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SEAGULL: No-reference Image Quality Assessment for Regions of Interest via Vision-Language Instruction Tuning

T0 review · 5 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read SEAGULL claims that a mask-guided vision-language model can return quality, importance, and distortion labels for any image region after instruction tuning on 33 million masked ROIs.

desk verdict A useful ROI-IQA system and datasets, but the headline superiority claim rests on an unfair comparison (SEAGULL fine-tuned on the test distribution's training split, baselines not). read the letter →

arxiv 2411.10161 v1 pith:L6M5CT7M submitted 2024-11-15 cs.CV

Zewen Chen , Juan Wang , Wen Wang , Sunhan Xu , Hang Xiong , Yun Zeng , Jian Guo , Shuxun Wang
show 3 more authors
Chunfeng Yuan Bing Li Weiming Hu
This is my paper · ORCID
classification cs.CV
keywords imagequalityassessmentregionofinterestvision-languagemodelinstructiontuningmask-basedfeatureextractionno-referencedistortionanalysisSegmentAnything
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 image quality assessment can be moved from whole images to arbitrary regions of interest without a reference image: given a mask produced by a segmentation model, a vision-language model can return a quality score, an importance score, and a distortion type with severity for the masked region. To make that work, the paper introduces a mask-based feature extractor that feeds both global context tokens and local region tokens into a language model, and it builds two ROI-labeling datasets: a roughly one-million-image synthetic set with 33 million automatically labeled ROIs for pretraining, and a 3,261-ROI authentic set annotated by 24 people for fine-tuning. On its test split, SEAGULL reports higher correlation and F1 scores than twelve prior IQA and VLM baselines on most ROI sub-tasks. Region-level quality labels matter because they give actionable guidance for targeted enhancement, compression, and video optimization, which whole-image scores cannot provide.

What carries the argument

The load-bearing component is the Mask-based Feature Extractor (MFE). It mask-pools the four scales of ConvNeXt features inside a binary ROI mask to form basic tokens, runs self-attention over them, then uses them as queries in cross-attention over the full-image tokens to build a global view token, while two CNNs on the mask-cropped patch produce a local view token. These two tokens are inserted as special placeholders next to the image tokens in the Vicuna LLM, so the model sees both the region's own pixels and its context. The other essential mechanism is the dataset construction: SEAGULL-100w generates distortions from RAW images through an ISP pipeline and derives ROI quality and importance labels from TOPIQ scores, while SEAGULL-3k provides human labels; both datasets use Analysis Instruction-Response and Judgment Instruction-Response pairs to tune the language model.

What would settle it

Take a random sample of, say, 300 ROIs from SEAGULL-100w, collect human ratings on the same five-level quality and importance scales used in SEAGULL-3k, and compute SROCC and PLCC between mean human scores and the TOPIQ-derived pseudo-labels. If agreement is low or varies strongly by distortion type, the pretraining signal is not a trustworthy stand-in for human ROI perception, and the pretraining gains in Table 3 could be an artifact of matching TOPIQ rather than matching people.

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Extended reading notes

Core claim

On the paper's own terms, SEAGULL is a no-reference ROI quality assessor: SAM converts a click or box into a precise mask, and the model fuses that mask with the full image to produce three outputs at once — ROI quality score, ROI importance score, and distortion type with severity. The key training recipe is two-stage: pretrain the projector, mask-based feature extractor, and LLM on SEAGULL-100w, where TOPIQ, a full-reference quality model, provides the pseudo-labels for quality and importance, then fine-tune on SEAGULL-3k, where human annotators provide the labels. The reported results are that SEAGULL outperforms existing vision-based and VLM-based IQA models on ROI quality, importance, and distortion identification (quality SROCC 0.7452, importance PLCC 0.8468, average distortion-type F1 46.93%), with the single exception that Osprey, a mask-based VLM that was also pretrained on SEAGULL-100w, keeps a small edge on importance-score prediction. The paper interprets these gains as evidence that mask-based region specification plus global and local token extraction is what ROI-level quality analysis needs.

Load-bearing premise

The pre-training labels for region quality and importance come from an automatic scoring model rather than from people, and the paper does not show that those automatic region-level labels agree with human judgments of a region's quality and importance.

