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REVIEW 4 major objections 6 minor 23 references

A model that reads a task prompt and an image can predict where people will look under that goal.

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2026-07-14 15:14 UTC pith:4N5SLUZJ

load-bearing objection Clean modular task-conditioned saliency pipeline; ablations undercut the claim that the full fusion design is necessary. the 4 major comments →

arxiv 2607.09827 v1 pith:4N5SLUZJ submitted 2026-07-10 cs.CV

TDSal: Task-Based Top-Down Saliency Prediction Model

Can Mizrakli , Tolga K. Capin This is my paper
classification cs.CV
keywords visual saliencytop-down saliencytask-based saliencyvisual attentiongazevision-language fusionSentence-BERTYOLO features
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

Most computer models of visual attention assume free viewing: people look at what is bright, colorful, or unusual. In real life, people look where a goal sends them—find the exit, check the traffic light, count the people. This paper argues that you can build a saliency model that takes a short natural-language task description, fuses its meaning with object-aware visual features from the image, and outputs a fixation map that shifts with the goal. The architecture is modular: a truncated object-detection backbone supplies spatial features, a sentence encoder supplies a task token, and a shallow transformer mixes them before a small decoder draws the map. On a four-task eye-tracking set the model produces maps that move with the prompt and reach high fixation-alignment scores under that protocol. The claim is that explicit language conditioning is a practical route to goal-directed attention prediction.

Core claim

Conditioning dense saliency prediction on natural-language task embeddings, fused with YOLO-derived spatial features through a shallow transformer, produces task-dependent fixation maps that better track goal-directed human attention than free-viewing assumptions allow.

What carries the argument

TDSal: YOLO spatial features projected to 128 channels, a Sentence-BERT task token of matching size, and a one-layer transformer that treats the task token as an extra sequence element so self-attention can reweight image regions by goal.

Load-bearing premise

The paper treats the full language-plus-transformer stack as the right way to model task-driven attention even though its own ablations show stripped-down variants scoring higher on most standard saliency numbers.

What would settle it

On a larger multi-task eye-tracking set with open-ended prompts, measure whether maps from the full TDSal shift more correctly with the stated goal than maps from the no-task or no-transformer ablations, under a metric that scores semantic consistency with the prompt rather than only aggregate map similarity.

Watch this falsifier — get emailed when new claim-graph text bears on it.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 6 minor

Summary. The paper proposes TDSal, a modular top-down saliency model that conditions dense fixation-density prediction on natural-language task prompts. Visual features are taken from the first 10 layers of a pretrained YOLOv5su backbone (single 12×12×512 map after SPPF), projected by a 1×1 FPM to 128 channels, fused with a Sentence-BERT (MiniLM-L6) task token via a one-layer transformer encoder, and decoded to a 96×96 saliency map. Training uses a KL+CC loss on Albayrak’s four-task eye-tracking set (1,968 image–map pairs; 70/15/15 split). Reported test scores include NSS 3.4583, AUC–Borji 0.9177, AUC-J 0.9515 and sAUC 0.8649; qualitative overlays show prompt-dependent map shifts. Contextual comparisons to TGSal and SalClassNet and a single-checkpoint ablation of task, transformer, SBERT and FPM are provided.

Significance. Task-conditioned dense saliency is a practically relevant and under-served problem relative to free-viewing benchmarks. The modular design (detection backbone + compact language token + shallow fusion only) is a clear, reproducible alternative to heavy multimodal transformers or diffusion saliency models, and the authors correctly avoid claiming direct MIT300/SALICON superiority. A public repository is linked. If the fusion pathway were shown to improve goal-aligned attention under controlled same-dataset baselines and task-sensitive metrics, the work would be a useful contribution to top-down saliency and vision–language attention. As written, the quantitative case for the full architecture is not yet established, so significance remains conditional on stronger evidence for the central design claim.

