Title: Dr. RAW: Towards General High-Level Vision from RAW with Efficient Task Conditioning
Venue: NeurIPS 2025
Accepted: yes
Keywords: Computational Photography

Image Signal Processor (ISP)
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.

Dr. RAW: Towards General High-Level Vision from RAWwith Efficient Task Conditioning

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 finetune networks for each task, Dr. RAW achieves state-of-the-art 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-mosaicing, to mitigate data inconsistencies stemming from sensor variations and lighting conditions. At the network level, we 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-specific tuning by updating only low-rank matrices in 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) on nine datasets encompassing various light conditions. By harnessing the intrinsic physical cues of RAW alongside parameterefficient techniques, Dr. RAW advances RAW-based vision systems, achieving both high accuracy and computational economy. The source code is available here.

1 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(a)), RAW data maintains higher bit depth and preserves the intrinsic physical information. These advantages, combined with their linear relationship to scene radiance, allow RAW to 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 over 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

weights. Since then, various approaches have been proposed to better improve task-specific performance (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 different tasks are typically acquired using distinct camera sensors, while camera manufacturers adopt sensors with varying color response characteristics [40; 1; 38]. At the same time, lighting characteristics and environmental conditions during photography can also cause variations in scene illumination [20; 42]. Formally, the captured camera RAW data can be represented as the following equation [5]:

where ω denotes the visible light spectrum (380 ∼ 720 nm), ρ the illuminant spectral power distribution, L the sensor-dependent spectral response, and R the scene response. In practice, even for the same scene, the captured RAW

LOD (mAP %)

PASCAL-RAW

Normal (mAP %)

PASCAL-RAW

Dark (mAP %)

PASCAL-RAW

Over-exp (mAP %)

ADE20K-RAW

Normal (mIOU %)

ADE20K-RAW

Dark (mIOU %)

ADE20K-RAW

Over-exp (mIOU %)

LIS (mAP %)

ExlPose (mAP %)

Dr. RAW (Ours)

RAW-Adapter

Dirty-Pixel

InvISP

72.1

37.0

41.2

45.3

90.4

89.7

90.3

38.5

46.3

Object

Detection

Semantic

Segmentation

Instance

Segmentation

Pose

Estimation

would vary due to differences in ρ and L . For perception, the inconsistency in data can make the downstream task challenging [47; 34], and current RAW-based perception models may further exacerbate inconsistency due to their task-oriented neural ISPs [15; 50].

Meanwhile, tuning inefficiency arises due to current RAW-based perception models normally fullytuned for a 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-efficient RAW processing systems that eliminate the requirement for extensive network parameter adjustments.

In this work, we propose Dr. RAW , a training-efficient unified solution that addresses the above challenges. Unlike previous methods that require optimizing a 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 of sRGB-pretrained knowledge while substantially reducing trainable parameters, achieved by introducing additional ∼ 2 % of the backbone's parameters and freezing the majority of network weights during adaptation. (Fig. 2(d)).

Our contributions could be summarized as follows:

We propose Dr. RAW, a new framework for RAW-based vision that achieves strong performance across tasks without end-to-end fine-tuning, instead leveraging a frozen RAWpretrained 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 9 diverse conditions (see Fig. 1). Including object detection [37; 20], 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.

RAW

sRGB

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Network1

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Network2

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(d). Efficiency & Performance

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 lowlevel 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] or 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. AdaptiveISP [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 an explicit ISP, research like Dirty-Pixels [16] replaces the ISP with a 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 a new model to improve downstream perception. Despite advancements in RAW-based high-level vision models, existing methods are often fully tuned and overfit for a specific downstream task, lacking the consideration of parameter-efficient task transfer for different tasks.