Editorial extensions

If this is right

  • If SEAGULL's results hold, ROI-level IQA no longer requires cropping or bounding boxes: a precise mask gives a model the region's exact boundary, and the same model can output score, importance, and distortion labels in one pass.
  • Pretraining on a large synthetic set with model-generated ROI labels transfers to authentic images: even 25% of SEAGULL-100w raises all sub-task metrics over no pretraining, and the full set raises importance SROCC from 0.7512 to 0.8603.
  • Global context tokens are what make importance prediction possible: removing them drops importance SROCC from 0.8603 to 0.2475, so a region's impact on overall image quality can only be judged when the model sees the whole image.
  • Because the model reports distortion type and severity, not just a number, it can be used not only to rank regions but to tell users what is wrong with a region and how badly.
  • The released datasets give the field a common training and evaluation ground for ROI-based IQA, which previously lacked fine-grained region labels.

Reading between the lines

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

  • If the auto-generated labels in SEAGULL-100w are systematically biased, then the pretraining gains shown in Table 3 may partly be learning TOPIQ's region-level biases; validating a sample of those pseudo-labels against human ratings would separate genuine perceptual learning from model imitation.
  • The importance score's operational definition — how much a full-reference metric drops when the distorted region is pasted back into the reference — makes importance depend on distortion visibility, which may understate the importance of clean but semantically central regions; human importance judgments could diverge from this metric exactly in high-quality ROIs.
  • The paper's own scaling analysis shows the synthetic-to-authentic gap flattening quality-score gains between 50% and 100% of SEAGULL-100w, so the large synthetic set contributes most to importance and distortion labels; this suggests future work should focus on authentic ROI data for quality-score gains.
  • The same mask-plus-global-and-local-token architecture could be adapted to video ROI quality by replacing SAM masks with tracked object masks over time, and to generated-image quality by querying edited regions.
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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

5 major / 5 minor

Summary. The paper proposes SEAGULL, a vision-language model for no-reference image quality assessment of user-specified regions of interest (ROIs) indicated by SAM masks. The method combines a ConvNeXt image encoder, a Mask-based Feature Extractor that pools multi-scale features inside the mask and fuses global and local view tokens, and a Vicuna LLM. The authors also introduce two datasets: SEAGULL-100w, containing roughly one million synthetic distorted images with about 33 million ROIs whose quality and importance labels are generated by TOPIQ, and SEAGULL-3k, containing 3,261 human-annotated ROIs from authentic images. After pre-training on SEAGULL-100w and fine-tuning on SEAGULL-3k, the model outputs ROI quality scores, importance scores, distortion types, and severity levels. The experimental section compares SEAGULL with twelve baselines on four sub-tasks and reports SROCC, PLCC, precision, recall, and F1, together with pre-training-scale ablations and component ablations.

Significance. If the results hold, the paper makes a useful contribution: it defines a new ROI-level IQA task, releases public datasets and code, and demonstrates a mask-guided VLM architecture that jointly predicts quality, importance, and distortion labels for arbitrary image regions. The final evaluation on the human-annotated SEAGULL-3k test set is a genuine strength and avoids the circularity that would arise if the test labels were derived from the same TOPIQ-generated pre-training labels. The main weaknesses are that the headline comparison in Table 1 does not put baselines on the same training footing, and the TOPIQ-generated pre-training labels are not validated against human judgments at ROI scale; these issues directly affect the strength of the central claim that SEAGULL remarkably surpasses existing models.

