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REVIEW 4 major objections 6 minor 48 references

Prompt-Aware Controllable Shadow Removal

T0 review · 4 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read Prompt-aware controllable shadow removal lets a user erase one chosen subject's shadow with a dot, line, or subject mask, with no shadow annotation at test time.

desk verdict Prompt-aware shadow removal is a new and sensible task, but the evaluation is partly circular—auto-generated prompts come from the same masks that define ground truth—so the user-prompt claim is not yet supported. read the letter →

arxiv 2501.15043 v2 pith:FV7VEVY7 submitted 2025-01-25 cs.CV

Kerui Chen , Zhiliang Wu , Wenjin Hou , Kun Li , Hehe Fan , Yi Yang This is my paper · ORCID
classification cs.CV
keywords prompt-awareshadowremovalcontrollableimageeditingdetectionuserpromptspatial-frequencyinteractiondense-sparseattentiondatasetPACSRNet
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper proposes a new paradigm for shadow removal: instead of removing all shadows or requiring a precise shadow mask, the user indicates the subject whose shadow should disappear with a dot, line, or subject mask. It builds PACSRNet, a two-module network in which a prompt-aware module predicts the shadow mask of the specified subject, and a shadow removal module restores that region using both the predicted mask and prompt-aware features. The paper also contributes PCSRD, a dataset of 11,900 shadow/non-shadow image pairs with three types of prompts, and reports that PACSRNet matches or exceeds mask-based state-of-the-art methods. A sympathetic reader would care because this repositions shadow removal as an interactive, subject-selective image-editing operation rather than a batch restoration step.

What carries the argument

The load-bearing mechanism is the prompt-to-shadow-mask pathway plus the prompt-guided restoration pathway. A prompt-aware module maps an image $x$ and a prompt $c$ to a predicted mask $\hat{m}$, and a shadow removal module maps $(x, \hat{m})$ to the restored image $\hat{y}$. Inside the prompt-aware module, a spatial-frequency interaction block applies the discrete wavelet transform to capture the frequency difference between shadow and non-shadow regions, while the shadow removal module uses a dense-sparse local attention block that balances full dense attention with a screened sparse attention to avoid pulling in irrelevant content.

What would settle it

Collect prompts from human users on images outside PCSRD's generation procedure and compare the predicted shadow mask against manual shadow annotations; if dot or line IoU falls far below the reported 0.816 or 0.825, the prompt-to-subject mapping does not transfer to natural prompts.

Watch

Extended reading notes

Core claim

On its own terms, the central discovery is a new task formulation: shadow removal should be conditioned on a user's indication of the subject whose shadow is to be erased, so the required input is a subject prompt instead of an accurate shadow mask. PACSRNet realizes this by first predicting the shadow mask of the chosen subject and then restoring the region; the paper reports that this prompt-conditioned removal is comparable to or better than mask-conditioned state-of-the-art methods, and that the predicted masks are accurate under all three prompt types.

Load-bearing premise

The demonstration of prompt-driven control rests on dot and line prompts generated from subject masks by a fixed dynamic-programming routine being representative of what real users would actually draw.

