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REVIEW 3 major objections 5 minor 1 cited by

ConText: Driving In-context Learning for Text Removal and Segmentation

T0 review · 3 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read ConText claims that adapting visual in-context learning to OCR by chaining text removal and segmentation into a single prompt yields state-of-the-art results on both task families.

desk verdict Solid SOTA numbers for OCR text removal and segmentation, but the in-context learnability evidence is confounded by the self-prompting training trick. read the letter →

arxiv 2506.03799 v1 pith:OEVLLUCN submitted 2025-06-04 cs.CV

Fei Zhang , Pei Zhang , Baosong Yang , Fei Huang , Yanfeng Wang , Ya Zhang This is my paper
classification cs.CV MSC 68T4568T07
keywords visualin-contextlearningtextremovalsegmentationtaskchainingself-promptingcontext-awareaggregationscenemaskedautoencoder
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

This paper tries to establish that visual in-context learning—solving a task from example image-label pairs placed in the prompt—can be made to work for fine-grained OCR tasks such as removing text from photographs and segmenting text into foreground masks. Earlier in-context vision models used a single image-label pair and forced the model to jump straight from input to output in one reconstruction step. ConText instead chains two related tasks into one prompt—original image, text-removed image, and segmentation mask—so the model reasons through an intermediate representation, much as chain-of-thought prompting helps language models. On top of that prompt design, the paper adds a cross-attention module that lets the two demonstrations inform each other and a self-prompting training trick that keeps the model sensitive to the demonstration. The paper reports state-of-the-art numbers on both tasks and takes the large gap between ground-truth and random demonstrations as evidence that genuine in-context learning, rather than context-free specialization, is happening.

What carries the argument

The load-bearing object is the task-chaining compositor: the prompt is built as a 2×3 grid of [original image, removed image, segmentation mask] covering both the demonstration pair and the query. During training both the removal and the segmentation labels are partially masked with spatially aligned masks, and a weight-shared decoder reconstructs both, so the model can exploit the fact that both outputs describe the same text regions. Two supporting mechanisms carry the rest: context-aware aggregation (CAA), a lightweight shared cross-attention module that enriches each label representation with features drawn from the other demonstration, and self-prompting, which with probability 0.2 builds the input from two identical pairs so the model keeps relying on the demonstration rather than becoming a task specialist. The backbone is a ViT-L masked autoencoder initialized from a pre-trained in-context segmentation checkpoint.

What would settle it

Train ConText with the same recipe but replace every pseudo-label with human-annotated removal and segmentation targets on the same images, then re-run the same benchmarks; if the reported margins collapse, the gains were partly fitting the pseudo-label teacher's bias. A second check: evaluate ConText with a demonstration drawn from a different script or style than the query, and if performance barely moves, the model has lost the demonstration sensitivity that self-prompting is claimed to preserve.

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Extended reading notes

Core claim

The central claim is that restructuring the in-context prompt from a single image-label pair into a task chain—original image, removed image, segmentation mask—lets one masked-autoencoder-style model perform text removal and segmentation in a single forward pass, and do so better than models fine-tuned for either task alone. The argument leans on a simple identity: the segmentation mask is exactly the visual difference between the original image and its erased version, so chaining the tasks gives the model an intermediate to reason through instead of demanding a direct one-step mapping. The paper attributes the remaining margin to context-aware aggregation, a shared cross-attention module that injects each demonstration's features into the other's label representation, and to self-prompting, which with probability 0.2 feeds two identical input-output pairs so the model cannot collapse into a demonstration-independent specialist. On the paper's numbers, ConText reaches a PSNR of 38.36 averaged over three text removal benchmarks and an fgIoU of 76.77% averaged over four segmentation datasets, outperforming both in-context generalists and task-specific specialists, and it retains a visible performance gap between ground-truth and randomly sampled demonstrations.