major comments (5)
  1. [Table 1; Secs. 5.1-A, 5.2-A] The central comparison in Table 1 is not apples-to-apples. SEAGULL is pre-trained on SEAGULL-100w and then fine-tuned on the SEAGULL-3k training split (Sec. 5.1-A), while the twelve baselines are only queried with ROIs and prompts (Sec. 5.2-A); no baseline is fine-tuned on SEAGULL-3k, and Osprey† receives only SEAGULL-100w pre-training. The reported margins, such as 0.7452 vs 0.7377 SROCC for Quality Score and 59.08% vs 56.25% F1 for Distortion Types, are therefore compatible with the explanation that task-specific fine-tuning on the benchmark distribution, rather than the proposed mask-based architecture, is responsible for the gains. Since Contribution 3 and the abstract claim that SEAGULL remarkably surpasses existing models rest on this table, the authors should fine-tune the strongest baselines on SEAGULL-3k under the same protocol or clearly present the comparison as zero-shot/frozen-model evaluation and temper the claim accordingly.
  2. [Table 1; Sec. 5.2-A; Contribution 3] The paper's own numbers contradict the headline claim on one of the four sub-tasks. On Importance Score, Osprey† achieves SROCC 0.8811 and PLCC 0.8756, while SEAGULL achieves 0.8603 and 0.8468, so SEAGULL is not the best model on this sub-task. The text calls this a slight advantage for Osprey, but the 0.0208 SROCC gap is larger than SEAGULL's 0.0075 SROCC margin over the second-best Quality Score model. The authors should either revise the contribution statement to say that SEAGULL outperforms baselines on most sub-tasks, or provide an analysis of why the proposed model underperforms on Importance Score.
  3. [Sec. 3.1.3 A-B; Table 3] The automatically generated labels in SEAGULL-100w are not validated against human perception at the ROI scale. ROI Quality Scores are TOPIQ scores on mask-cropped patches, and ROI Importance Scores are defined as the TOPIQ difference between the reference image and the image with the distorted ROI pasted back in; this proxy conflates the visibility of a distortion with its semantic importance to the overall image. Because Table 3 measures the pre-training benefit using models trained on these labels and then fine-tuned on human labels, the improvement could in part reflect learning TOPIQ's regional biases rather than a general ROI-quality perception ability. The paper should provide a human-agreement analysis, for example the correlation between TOPIQ-derived scores and SEAGULL-3k human labels on a held-out subset, or an ablation that replaces TOPIQ labels with human labels.
  4. [Sec. 5.2-A; Table 1] No uncertainty quantification is reported for any of the headline metrics. The margins over the second-best model are small, and the Sample-Average metrics aggregate many ROIs from the same image, so the effective sample size is unclear. The authors should report confidence intervals obtained by bootstrapping at the image level, or significance tests, and state how many distinct test images contribute to the results in Table 1.
  5. [Sec. 3.2; Sec. 5.2-A; Table 1] The evaluation of Distortion Severity Degrees is underspecified for authentic images. The annotation protocol in Sec. 3.2 assigns a severity rating per distortion type and notes that authentic images often have multiple distortions, but the severity sub-task is evaluated as a single 30-class label, with a prediction considered correct only when both the severity degree and the distortion type align. The paper should specify how multiple per-type severity ratings are aggregated into the single class, how Precision, Recall, and F1 are computed for this multi-label setting, and what the chance level is; without these details, the severity results in Table 1 cannot be interpreted.
minor comments (5)
  1. [Sec. 5.3-B] The acronym for the mask-based feature extractor is written as 'MEF' once in the ablation paragraph ('remove the local and global tokens from the MEF'), but the model component is called MFE elsewhere; please standardize the notation.
  2. [Sec. 5.2-A] In the sentence 'The ROIs and text prompts are used to qurey the models for ROI quality analysis', 'qurey' should be 'query'.
  3. [Sec. 4.1] In 'we employe the ConvNeXt-Large model', 'employe' should be 'employ'.
  4. [Sec. 5.3-C] In the visualization discussion, 'it's reliability' should be 'its reliability'.
  5. [Sec. 3.1.1; Eq. (1)] The paper does not describe how the twenty parameter values for each ISP distortion type are spaced, which matters for the reliability and reproducibility of the synthetic labels. In addition, the bin boundaries in Eq. (1) are stated as M*i/5 < st <= M*(i+1)/5, which assigns a score exactly equal to M*i/5 to the lower bin while the upper boundary is inclusive; please clarify the intended discretization.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the headline results are tested against the independently human-annotated SEAGULL-3k test split, not against the TOPIQ-generated SEAGULL-100w labels that define the pre-training targets.

full rationale

The derivation chain is not circular by construction. The pre-training labels in SEAGULL-100w are generated by TOPIQ (Section 3.1.3-A,B), so pre-training necessarily teaches TOPIQ-like judgments; but this is a validity/soundness concern, not a circularity one. All headline claims (Contribution 3, Tables 1-4) are evaluated on the test split of SEAGULL-3k, whose labels are human annotations from 24 trained annotators with at least 7 judgments per ROI (Section 3.2), and whose test images are disjoint from training images (80/20 split, Section 5.1-B). No quantity reported as a 'prediction' is computed from the same TOPIQ outputs that defined the training target at evaluation time. The pre-training ablations (Table 3) also measure usefulness of SEAGULL-100w by downstream human-evaluated performance, not by agreement with TOPIQ. The concern that baselines in Table 1 are not fine-tuned on SEAGULL-3k while SEAGULL is, and that Osprey† is pre-trained but not fine-tuned, is a benchmark-fairness issue and not a definitional reduction: no equation or fitted parameter makes the comparison equal to its input. Self-citations to the authors' prior IQA works [7,8,50] are related-work context and are not load-bearing for the ROI-quality claim; no uniqueness theorem or ansatz is imported from those citations. Accordingly, no circular step meets the evidentiary standard required here.