2.2 Parameter-Efficient Fine-Tuning

Parameter-efficient fine-tuning (PEFT) focuses on freezing pre-trained models and either fine-tuning only a 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 to adapt to new tasks while preserving its pre-trained knowledge. While [23] introduces taskspecific modifications at the input or feature level, low-rank adaptation (LoRA) [22] injects trainable low-rank decomposition matrices into pre-trained weights, optimizing internal weight updates in a low-rank manner. Inspired by these works, we extend such techniques to RAW-based applications.

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3 Method

The overall pipeline of our method is illustrated in Fig. 3 (a). Dr. RAW incorporates pre-processing 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-prior 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 are subject to 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, we incorporate sensor & illumination mapping blocks followed by a lightweight re-mosaicing block to process input RAW data. Motivated by the white balance design in [2], which first estimates a 3 × 3 matrix M to eliminate sensor differences and then a 1 × 3 matrix L for illumination estimation, we adopt a similar two-stage approach. As illustrated in Fig.3 (a) left, we first extract the RGBuv histogram [18] from the input demosaiced RAWdata to get its pixel distribution. The histogram is then fed into a sensor mapping block to estimate the matrix M 3 × 3 , which is then multiplied with RAW data. The transformed image is subsequently passed through an illumination mapping block to estimate the illumination ma-

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Covariance Ellipse of RAW Images

Covariance Ellipse of Mapped Images

trix L 1 × 3 . Both sensor mapping and illumination mapping blocks are composed of three simple convolution blocks.

After that, a re-mosaicing block (Fig.3(b)) R is further added to alleviate sensor- and scene-dependent 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 I is shown as follows:

We show the PCA projection of histograms in input RAW images and mapped images I 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 a 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 in an efficient way.

Taking into account the sensor difference and illumination condition, we propose a sensor prior prompt (SPP) tuning. Specifically, we adopt a set of learnable prompts P = { p k ∈ R d | k ∈ N , 1 ≤ 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:

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:

, where E is the image patch embeddings, and Q ′ = [ P , E ] W Q , K ′ = [ P , E ] W K , V ′ = [ P , E ] W V . Eq. 4 can therefore be decoupled as:

The off-diagonal terms in the attention matrix (i.e., P W Q ( E W K ) T and E W Q ( P W K ) T ) mean that the SPPs interact with the original image path embeddings in the attention computation, and the top-left term P W Q ( P W K ) T provides sensor-specific influence on the attention. On the other hand, the term P W V represents the influence imposed on the original image patch embeddings by SPPs.

In addition to SPP, we 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 rankdecomposed matrices W A , W B into the attention without modifying the original weights, enabling us to achieve effective fine-tuning with minimal parameter overhead.

To stabilize the tuning, W A is initialized with a Gaussian distribution, and W B is initialized with all zeros. By jointly optimizing LoRA and SPP, we retain the benefits of a strong pretrained backbone while introducing task-specific adaptability in a lightweight manner. This hybrid strategy significantly reduces the number of trainable parameters (see Fig. 3(d)) and facilitates fast adaptation to diverse downstream tasks with limited computation. Please find App. B.4 for more details.

4 Experiments

4.1 Experimental Setup

Datasets. We conducted experiments on semantic segmentation, object detection, instance segmentation, and pose estimation, utilizing a combination of various synthetic and real-world RAW image datasets. For object detection, we adopted 2 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 ExLPose [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 on the open-source computer vision toolboxes: mmdetection [11], mmsegmentation [13], and mmpose [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 (mIoU) to evaluate semantic segmentation, and mean Average Precision (mAP) to 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.e., AED20K RAW. Once pretrained, the backbone is frozen and transferred to other tasks. Fig. 3(d) presents a 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. 1 provides a comparison of semantic segmentation performance across multiple methods, alongside 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 to illumination 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 learning-based alternatives, Dirty-Pixel [16] and RAW-Adapter [15] show improved robustness across lighting conditions. RAW-Adapter, in particular, yields the highest mIoU 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 illumination. It attains SOTA mIoU in both over-exposed and dark settings, while maintaining competitive results in normal lighting. Notably, Dr. RAW requires fewer tunable parameters-only corresponding to a tunable ratio of 42.9%, which is significantly lower than other competitive approaches. This demonstrates the effectiveness of Dr. RAW's parameterefficient design, striking a superior balance between segmentation accuracy and model compactness, particu-

Table 1: Semantic segmentation results on ADE20K RAW. Best results are bolded and second-best are underlined.