Load-bearing premise

The load-bearing premise is that the training labels are trustworthy, and several are not human-annotated: as disclosed in Appendix B.1, removal images for TextSeg are produced by an external eraser model and segmentation masks for SCUT-EnsText and SCUT-Syn are computed by gray-scale differencing, so part of the reported gains could be fitting those pseudo-labels rather than genuine in-context reasoning.

Editorial extensions

If this is right

  • Any pair of visual tasks joined by a shared logical identity—here, that the segmentation mask equals the difference between an image and its erased version—can be chained into a single prompt to lift performance on both tasks.
  • In-context models trained on visually heterogeneous domains need explicit pressure, such as self-prompting, to stay demonstration-sensitive; without it, the model degrades into a context-free specialist.
  • The growing gap between ground-truth and random demonstration performance as training data scales indicates that the model's gains come partly from genuine in-context inference, and that this capability improves with more data.
  • A single randomly chosen demonstration suffices for the model to beat task-specific specialists, suggesting that costly demonstration retrieval is not needed for OCR-style in-context tasks.
  • The model can follow explicit human-style visual prompts—colored circles, boxes, and strokes drawn on the query image—without ever training on such annotations, which the paper presents as evidence of generalized in-context understanding.

Reading between the lines

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

  • If the intermediate-reasoning story is right, then adding a third linked task to the chain—say, text detection or recognition—should further improve removal and segmentation; this is a direct and cheap test of the task-chaining mechanism.
  • Because the removal labels for TextSeg come from another eraser model and the segmentation masks for SCUT-EnsText and SCUT-Syn come from gray-scale differencing, the reported margins may partly encode that teacher's biases; retraining with purely human annotations would measure that contamination.
  • The fixed self-prompting probability of 0.2 trades off in-context sensitivity against task accuracy, so a schedule that anneals the probability during training, or adapts it per sample, might dominate the fixed setting.
  • The watermark-removal result suggests the same chain composer transfers beyond OCR to any foreground-erasure task pair, such as shadow or reflection removal, where an erasure output and a foreground mask are naturally paired.
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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

3 major / 5 minor

Summary. The paper proposes ConText, a visual in-context learning (V-ICL) framework for text removal and segmentation. Three components are introduced: a task-chaining prompt that combines image, removal, and segmentation labels; a context-aware aggregation (CAA) module that fuses cross-demonstration information; and a self-prompting (SP) training strategy that occasionally feeds the model two identical input-output pairs. Experiments on multiple text removal and segmentation benchmarks report state-of-the-art results for random-demonstration inference (average PSNR 38.36 over three removal benchmarks; average fgIoU 76.77 over four segmentation benchmarks) and further claim generalized in-context learnability, evidenced by a large performance gap when the demonstration is the ground-truth label instead of a randomly selected sample.

Significance. If the central claims hold, this is a useful contribution to V-ICL for OCR: it is the first OCR-focused V-ICL generalist, it releases code, and it reports strong improvements over both generalist baselines and task-specific specialists on standard benchmarks. The random-demonstration SOTA numbers are supported by extensive comparisons and appear independent of the learnability-protocol confound discussed below. However, the paper's broader claim that self-prompting preserves generalized in-context learnability rests on a GT-demonstration measurement that may be in-distribution for ConText but out-of-distribution for the baselines, so the ICL-specific conclusion needs additional controls or a reinterpretation before the contribution is fully established.