major comments (4)
  1. Table 4 (and §4.5): the central claim that YOLO + Sentence-BERT + transformer fusion enables more faithful task-driven saliency is undercut by the paper’s own ablations. The w/o Transformer variant improves CC (0.6902 vs 0.6435), SIM (0.5575 vs 0.5160), NSS (3.9983 vs 3.6261), AUC–Borji, AUC-J and sAUC; w/o Task also edges the full model on CC/SIM/NSS. Reinterpreting this as a limitation of aggregate metrics is not supported by any new task-specific or semantic-consistency measure. Either introduce such a metric (e.g., prompt-swap consistency, per-task shift alignment) that favors the full model, or revise the claim so that necessity of TFM/task token is not asserted from these scores.
  2. §4.4 / Table 3: literature numbers for TGSal and SalClassNet are presented as contextual references, but they use different conditioning signals, datasets and protocols. Without same-dataset re-implementation or shared splits, absolute scores (NSS, AUC, etc.) cannot support that TDSal’s fusion design is competitive for natural-language task conditioning. At minimum, report a strong free-viewing or task-agnostic baseline trained on the same Albayrak split, and clarify that Table 3 is not a ranking.
  3. §4.5 and Limitations: each ablation is a single checkpoint with no error bars, seeds, or repeated runs. Metric differences of the size shown can arise from training noise on a 1,377-pair set. Load-bearing architectural conclusions require multi-seed means/stds (or at least several independent runs) before arguing that poorer full-model scores still vindicate the design.
  4. §3.3–3.4 / RQ1–RQ2: the dataset has only four fixed task categories. The paper claims conditioning on natural-language task descriptions and prompt-dependent shifts (Fig. 4), but does not test held-out phrasings, compositional prompts, or open vocabulary. Without that, the Sentence-BERT pathway’s benefit over a 4-way task ID embedding remains unproven; a controlled language-vs-ID ablation would directly address RQ1–RQ2.
minor comments (6)
  1. Abstract and §1 claim ‘more faithful modeling’ without stating the comparison condition; align wording with the limited, task-conditioned protocol used in §4.1.
  2. Table 2 vs Table 4: full-model numbers differ slightly (e.g., test CC 0.6423 vs ablation CC 0.6435); state whether ablations use val or a different checkpoint/protocol.
  3. §3.2: ‘spatially rich’ / multi-receptive-field language for a single stride-32 map after SPPF is easy to over-read; a short note that no FPN is used would help.
  4. Fig. 4: add the exact task prompt text per column so readers can judge semantic alignment without guessing.
  5. Loss (§3.4): α=β=1.0 is fine but unmotivated; a one-line sensitivity note would strengthen reproducibility claims.
  6. Minor typos/spacing: ‘humanattentionisoftenshaped’, ‘saliencymap’, ‘T able 1’, inconsistent ‘V alidation’ spacing in Table 2.

Circularity Check

0 steps flagged

No circular derivation: TDSal is ordinary supervised multimodal training; reported metrics are not forced by fitted constants or self-citation chains.

full rationale

Walking the claimed chain (YOLO spatial features + Sentence-BERT task token + shallow transformer fusion → task-conditioned saliency maps, evaluated by CC/KL/SIM/NSS/AUC on Albayrak’s four-task eye-tracking set) yields no step that reduces to its inputs by construction. The loss L = α KL + β(1−CC) with α=β=1.0 is a hand-set training objective, not a parameter fitted on a subset and then re-reported as a prediction. Architecture choices (first 10 YOLOv5su layers, 1×1 FPM, MiniLM-L6→128 task token, 1-layer fusion encoder) are a priori design decisions, not uniqueness theorems or ansatzes imported from the authors’ prior work. Citations to Albayrak supply the dataset, not a load-bearing theoretical premise that forbids alternatives; there is no self-citation uniqueness claim, no renaming of a known free-viewing law as a new task-driven identity, and no equation that is definitionally identical to a fitted input. Ablation Table 4 (full model underperforming w/o Transformer / w/o Task on several aggregate metrics) and the authors’ reinterpretation of that result are validity/interpretation issues, not circular forcing of the reported numbers. The paper is self-contained as standard empirical ML; circularity score is therefore 0.