4.3 Object Detection

Tab. 2 presents object detection performance on the RASCAL-RAW dataset, while also accounting for model efficiency in terms of tunable parameters. We compare three tuning strategies: frozen , where only the detection head is trained with a fixed backbone; fully-tuned , where both backbone

Karaimer

InvISP

SID

DNF

LiteISP

RAW Image

Ground Truth

Dr. RAW (Ours)

RAW-Adapter

Karaimer

InvISP

SID

DNF

LiteISP

RAW Image

Ground Truth

Dr. RAW (Ours)

RAW-Adapter

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tabl

Method

Setting, No. of params(M)

♣

↓

mAP

normal

over-exp

dark

Default ISP

frozen, 39.62 (10.93 / 28%)

81.4

-

-

fully-tuned, 39.62 (39.62 / 100%)

86.7

-

-

Karaimer

[25]

frozen, 39.62 (10.93 / 28%)

89.3

87.6

83.1

fully-tuned, 39.62 (39.62 / 100%)

90.1

89.1

87.6

Demosacing [37]

frozen, 39.62 (10.93 / 28%)

88.1

89.4

86.5

fully-tuned, 39.62 (39.62 / 100%)

90.0

90.1

87.9

InvISP [53]

frozen, 39.62 (10.93 / 28%)

88.7

89.3

72.3

fully-tuned, 39.62 (39.62 / 100%)

89.6

89.8

78.5

Dirty-Pixel [16]

fully-tuned, 39.42 (39.42 / 100%)

89.7

89.0

83.6

RAW-Adapter [15]

fully-tuned, 37.11 (37.11 / 100%)

89.7

89.5

86.6

Dr. RAW

adapter, 38.67 (11.36 /

29%

)

90.4

90.3

89.7

Method

Setting

mAP

Default ISP

frozen

46.8

fully-tuned

65.6

Direct (RAW)

frozen

47.5

fully-tuned

67.2

Karaimer

[25]

frozen

40.6

fully-tuned

62.5

Dirty-Pixel [16]

fully-tuned

61.6

RAW-Adapter [15]

fully-tuned

62.1

Dr. RAW

adapter

72.1

♣ Refer to Tab. 1 for the format of No. of params.

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 a total parameter count ( 38 . 67 M) comparable to Dirty-Pixel [16] ( 39 . 42 M) and RAW-Adapter [15] ( 37 . 11 M), Dr. RAW employs an adapter-based strategy requiring only 11 . 36 Mparameters (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 a fraction of the model parameters. Qualitative comparison is 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 a new SOTA. The proposed components are both effective and efficient, enabling substantial gains even in the absence of paired supervision or intensive parameter updates.

(a). Object Detection

Ground Truth

Dr. RAW (Ours)

Karaimer

SID

InvISP

Ground Truth

Dr. RAW (Ours)

Karaimer

Direct(RAW)

Default ISP

(c). Instance Segmentation

Ground Truth

Dr. RAW (Ours)

Karaimer

RAW-Adapter

InvISP

person

person:989on:100.0

person:99.3

江

4.4 Pose Estimation

The quantitative results presented in Tab. 4 report the mAP across several low-light testsets (LLN, LL-H, LL-E, LL-A), comparing Dr. RAW against relevant prior methods. Operating under the constraint of utilizing only dark RAW images, Dr. RAW consistently establishes a 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 lowlight 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 mAP 75 , which demands higher localization accuracy, highlights the quality of the instance masks predicted by our method even under challenging lowlight conditions using only dark RAW input. It is noteworthy that Dr. RAW achieves this SOTA 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 et al. baselines. This underscores the efficacy of our proposed components in Dr. RAW. Fig. 6(c) illustrates representative examples of

instance segmentation results. Additional results against methods leveraging paired RAW-dark and RAW-normal supervision can be found in App. E.