major comments (3)
  1. [Section 5.3, Figure 5, Table 5, Appendix B.2] The ground-truth demonstration protocol is not precisely defined, and the reported numbers indicate a confound. If the 'ground-truth-based demonstration' is the query image paired with its own ground-truth label, then this input is exactly the self-prompting condition described in Section 4.3 ('two identical input-output pairs'), which ConText was trained on with probability 0.2 but the Painter and SegGPT baselines were not. Table 5 shows that the RS-versus-GT gap on TotalText segmentation and SCUT-EnsText removal grows from +0.65 fgIoU / +0.37 PSNR without SP to +3.98 fgIoU / +1.42 PSNR with SP-0.2. This pattern is consistent with the model memorizing the identical-pair training condition rather than with a general ability to reason from arbitrary GT demonstrations. Please (a) define the protocol explicitly, (b) add a control with a non-identical but ground-truth demonstration, and (c) either train baselines under the same self-prompting schedule or reinterpret the gap as an upper-bound measurement rather than evidence against 'context-free inference'.
  2. [Appendix B.1 and Impact Statement] The training targets for ConTextV include pseudo-labels: removal labels for TextSeg are generated by ViT-Eraser, and segmentation masks for SCUT-EnsText and SCUT-Syn are produced by gray-scale distance between original and erased images. The Impact Statement itself acknowledges that these labels can introduce noise and bias. Because ConTextV is trained on these pseudo-labeled datasets and then compared with specialists on TextSeg segmentation (Table 2) and SCUT-EnsText removal (Table 3), a systematic bias in the pseudo-labels could inflate the reported specialist-level results. Please quantify the quality of the pseudo-labels (for example, IoU against human masks on a subset) or provide an ablation that removes the pseudo-labeled datasets; without such evidence, the specialist comparison does not fully isolate the effect of the model design from the effect of the training labels.
  3. [Section 5.3, PromptText, Appendix B.4] The claim that PromptText is 'not training-involved' is inaccurate for ConTextV and for any baseline trained on TextSeg, because PromptText is constructed from the TextSeg validation set. Since ConTextV is trained on TextSeg training data, the evaluation is not fully held-out for that model. The HierText-trained ConText row provides a partially clean check, but the section should either restrict the claim to ConText or evaluate PromptText on a dataset that is disjoint from all training sets; the current wording overstates the generalization evidence.
minor comments (5)
  1. [Section 1 (contribution bullet)] The bullet lists '+4.50PNSR'; 'PNSR' should be 'PSNR.'
  2. [Section 5.4, Table 5] The table headers 'RS / GT' are ambiguous: clarify that the first value is the random-selected score and the second value is the delta from the GT-demonstration score, not an absolute GT score.
  3. [Appendix B.2, Tables 11-13] The rows reporting ground-truth-demonstration results are not visually labeled; add a caption note or row labels (e.g., 'GT demo') so readers can distinguish them from random-demonstration rows.
  4. [Figure 5 caption] The phrase 'a sounding upper and lower bounds' should be 'a sound upper and lower bound' (or 'sound upper and lower bounds').
  5. [Section 5.4] The sentence 'the intuitivelinear fusion (eF1) yields a significant improvement (+3.61%) fgIoU for segmentation and +1.33 PSNR for removal)' has a mismatched parenthesis and should be reworded for clarity.

Circularity Check

1 steps flagged · score 4.0 of 10

The GT-demonstration 'upper bound' behind the generalized in-context learnability claim is the self-prompting training distribution, so that claim is partly forced; the random-demonstration SOTA numbers remain independent.

  1. fitted input called prediction [Section 4.3 (Self-prompting); Section 5.3 (In-context Learnability); Table 5; Appendix B.1]
    "we propose constructing model inputs by using two identical input-output pairs (eFi = eFj) with a certain probability ... The probability of self-prompting is set to 0.2 ... Figure 5 reports all models’ performance given both the randomly-selected and ground-truth-based demonstration samples, yielding the upper and the normal in-context inference abilities ... RS (GT) denotes the model’s performance with randomly-selected (ground-truth) demonstration."