Editorial extensions

If this is right

  • A user can remove the shadow of a selected subject at test time with no shadow annotation, using only a dot, line, or subject mask.
  • The full PACSRNet with dot, line, and subject-mask prompts reaches 40.956, 41.056, and 41.592 dB PSNR on PCSRD, above the mask-required ShadowDiffusion baseline at 40.823 dB.
  • The shadow removal module alone improves shadow-region PSNR on ISTD+ by 0.63 dB over the strongest baselines.
  • Prompt type is robust: predicted shadow-mask IoU stays high across dot, line, and subject masks at 0.816, 0.825, and 0.872.
  • PCSRD supplies 11,900 multi-subject samples with three prompt types for training and benchmarking prompt-based removal.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A direct extension is iterative prompting: allowing a user to add, move, or refine a dot or line after seeing the initial prediction, which would test whether the prompt-aware module supports correction rather than only one-shot selection.
  • The same two-module structure could transfer to other spatially local degradations such as glare or reflection removal, where the user points at the object owning the artifact instead of annotating the artifact region.
  • If the paradigm holds, shadow removal in photo-editing tools could shift from a pre-processing batch operation to an interactive step where the user need not know where shadows are, only which subject matters.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. The paper proposes a new task, prompt-aware controllable shadow removal, in which a user specifies a subject via a dot, a line, or a subject mask and the model removes only the shadow of that subject without requiring a shadow mask at inference. The proposed PACSRNet consists of a prompt-aware module that predicts the shadow mask for the specified subject and a shadow removal module that uses this mask and prompt-guided features to restore shadowed regions. A new dataset, PCSRD, is constructed from DESOBAv2 by automatically generating dot and line prompts from subject masks. Experiments are reported on PCSRD and ISTD+, comparing against five shadow removal baselines, along with ablations of the main components.

Significance. If the central claim is validated, the paradigm could make shadow removal more practical and interactive by replacing precise shadow-mask annotations with simple user prompts. The dataset is the first of its kind for this task and may facilitate follow-up work. The architecture itself is plausible: the spatial-frequency interaction and dense-sparse attention blocks are sensible, and the ablation study in Table 2 supports their individual contributions. However, the current evidence for the key prompt-awareness claim is weak. The prompts used in evaluation are automatically generated from the same subject masks that define the ground truth, the full prompt-based model has not been tested on real images, and the full model's quantitative performance is below several strong mask-based baselines. These issues currently limit the support for the paper's central practical claim.

major comments (4)
  1. [Section 4.1, Table 3] The evaluation of prompt-awareness is circular. Dot and line prompts in PCSRD are generated from the subject mask by a dynamic programming strategy (Section 4.1), and the same subject mask is the ground truth for the mask prediction metrics reported in Table 3. At test time the model only sees prompts sampled from the exact distribution that the training labels define, so the reported IoU/BCE values do not indicate how the model will behave with real user prompts such as off-center clicks, partial line strokes, or clicks placed on the shadow itself. Please add a human-prompt experiment (even a small user study) or, at minimum, a held-out set of manually annotated dot/line prompts, and report robustness to varying prompt placements.
  2. [Table 1] The quantitative comparison does not support the stated 'superiority' of the full prompt-based model. On PCSRD, PACSRNet with dot, line, and subject mask prompts achieves all-region PSNR of 40.96, 41.06, and 41.59 dB, respectively, which is lower than ShadowFormer (42.30 dB), HomoFormer (42.22 dB), and Inpaint4Shadow (41.97 dB); on shadow regions the gap is larger (e.g., 43.38 dB for the dot prompt versus 45.32 dB for ShadowFormer). The claim in Section 4.2 that the method is 'comparable to ShadowDiffusion' selects the weakest mask-based baseline and ignores the stronger ones. Please provide a more balanced interpretation of the results, add error bars or statistical significance tests, and explicitly discuss the trade-off between prompt-based control and removal quality.
  3. [Section 4.1, Testing Dataset] The full prompt-aware pipeline is never evaluated on real images. The ISTD+ experiments in Table 1 use only PACSRNet†, i.e., the shadow removal module with the ground-truth shadow mask as input, not the complete system with dot/line/subject-mask prompts. The PCSRD dataset itself is built from DESOBAv2 composite shadow images, so the full system is evaluated only on synthetic data. Since the paper's central practical claim is that a user can specify a subject's shadow without any shadow mask on real scenes, the absence of a real-image test of the full pipeline leaves the main claim unsupported. Please test the full model on a real shadow removal benchmark with manually provided prompts, or clearly state this as a limitation and temper the corresponding claims.
  4. [Section 4.1, Dataset Generation] The description of the dot/line prompt generation is not reproducible. The paper states only that dot and line prompts are generated 'by a dynamic programming strategy' from the subject mask, without specifying how the dot location is chosen, how the line is drawn, whether multiple prompts per subject are sampled, and what the resulting distribution looks like. Because the prompt distribution is the defining feature of the new dataset and directly shapes the task, please provide the full algorithm and a visualization of the generated prompts relative to the subject masks.
minor comments (6)
  1. [Abstract and various] There are several typos: 'Cntrollable' in the abstract, 'the our PACSRNet' in the caption of Figure 2, 'Shdow' in the caption of Figure 3, and 'PSCSRNet' in the caption of Table 3.
  2. [Throughout] The paper uses both 'dot' and 'point' for the same prompt type (e.g., 'w/ Dot' in tables versus 'point' in Section 4.2). Please use one consistent term.
  3. [Section 3.2] The input representation of the prompt is not specified. Please state how a dot or line is encoded (e.g., a sparse binary map, a coordinate list) and how it is concatenated or fused with the image features in the prompt-aware module.
  4. [Tables 1-3] No error bars or statistical significance measures are reported; all metrics appear to be from a single run. Given that the ablations report margins as small as 0.11 dB (Table 2), please report means and standard deviations over multiple seeds or runs.
  5. [Section 2.2 and Section 4.2] Instance shadow detection methods (Wang et al., 2020, 2021, 2022) are mentioned in related work but are not compared or discussed as baselines for the prompt-aware mask prediction. Since these methods also predict a specific object's shadow, a comparison (or an explicit explanation of why they are not applicable) would strengthen the evaluation.
  6. [Section 3.4] The loss weight λ is set to 3 with no sensitivity analysis. Please report the effect of varying λ on the validation performance, as this is the only free parameter in the loss.