4.6 Ablation Study

Tab. 6 presents a component-wise ablation study evaluating the impact of the 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 on 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 to 89.5), demonstrating its capacity for efficient adaptation. Combining the pre-processing block and LoRA provides further improvement, especially on LOD (+26.2 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-1k (SwinT (in1k)), and Vision Transformer pretrained on ImageNet-1k (ViT (in1k)). 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 (in1k) highlights the importance of pretraining on RAW data, which preserves richer visual information compared to standard RGB inputs. Furthermore, when our techniques are applied to the ViT (in1k) backbone, the model still achieves strong results, demonstrating the generalizability of our proposed components beyond a specific architecture.

Considering the large-scale RAW pretraining is not available for our baselines, we trained Dr. RAW using full fine-tuning on in1K 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 in1K pretrained weights, Dr. RAW significantly outperforms the baseline (Direct RAW). For example, on the LOD 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 (in1K-pretrain) to 72.1 (RAW-pretrain), achieving a gain of +4.3. Moreover, compared to fully fine-tuning with In1K 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-RAW is not available, as shown in Tab. 9. We pre-trained a new backbone using only the much smaller PASCALRAW 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 a 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 is represented by a 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 chromaticity distribution, and ellipses illustrate group-wise covariance in the projected space. RAW images exhibit a long, curved spread with a large and anisotropic covariance ellipse, reflecting significant chromatic variability. In contrast, the mapped images form a 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

Wepresent Dr. RAW, a 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 high-performance perception in RAW domains.

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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 a 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 a more streamlined yet effective architecture. It leverages simple convolutional operations, channel-wise normalization, and efficient feature modulation strategies to extract and refine features.

A 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:

This operation enables direct and efficient channel-wise interaction without introducing additional parameters or computational overhead. By operating in-place and avoiding expensive non-linear functions, SG significantly reduces memory access costs while still enabling expressive transformations, making it particularly well-suited for real-time and resource-constrained applications.

In the decoding path, the block integrates PixelShuffle [44] for upsampling, which has become a preferred alternative to transposed convolutions due to its artifact-free nature and computational simplicity. PixelShuffle rearranges a tensor of shape ( C · r 2 , H, W ) into a higher-resolution tensor of shape ( C, rH, 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-style self-attention or heavy MLPs, allows it to be deeply stacked without overfitting or vanishing gradients, making it highly scalable.

B Vision Transformer and Swin Transformer

B.1 Vision Transformer

For a plain vision transformer (ViT) with N layers, an image is divided into m fixed-sized patches { I j ∈ R 3 × h × w | j ∈ N , 1 ≤ j ≤ 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:

We denote the collection of image patch embeddings E i = { e j i ∈ R d | j ∈ N , 1 ≤ j ≤ m } , as inputs to the ( i +1) -th transformer layer ( L i +1 ) . The ViT is formulated as:

Each layer L i 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 i packed together into a query matrix Q = E i W Q , a key matrix K = E i W K , and a value matrix V = E i W V , where W Q , W K , W V ∈ R d × d . We compute the matrix of outputs as:

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 a ReLU activation in between.

𝑛𝑛

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…

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W-MSA 1/ Swin-MSA 1

Patch Merging 1

…

…

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…

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W-MSA 2/ Swin-MSA 2

W-MSA

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/ Swin-MSA

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Patch Merging

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𝑝𝑝

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(b).