    The 'ground-truth demonstration' evaluation is the same input condition as the self-prompting training strategy: the demonstration and query are identical input-output pairs (eFi = eFj). ConText was trained on exactly this pattern with probability 0.2, while the Painter/SegGPT baselines were not, so the large RS-vs-GT gap in Table 5 (+3.98 fgIoU / +1.42 PSNR with SP-0.2, vs +0.65 / +0.37 without SP) largely measures recall of the trained self-prompting condition rather than generalized in-context inference from novel demonstrations. The paper uses this gap as the central evidence that self-prompting 'prevent[s] the model's in-context learnability from devolving into specialist-like, context-free inference,' but the upper bound is in-distribution by construction.

full rationale

The main SOTA results (Section 5.2, Tables 1-3, 9) are evaluated on held-out benchmarks with random demonstrations and are independent of the training-design choices, so they are not circular. The only load-bearing circularity is the in-context learnability metric in Section 5.3: the GT-demonstration condition duplicates the self-prompting training input defined in Section 4.3 (eFi = eFj, p=0.2), so the claimed 'upper bound' and the resulting RS-vs-GT gap are in part forced by the training distribution rather than by genuine ICL. This is a fitted training augmentation presented as a prediction of an upper bound, and it is the central evidence for the paper's claim that self-prompting preserves generalized in-context learnability. The pseudo-label caveats acknowledged in the Impact Statement are real data-quality risks but are not circular, since the reported comparisons use independent human-annotated benchmarks for the headline numbers. No load-bearing self-citation chain was found.

Assumptions & free parameters 5 free parameters · 4 assumptions · 0 invented entities

The method relies on domain assumptions about task relationships and label quality, plus several hand-chosen hyperparameters. No new physical or mathematical entities are introduced.

free parameters (5)
  • self-prompting probability = 0.2
    Chosen by hand and ablated in Table 5; SP-0.2 vs SP-0.6 changes the trade-off between task-specific accuracy and in-context responsiveness.
  • masking ratio = 85%
    Ablated in Table 6; the model peaks at 85% masking, with declines at 95%.
  • removal reconstruction loss weight = 0.3
    Set in Appendix B.1 without ablation, affects the balance between segmentation and removal reconstruction losses.
  • pixel-level supervision loss weight = 1
    Set in Appendix B.1, weights the extra cross-entropy pixel supervision used during training.
  • CAA learnable weights alpha_o, alpha_y = learned
    Learnable scalar weights in the context-free fusion step, Section 4.2; optimized during training, not derived from first principles.
assumptions (4)
  • domain assumption The segmentation mask corresponds to the visual difference between the original image and its erased counterpart.
    This is the motivation for task-chaining in Section 4.1; if false, the intermediate removal output would not provide useful supervision for segmentation.
  • domain assumption MAE-style mask reconstruction is a suitable training objective for text removal and segmentation.
    The whole framework uses masked reconstruction as the surrogate task, following Bar et al. 2022 and the Painter/SegGPT baselines, Section 3.
  • domain assumption Cross-attention aggregation at the query label position improves in-context reasoning.
    CAA is motivated by an ICL label-anchor hypothesis from cited works, Section 4.2; the module is evaluated only in ablation, not by an external theoretical justification.
  • domain assumption Pseudo-labeled removal and segmentation targets are accurate enough to serve as ground truth.
    Appendix B.1 uses ViT-eraser outputs for TextSeg and gray-scale distance for SCUT-EnsText/SCUT-Syn masks; the paper acknowledges the labels are noisy.

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Cite this review

Pith. "Pith review of ConText: Driving In-context Learning for Text Removal and Segmentation." pith.science (2026). https://pith.science/paper/OEVLLUCN

@misc{pith2026250603799,
  author       = {Pith},
  title        = {Pith review of: ConText: Driving In-context Learning for Text Removal and Segmentation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/OEVLLUCN}},
  note         = {Machine review of arXiv:2506.03799}
}
read the original abstract