Circularity Check

0 steps flagged · score 0.0 of 10

No load-bearing circularity: the prompt-to-shadow-mask mapping is trained and evaluated on held-out samples, and the synthetic prompt distribution is an external-validity limitation, not an input-output identity.

full rationale

The paper's central claim is an empirical architecture, not a theorem, and its derivation chain does not reduce to its inputs by construction. The prompt-aware module maps a shadow image and a prompt (dot, line, or subject mask) to a predicted shadow mask and a restored image. Although Section 4.1 states that dot and line prompts are automatically generated from the corresponding subject mask by a dynamic programming strategy, the prediction target is the subject's shadow mask, not the subject mask itself, and the input prompt is a sparse or degraded cue rather than the target. No equation in the paper equates the predicted shadow mask with the prompt, and the train/test split prevents the exact training prompts from being reused at test time. The reliance on automatically generated prompts is a legitimate external-validity limitation for the 'user prompt' claim, but it is not a circular reduction: the model is not fitted to a quantity and then reported as predicting that same quantity. Self-citations by the authors appear only in introductory, related-work, or metric definitions and are not load-bearing; there is no imported uniqueness theorem and no ansatz smuggled in via self-citation. The ISTD+ evaluation of the shadow removal module with a ground-truth mask is a genuine external benchmark for that module, even though it does not test prompt-awareness on real images. Thus, under the strict definition of circularity required here, no circular step is present.

Assumptions & free parameters 1 free parameters · 4 assumptions · 0 invented entities

The central claim rests on the validity of auto-generated prompts as user prompts, on the DESOBAv2-derived synthetic dataset, and on the accuracy of its shadow masks. These are domain assumptions adopted from prior datasets rather than independently established in this paper. The only explicit hand-set hyperparameter is lambda equals 3; standard learned network weights are excluded from the count.