Assumptions & free parameters 2 free parameters · 3 assumptions · 0 invented entities

The model introduces no new physical or conceptual entities. The load-bearing assumptions concern label validity: TOPIQ's patch-level quality and the replacement-based importance proxy are taken as ground truth for 33M ROIs without human validation. The hand-set 0.92 threshold and the Eq.(1) bin boundaries are free design choices that shape both labels and evaluation.

free parameters (2)
  • without-distortion threshold = 0.92
    In Section 3.1.3(C), ROIs with TOPIQ quality score above 0.92 are relabeled as 'without distortions'. This hand-set threshold changes the label distribution for a large fraction of the 33M ROIs.
  • category discretization bins = five equal bins with scale M
    Equation (1) maps continuous quality, importance, and severity scores into five discrete levels using fixed bin boundaries M*i/5. The boundary choice affects both the training targets and the evaluation categories.
assumptions (3)
  • domain assumption TOPIQ scores are valid quality labels for mask-cropped ROI patches
    Section 3.1.3(A) uses TOPIQ FR-IQA scores on padded ROI crops as ground truth quality for SEAGULL-100w, without human validation at ROI scale.
  • domain assumption ROI importance equals the full-reference quality difference caused by replacing the ROI in the reference image
    Section 3.1.3(B) defines importance operationally as the TOPIQ difference between the reference image and the image with the distorted ROI pasted back in; this proxy is not validated against human importance judgments.
  • domain assumption SAM and SEEM masks delineate semantically meaningful ROIs
    The datasets derive all ROIs from SEEM detections and SAM masks, assuming these masks correspond to objects or components that humans would consider meaningful regions.

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Cite this review

Pith. "Pith review of SEAGULL: No-reference Image Quality Assessment for Regions of Interest via Vision-Language Instruction Tuning." pith.science (2026). https://pith.science/paper/L6M5CT7M

@misc{pith2026241110161,
  author       = {Pith},
  title        = {Pith review of: SEAGULL: No-reference Image Quality Assessment for Regions of Interest via Vision-Language Instruction Tuning},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/L6M5CT7M}},
  note         = {Machine review of arXiv:2411.10161}
}
read the original abstract

Existing Image Quality Assessment (IQA) methods achieve remarkable success in analyzing quality for overall image, but few works explore quality analysis for Regions of Interest (ROIs). The quality analysis of ROIs can provide fine-grained guidance for image quality improvement and is crucial for scenarios focusing on region-level quality. This paper proposes a novel network, SEAGULL, which can SEe and Assess ROIs quality with GUidance from a Large vision-Language model. SEAGULL incorporates a vision-language model (VLM), masks generated by Segment Anything Model (SAM) to specify ROIs, and a meticulously designed Mask-based Feature Extractor (MFE) to extract global and local tokens for specified ROIs, enabling accurate fine-grained IQA for ROIs. Moreover, this paper constructs two ROI-based IQA datasets, SEAGULL-100w and SEAGULL-3k, for training and evaluating ROI-based IQA. SEAGULL-100w comprises about 100w synthetic distortion images with 33 million ROIs for pre-training to improve the model's ability of regional quality perception, and SEAGULL-3k contains about 3k authentic distortion ROIs to enhance the model's ability to perceive real world distortions. After pre-training on SEAGULL-100w and fine-tuning on SEAGULL-3k, SEAGULL shows remarkable performance on fine-grained ROI quality assessment. Code and datasets are publicly available at the https://github.com/chencn2020/Seagull.

Figures

Figures reproduced from arXiv: 2411.10161 by the authors.

Figure 1
Figure 1. (A) Illustrations of the typical Vision-based and VLM [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. The automatic pipeline for generating the S [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Overview of the SEAGULL (left) and the Mask-based Feature Extractor (right). Best viewed in color. distortions of this region”. B) Judgment Instruction-Response. To enhance the qual￾ity understanding and robustness of VLMs, we additionally design three JIRs, namely quality score judgment, impor￾tance score judgment and distortion type judgment, with queries like: “Is the quality of this region good”, “Is this re￾gio… view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: ROI quality analysis results from Human, VLMs and [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]

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Forward citations

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Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.