This paper presents the first study on adapting the visual in-context learning (V-ICL) paradigm to optical character recognition tasks, specifically focusing on text removal and segmentation. Most existing V-ICL generalists employ a reasoning-as-reconstruction approach: they turn to using a straightforward image-label compositor as the prompt and query input, and then masking the query label to generate the desired output. This direct prompt confines the model to a challenging single-step reasoning process. To address this, we propose a task-chaining compositor in the form of image-removal-segmentation, providing an enhanced prompt that elicits reasoning with enriched intermediates. Additionally, we introduce context-aware aggregation, integrating the chained prompt pattern into the latent query representation, thereby strengthening the model's in-context reasoning. We also consider the issue of visual heterogeneity, which complicates the selection of homogeneous demonstrations in text recognition. Accordingly, this is effectively addressed through a simple self-prompting strategy, preventing the model's in-context learnability from devolving into specialist-like, context-free inference. Collectively, these insights culminate in our ConText model, which achieves new state-of-the-art across both in- and out-of-domain benchmarks. The code is available at https://github.com/Ferenas/ConText.

Figures

Figures reproduced from arXiv: 2506.03799 by the authors.

Figure 1
Figure 1. Comparison with previous V-ICL paradigm and our pro￾posed OCR-adapted in-context system. Instead of relying simply on task-specific tuning, our ConText focuses on chaining together related visual tasks to leverage their mutual benefits, thereby em￾bracing a more powerful in-context understanding and reasoning. powerful reasoning capabilities. Notably, in-context learn￾ing (ICL) (Rubin et al., 2021; Dong et al., 2022… view at source ↗
Figure 2
Figure 2. The training pipeline of previous V-ICL generalists (ob￾ject segmentation as the illustrative task). This MAE-based frame￾work formalize ICL as an composited-image (image-label) recon￾struction process. During training, the two image-label pairs serve as the mutual in-context demonstration for each other, while during inference, only one pairs’ label is masked to generate the query output. This baseline possesses 3 … view at source ↗
Figure 3
Figure 3. The training pipeline of ConText, a V-ICL framework for text removal and segmentation, enhances the baseline by leveraging inherent characteristics. We create an end-to-end multi-task generation through task-chaining. Additionally, our context-aware aggregation (CAA) enhances label representation towards better in-context understanding. We also employ a self-prompting strategy to ensure in￾context learnability for t… view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: Visualized TotalText samples generated from several in-context generalists. Each set of samples consists of the (original image, removal result, segmentation map). The prompt refers to the given visual demonstration. The red circles indicate the differences in segmenta…
Figure 5
Figure 5. Figure 5: Comparison of several visual generalists on text segmen￾tation and removal tasks when given the randomly-selected (RS) and ground-truth-based (GT) prompts. Here the segmentation (re￾moval) task is evaluated against four (SCUT-EnsText) benchmarks. This two-case performa…
Figure 6
Figure 6. Figure 6: Visualized samples of several methods on the prompting datasets. The results of the specialists are obtained through Hi￾SAM and ViT-eraser, respectively. Other generalists are prompted by the given demonstration. Zoom in for a better view [PITH_FULL_IMAGE:figures/full…
Figure 7
Figure 7. Figure 7: Illustration of label role proposed in Yu & Ananiadou (2024). Intuitively, shallow layers merge features into label positions and last position within ICL. This mechanism also inspires the design of our context-aware module. B. Experiments B.1. More Training Details Ou…
Figure 8
Figure 8. Figure 8: Visualized samples of our designed PromptText. Zoom in for a better view. Removal Segmentation Input ViT-eraser ConTextV Ground-Truth Input Hi-SAM ConTextV Ground-Truth [PITH_FULL_IMAGE:figures/full_fig_p019_8.png]
Figure 9
Figure 9. Figure 9: Qualitative comparison of our method and existing specialists on SCUT-EnsText and HierText. Our method is prompted by random demonstration. Clearly, our framework demonstrates promising performance across these tasks. Zoom in for a better view. 19 [PITH_FULL_IMAGE:fig…

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Cited by 1 Pith paper

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    Decoupling images into foreground and background before aligning them with text improves CLIP prompt tuning on few-shot and generalization benchmarks.

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    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 gl...

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.