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B.2 Swin Transformer

Swin transformer [35] is built by replacing the standard MSA module in a transformer block by a 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 a non-overlapping manner. The window-based self-attention module lacks connections across windows, which limits its modeling power. To introduce cross-window connections while maintaining the efficient computation of non-overlapping windows, swin transformer utilizes a shifted window partitioning approach that alternates between two partitioning configurations in consecutive transformer blocks. The first module uses a 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 by ( ⌊ W 2 ⌋ , ⌊ W 2 ⌋ ) pixels from the regularly partitioned windows. The shifted window partitioning 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 a set of learnable sensor prior prompts (SPP) P = { p k ∈ R d | k ∈ N , 1 ≤ k ≤ K } 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 is kept frozen. Each query p is generated by projecting the concatenation of the sensor mapping matrix and the illumination mapping matrix into a few d -dimensional embeddings

They are inserted in the embeddings after the Embed layer (Eq. 8).

For a ViT-based backbone, as shown in Fig. 7(a), we integrate the SPPs between the cls token and image embeddings. The process for the i th layer is formulated as:

, where denotes removing the token at the position corresponding to p in the output of the i th layer, followed by inserting p i +1 before feeding E i +1 into the ( i +1) th layer.

For the Swin Transformer-based backbone, we incorporate the SPPs within local windows, excluding them during patch merging, as shown in Fig. 7(b).

B.4 Low-Rank Adaptation

A typical transformer-based 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 W h ∈ R d × d by representing it with a low-rank decomposition, i.e.,

, where W A h ∈ R d × r , W B h ∈ R r × d , and r ≪ d . During training, W h is frozen and does not receive gradient updates, while W A h and W B h contain trainable parameters. Both W h and ∆ W are multiplied with the same input, and their respective output vectors are summed coordinate-wise. Therefore, consider a matrix multiplication h = W h e in a well-trained backbone, where e is an embedding, the adapted forward pass yields:

W A h is initialized with a Gaussian distribution, and W B h is initialized with all zeros, leading to ∆ W = 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, we insert low rank matrices into W Q , W K , W V 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 ′ h t = W h t + W B h t W A h t for task t during inference. When we need to switch to another downstream task t ′ , we can replace W A h t and W B h t with W A h t ′ and W B h t ′ , a quick operation with very little memory overhead. Importantly, we do not introduce any additional latency during inference compared to a fine-tuned model by construction.

C Datasets

We conducted experiments on object detection, semantic segmentation, instance segmentation, and pose estimation, utilizing a combination of various synthetic and real-world RAW image datasets. For object detection, we adopted 2 open-source real-world datasets, PASCAL RAW [37] and LOD [20]. LOD is a real-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 a 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 is the same as 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 a 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 a 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 metrics 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 as the ratio between the intersection and the union of the predicted and ground truth masks.

This formulation penalizes both false positives and false negatives, thus providing a stringent assessment of segmentation quality. Unlike pixel accuracy, which may be overly optimistic in imbalanced datasets, IoU offers a 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 IoU.

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 at high recall and penalizes false positives and missed detections, making it a rigorous standard for assessing performance across a range of 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 a set of predictions sorted by confidence, the precision and recall values are computed at each rank, and the AP for class c is calculated as:

The mean Average Precision (mAP) is then computed as the average over all classes:

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: mAP box (based on bounding boxes) and mAP mask (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:

, where d i is the Euclidean distance between the predicted and ground truth keypoints, s is the object scale, k i is a keypoint-specific constant controlling falloff, and v i is the visibility flag. Similar to mAP in detection, AP is computed at multiple OKS thresholds (e.g., 0.50-0.95), and the final pose mAP is the mean across these thresholds and keypoints.

E Detailed Experimental Results

Owing to space constraints, we are unable to include the complete set of experimental results in the main manuscript. Additional results are provided in the appendix. All experiments were conducted on a 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-exp), 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 runner-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-invariant capability of Dr. RAWand its effectiveness in learning directly from RAW data without relying on handcrafted ISP operations.

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 finegrained texture details. While RAW-Adapter yields the highest AP on car (91.9), it underperforms on other classes such as bottle (42.5) and TV monitor (42.4), indicating limited generalization. ISPbased pipelines (e.g., Default ISP and Karaimer et 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 on 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 our approach successfully bridges the gap between efficiency and performance, setting a new standard for RAW-domain recognition.