free parameters (1)
  • loss trade-off lambda = 3
    Set empirically in Section 3.4 to weight shadow-removal and shadow-prediction losses; no sensitivity analysis is reported. Network weights are trained and are not listed separately.
assumptions (4)
  • domain assumption PCSRD, built by sampling DESOBAv2 composites, faithfully represents real-world multi-subject scenes with shadows and realistic user-prompt use.
    All training and evaluation for the central prompt-based claim use this synthetic dataset; no real captured shadow scenes are used.
  • ad hoc to paper Dot and line prompts generated from subject masks by a dynamic programming strategy are a valid proxy for diverse user prompts.
    This assumption connects the headline 'user prompt' capability to the experiments; no human-provided prompts or out-of-distribution prompt tests are reported.
  • domain assumption DESOBAv2 ground-truth shadow masks are accurate and correspond to the specified subject's shadow.
    The prompt-aware module is supervised with these masks through the shadow prediction loss, so errors propagate into both mask prediction and removal.
  • domain assumption Shadow regions have lower spatial frequency and abrupt boundaries relative to non-shadow regions, justifying the spatial-frequency interaction block.
    This heuristic motivates the DWT-based frequency interaction in Section 3.2 and may not hold for soft penumbras or complex lighting.

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Cite this review

Pith. "Pith review of Prompt-Aware Controllable Shadow Removal." pith.science (2026). https://pith.science/paper/FV7VEVY7

@misc{pith2026250115043,
  author       = {Pith},
  title        = {Pith review of: Prompt-Aware Controllable Shadow Removal},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/FV7VEVY7}},
  note         = {Machine review of arXiv:2501.15043}
}
read the original abstract

Shadow removal aims to restore the image content in shadowed regions. While deep learning-based methods have shown promising results, they still face key challenges: 1) uncontrolled removal of all shadows, or 2) controllable removal but heavily relies on precise shadow region masks. To address these issues, we introduce a novel paradigm: prompt-aware controllable shadow removal. Unlike existing approaches, our paradigm allows for targeted shadow removal from specific subjects based on user prompts (e.g., dots, lines, or subject masks). This approach eliminates the need for shadow annotations and offers flexible, user-controlled shadow removal. Specifically, we propose an end-to-end learnable model, the Prompt-Aware Controllable Shadow Removal Network (PACSRNet). PACSRNet consists of two key modules: a prompt-aware module that generates shadow masks for the specified subject based on the user prompt, and a shadow removal module that uses the shadow prior from the first module to restore the content in the shadowed regions. Additionally, we enhance the shadow removal module by incorporating feature information from the prompt-aware module through a linear operation, providing prompt-guided support for shadow removal. Recognizing that existing shadow removal datasets lack diverse user prompts, we contribute a new dataset specifically designed for prompt-based controllable shadow removal. Extensive experimental results demonstrate the effectiveness and superiority of PACSRNet.

Figures

Figures reproduced from arXiv: 2501.15043 by the authors.

Figure 1
Figure 1. Comparison with existing shadow removal methods. (a) [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. The overview of the our PACSRNet. PACSRNet is composed of a prompt-aware module and a shadow removal module. Prompt￾aware module takes a shadow image and a prompt c as inputs generating corresponding shadow mask mb and prompt-aware guidance feature. The generated shadow mask will be served as explicit guidance and fed into the shadow removal module along with the shadow image for shadow removal. The prompt-aware gui… view at source ↗
Figure 3
Figure 3. Examples of shadow removal results based on dot, line and subject mask prompts. In the same image, we use the prompt to specify [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Visualization of prompt-aware guidance feature maps. It [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: Visual comparison of the predicted shadow region masks [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]
Figure 7
Figure 7. Figure 7: Example of shadow removal results on the ISTD+ [PITH_FULL_IMAGE:figures/full_fig_p007_7.png]

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    write newline

    " write newline "" before.all 'output.state := FUNCTION fin.entry add.period write newline FUNCTION new.block output.state before.all = 'skip after.block 'output.state := if FUNCTION new.sentence output.state after.block = 'skip output.state before.all = 'skip after.sentence '...

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.