Axiom & Free-Parameter Ledger

3 free parameters · 3 axioms · 1 invented entities

The central claim rests on ordinary deep-learning design choices plus three domain assumptions about feature suitability and metric interpretation. No new physical entities are postulated. Free parameters are the usual training knobs and the equal loss weights.

free parameters (3)
  • loss weights alpha, beta = 1.0, 1.0
    Set by hand to 1.0 for the combined KL + (1-CC) objective; no search or justification beyond convenience.
  • learning rate and epoch count = 1e-4, 50
    Adam 1e-4 for 50 epochs, batch 8; chosen without reported sweep.
  • feature dimensions (512->128, task 384->128) = 128
    Projection sizes chosen to match transformer d_model; not derived.
axioms (3)
  • domain assumption Early YOLO layers supply spatially rich, object-centered features that align with human gaze under tasks.
    Stated as design hypothesis in Section 3.1; used to justify truncating YOLOv5su at SPPF.
  • domain assumption Sentence-BERT MiniLM embeddings of natural-language task prompts carry the semantic intent needed to modulate saliency.
    Section 3.2 Task Encoder; no independent verification that the embedding space matches gaze shifts.
  • ad hoc to paper Aggregate saliency metrics (CC, NSS, SIM, AUC) can fail to capture task-semantic consistency, so poorer scores for the full model do not refute the architecture.
    Introduced in Section 4.5 to reconcile the ablation table with the claim that the full model is preferable.
invented entities (1)
  • TDSal modular fusion pathway (FPM + TFM + task token) no independent evidence
    purpose: To realize explicit language-conditioned dense saliency without a full multimodal backbone.
    The named architecture is the paper's main contribution; it has no existence outside this work.

pith-pipeline@v1.1.0-grok45 · 14678 in / 2797 out tokens · 34689 ms · 2026-07-14T15:14:54.195549+00:00 · methodology

0 comments
read the original abstract

Visual saliency aims to predict the regions of an image most likely to attract human visual attention. While most saliency models assume free-viewing conditions, human attention is often shaped by explicit task goals. In this work, we address task-driven saliency prediction by proposing a model that conditions visual attention on natural-language task descriptions. The model produces task-dependent saliency maps that reflect how attention shifts under different viewing intents. Through quantitative and qualitative analysis, we show that incorporating explicit task semantics enables more faithful modeling of goal-directed visual attention.

Figures

Figures reproduced from arXiv: 2607.09827 by Can Mizrakli, Tolga K. Capin.

Figure 1
Figure 1. Figure 1: Conceptual overview of task-driven saliency prediction. Different task prompts lead to distinct human fixation patterns on the same image. We model this by fusing visual features extracted from the image with semantic representations of the task prompt, producing a task-based saliency map that reflects where a viewer would look given a specific goal. – RQ1: How can textual task definitions, encoded through… view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the model architecture. TDSal builds on these directions by combining language-based task condi￾tioning with object-centered visual features for dense saliency prediction. 3 Proposed Method We propose TDSal, a task-conditioned saliency model composed of four main stages: (i) a YOLO-based backbone that extracts spatial visual feature maps, (ii) a 1×1 Feature Projection Module (FPM) that reduces … view at source ↗
Figure 3
Figure 3. Figure 3: Training and validation trends over 50 epochs. time, validation NSS and AUC–Borji increase steadily, while CC and SIM im￾prove gradually and KL divergence decreases. These trends indicate stable con￾vergence and progressively improved alignment with task-conditioned fixation distributions. 4.3 Qualitative Visualization [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Task-conditioned saliency predictions. Rows show input overlays, ground-truth fixation density maps, and predicted saliency maps. TGSal uses image captions or descriptions, and SalClassNet uses a visual clas￾sification objective. Their datasets and evaluation protocols also differ. The ta￾ble therefore highlights methodological proximity and metric coverage, while motivating the need for standardized bench… view at source ↗

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Reference graph

Works this paper leans on

23 extracted references · 1 canonical work pages

  1. [1]

    Master’s thesis, TED University (2020),https://github

    Albayrak, D.: A Study of Visual Saliency for Free-Viewing and Task- Oriented Condition. Master’s thesis, TED University (2020),https://github. com/DilaraAlbayrak/Task-based-eye-fixation-dataset

    2020

  2. [2]

    IEEE Transactions on Image Processing22(1), 55–69 (2012)

    Borji, A., Sihite, D.N., Itti, L.: Quantitative analysis of human-model agreement in visual saliency modeling: A comparative study. IEEE Transactions on Image Processing22(1), 55–69 (2012)

    2012

  3. [3]

    In: European Conference on Computer Vision

    Chen, S., Jiang, M., Yang, J., Zhao, Q.: Air: Attention with reasoning capability. In: European Conference on Computer Vision. pp. 91–107. Springer (2020)

    2020

  4. [4]

    In: Pro- ceedings of the IEEE Conference on computer vision and pattern recognition