E.2 Pose Estimation

Tab. 13 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 RAW-dark/RAW-normal supervision ( ♢ ) and those restricted to unpaired training solely on RAW-dark images ( ♠ ), the category encompassing Dr. RAW. We highlight the leading performance

Table 13: Pose estimation performance across different methods trained solely on RAW-dark images. Best results are bolded and second-best are underlined within each training category .

for each metric separately within each training paradigm. Specifically, the best-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 a new SOTA benchmark. It achieves superior mAP scores compared to all fully-tuned baselines within this category, including Direct (RAW), Karaimer et 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, a comparative analysis against methods leveraging paired RAW-dark and RAWnormal supervision ( ♢ ) reveals the striking competitiveness of Dr. RAW. While the top-performing 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 paireddata techniques across all conditions. Critically, on 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.

E.3 Instance Segmentation

Tab. 14 summarizes the instance segmentation performance of our proposed method, Dr. RAW, against a variety of methods. Our proposed method, Dr. RAW, achieves strong performance while operating in the adapter-based setting, striking a 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 ♠ ,

♢ The model is trained on the RAW-dark and RAW-normal 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 bolded and second-best are 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 the best and second-best results in each training category separately: methods trained with paired RAW-normal supervision ( ♢ ) 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% in mAP 50 , and 1.7% in mAP 75 . The substantial gain in mAP 75 , 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. While the bounding box mAP (mAP box ) of 43.6 is slightly below the fully-tuned Direct (RAW) method, our mask mAP metrics indicate superior segmentation accuracy. It is noteworthy that Dr. RAW achieves this SOTA 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 et al. baselines. Compared to methods trained with paired data ( ♢ ), Dr. RAW is remarkably competitive. While Chen et 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+SGN (37.1 mAP), Zero-DCE+SGN (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 in the form of paired normal-light images.

Tab. 15 reports per-class performance on LIS for both object detection (AP box ) and instance segmentation (AP mask ) across the methods trained solely on RAW-dark images. Dr. RAW consistently outperforms prior approaches across nearly all categories, achieving the best AP box in 6 out of 8 classes and the best AP mask in 7 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 to Karaimer et al. and the Default ISP pipeline, Dr. RAW demonstrates superior adaptability under real-world RAW distributions. While Direct(RAW) benefits from bypassing ISP artifacts, it lacks robustness in categories like TV monitor or car, where subtle color and texture cues are essential. These gains can be attributed to two key components in our architecture. First, the pre-processing 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-fidelity feature extraction early in the pipeline. Second, the parameter-efficient strategies effectively task-specific information without altering the domain-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.

E.4 Future Direction

For future research directions, we believe it is feasible to train a foundation model based on RAW images that supports multi-task learning without the need for adaptation in each specific task. For example, we could build multiple decoders on a shared backbone to address various RAW-based computer vision tasks. This approach is of crucial importance to real-world systems and downstream tasks such as autonomous driving and wildlife monitoring.

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Peer reviews:
comments: 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.

STRENGTHS AND WEAKNESSES:
Strengths:
S1: The motivation is clear and well-justified.
S2: The presentation is well-organized and clearly structured.
S3: 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.

Weaknesses:
W1: 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.

W2: As shown in Table 7, the performance benefits significantly from pretraining on the large-scale RAW dataset.
1) It is recommended to provide more details about the pretraining strategy.
2) Do the other comparison methods also leverage RAW-based pretraining? If not, is the comparison fair?

W3: 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.

QUESTIONS:
My main concerns are outlined in the weaknesses listed above. Please check them.

LIMITATIONS:
Yes
TITLE: Official Review
CLARITY: 4
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-07-01

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:  \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.\n\nQUESTIONS:\n1. 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?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 1, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\nI 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?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\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.\n\nQUESTIONS:\nSee weaknesses\n\nLIMITATIONS:\nSee weaknesses", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-04"}