers to RAW sensor data that has been converted into a 3-channel RGB format using a simple demosaicing algorithm. This is a standard and necessary pre-processing step adopted by all previous works.\n\n**Question 2**: We kindly refer the reviewer to our response to **Weakness 3** for reviewer **p2T5**.\n\n**Question 3**: It provides three main advantages\n1. Drastically Reduced Storage Costs: Instead of saving a complete large model for each task, we store the task-specific parameters, which can be less than 1% of the total.\n2. Improved Training Efficiency: Updating fewer parameters requires less GPU memory to compute and store gradients.\n3. Better Knowledge Retention: By keeping the backbone frozen, the model avoids \"catastrophic forgetting\" and retains the powerful, general-purpose knowledge from its initial training. \n\n**Question 4**: Dr. RAW achieves an inference speed of 15.9 FPS. This is highly comparable to RAW-Adapter, which runs at 17.5 FPS. A processing speed of 15.9 FPS is sufficient and practical for a range of applications."}, {"review_id": "YGWLRrZSAp", "reviewer": "Reviewer_mwbm", "summary": "This work proposes Dr. RAW, a framework designed to address data inconsistency and tuning inefficiency in RAW-based visual tasks. The key contributions include a preprocessing mechanism that integrates sensor and illumination mapping modules, followed by a re-mosaicing block, as well as a sensor-prior-efficient tuning strategy that jointly optimizes LoRA and SPP. Experiments on semantic segmentation, object detection, instance segmentation, and pose estimation demonstrate the efficiency of the proposed method.", "questions": "My main concerns are outlined in the weaknesses listed above. Please check them.", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\nS1: The motivation is clear and well-justified.\nS2: The presentation is well-organized and clearly structured.\nS3: The method integrates prompt tuning and LoRA into RAW-based high-level visual tasks, achieving strong performance with parameter-efficient tuning and establishing a new state-of-the-art in this domain.\n\nWeaknesses:\nW1: For the object detection task, why are the comparison methods different between the RASCAL-RAW dataset and the LOD dataset? Additionally, further explanation is needed regarding the advantage of Dr. RAW over the fully tuned RAW-Adapter on LOD, as their performance differences are relatively minor on RASCAL-RAW.\n\nW2: As shown in Table 7, the performance benefits significantly from pretraining on the large-scale RAW dataset.\n1) It is recommended to provide more details about the pretraining strategy.\n2) Do the other comparison methods also leverage RAW-based pretraining? If not, is the comparison fair?\n\nW3: In the definition of learnable prompts in line 160, the variable K should be defined within the main text, rather than only in the appendix.", "was_revised": true, "final_justification": "Considering the pretraining issue, I have decided to maintain my rating.", "rebuttal": "We thank the reviewer for the time and valuable feedback. We found the comments to be very constructive and have noticed several common themes regarding the clarity of our novelty and experimental details.\n\n**Weakness 1**: We appreciate you pointing out the inconsistency in the comparison methods between the two datasets. To address this and provide a more thorough comparison, we have now run experiments for Demosaicing and InvISP on the LOD dataset. \n| Method      | Setting      | mAP  |\n|-------------|--------------|------|\n| Demosacing  | frozen       | 58.5 |\n| Demosacing  | fully-tuned  | 41.1 |\n| InvISP      | frozen       | 56.9 |\n| InvISP      | fully-tuned  | 42.7 |\n\nWe will update Table 3 in the final manuscript with these new results to ensure the set of compared methods is consistent across both PASCAL-RAW and LOD.\n\nYour observation about the performance difference is correct and highlights a key strength of our method. The performance gap between Dr. RAW and other methods is more significant on more difficult datasets. PASCAL-RAW is a relatively less challenging dataset where most well-tuned methods can achieve high accuracy. This leads to a \"ceiling effect,\" where the performance differences between top methods are naturally smaller. In contrast, LOD is a significantly more challenging benchmark. This is where the architectural advantages of Dr. RAW become evident, which is reflected in the results: on LOD, Dr. RAW achieves 72.1 mAP, a significant +10.0 mAP improvement over the previous SOTA. This large margin on a challenging, real-world low-light dataset is a strong testament to the superiority of our approach.\n\n**Weakness 2**: Yes, we will add more details about the pre-training. The comparison is fair because all methods were trained on the same RAW datasets; the key difference is our efficient adaptation strategy versus their full fine-tuning approach. \n1. Pre-training Strategy: We agree that more details on the pre-training strategy are beneficial. Our backbone was pre-trained on the large-scale ADE20K RAW dataset. It was pre-trained specifically to close the domain gap between standard RGB images and RAW sensor data. This was accomplished through an end-to-end training process for the semantic segmentation task on the large-scale ADE20K RAW dataset. After this pre-training phase, the backbone's weights were frozen, and this single, RAW-specialized backbone was then used for all other downstream tasks, such as object detection and pose estimation.\n\n2. Fairness of Comparison: Thank you for this important question. To clarify:\n    1. Do other methods use RAW-based pre-training? **No.** As stated in our paper, most widely-used backbones are pre-trained on RGB images, which is the standard approach the comparison methods follow before they are adapted to the target RAW datasets.\n    2. Is the comparison fair? **Yes**, the comparison is fair because we're evaluating two different end-to-end methodologies for RAW adaptation, not just the backbones in isolation. While the comparison methods don't use large-scale RAW pre-training, they rely on full fine-tuning to adapt. Prior work's methods start with a standard, RGB-pre-trained backbone and then fully fine-tune the entire network on the target RAW dataset. This full fine-tuning is their mechanism for domain adaptation. Our method proposes a new, more efficient paradigm that includes RAW-specific pre-training to close the domain gap, followed by parameter-efficient fine-tuning on a frozen backbone.\n\nOur paper demonstrates that our proposed paradigm, which includes the RAW pre-training as a key component, is superior to the standard full fine-tuning paradigm. \n\n**Weakness 3**: Thank you for pointing this out. We will revise the manuscript to add a definition for K, as noted in the appendix:\n\"Here, K denotes the number of SPP adopted in the backbone.\""}]

Author rebuttal:

Meta-review: This paper is reviewed by four reviewers. All the 4 reviewers recommend acceptance (3 borderline accepts and 1 accept). The authors have comprehensively addressed the weaknesses and questions raised by the reviewers, strengthening the paper toward acceptance. These include clarifications regarding the novelty, explanations of the learnable re-mosaicking block and LoRA, and the conducted zero-shot and ablation studies for the proposed pre-processing blocks. Given the thorough rebuttals and demonstrated improvements, the paper is well-positioned for acceptance.

The authors should include the additional experiments (e.g., zero-shot results) and add clarifications to address the raised concerns on effectiveness, novelty and clarity in the final copy as promised. The recommendation is acceptance.
Decision: Accept (poster)