    Fan, S., Shen, Z., Jiang, M., Koenig, B.L., Xu, J., Kankanhalli, M.S., Zhao, Q.: Emotional attention: A study of image sentiment and visual attention. In: Pro- ceedings of the IEEE Conference on computer vision and pattern recognition. pp. 7521–7531 (2018)

    2018

  5. [5]

    Nature reviews neuroscience2(3), 194–203 (2001)

    Itti, L., Koch, C.: Computational modelling of visual attention. Nature reviews neuroscience2(3), 194–203 (2001)

    2001

  6. [6]

    IEEE Transactions on pattern analysis and machine intelligence 20(11), 1254–1259 (2002)

    Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on pattern analysis and machine intelligence 20(11), 1254–1259 (2002)

    2002

  7. [7]

    In: Pro- ceedings of the IEEE conference on computer vision and pattern recognition

    Jiang, M., Huang, S., Duan, J., Zhao, Q.: Salicon: Saliency in context. In: Pro- ceedings of the IEEE conference on computer vision and pattern recognition. pp. 1072–1080 (2015) TDSal: Task-Based Top-Down Saliency Prediction Model 15

    2015

  8. [8]

    In: European conference on com- puter vision

    Jiang, M., Xu, J., Zhao, Q.: Saliency in crowd. In: European conference on com- puter vision. pp. 17–32. Springer (2014)

    2014

  9. [9]

    https://doi.org/10.5281/zenodo.3908559, version 7.0

    Jocher, G., Ultralytics: Ultralytics YOLOv5.https://github.com/ultralytics/ yolov5(2020). https://doi.org/10.5281/zenodo.3908559, version 7.0

    doi:10.5281/zenodo.3908559 2020

  10. [10]

    In: 2009 IEEE 12th international conference on computer vision

    Judd, T., Ehinger, K., Durand, F., Torralba, A.: Learning to predict where humans look. In: 2009 IEEE 12th international conference on computer vision. pp. 2106–

    2009

  11. [11]

    In: Proceedings of the IEEE/CVF international conference on computer vision

    Kamath, A., Singh, M., LeCun, Y., Synnaeve, G., Misra, I., Carion, N.: Mdetr- modulated detection for end-to-end multi-modal understanding. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 1780–1790 (2021)

    2021

  12. [12]

    In: Proceedings of the European Conference on Computer Vision (ECCV)

    Kummerer, M., Wallis, T.S., Bethge, M.: Saliency benchmarking made easy: Sep- arating models, maps and metrics. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 770–787 (2018)

    2018

  13. [13]

    In: 2015 International Symposium on Bioelectronics and Bioin- formatics(ISBB).pp.200–203(2015).https://doi.org/10.1109/ISBB.2015.7344958

    Liang,J.,Zhang,Y.:Topdownsaliencydetectionviakullback-leiblerdivergencefor object recognition. In: 2015 International Symposium on Bioelectronics and Bioin- formatics(ISBB).pp.200–203(2015).https://doi.org/10.1109/ISBB.2015.7344958

    work page doi:10.1109/isbb.2015.7344958 2015

  14. [14]

    In: Proceedings of the IEEE/CVF international conference on computer vision

    Liu, N., Zhang, N., Wan, K., Shao, L., Han, J.: Visual saliency transformer. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 4722–4732 (2021)

    2021

  15. [15]

    In: Pro- ceedings of the IEEE/CVF conference on computer vision and pattern recognition

    Mondal, S., Yang, Z., Ahn, S., Samaras, D., Zelinsky, G., Hoai, M.: Gazeformer: Scalable, effective and fast prediction of goal-directed human attention. In: Pro- ceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 1441–1450 (2023)

    2023

  16. [16]

    Computer Vision and Image Understanding172, 67–76 (2018)

    Murabito, F., Spampinato, C., Palazzo, S., Giordano, D., Pogorelov, K., Riegler, M.: Top-down saliency detection driven by visual classification. Computer Vision and Image Understanding172, 67–76 (2018)

    2018

  17. [17]

    arXiv preprint arXiv:2410.07149 (2024)

    Neo, C., Ong, L., Torr, P., Geva, M., Krueger, D., Barez, F.: Towards inter- preting visual information processing in vision-language models. arXiv preprint arXiv:2410.07149 (2024)

    Pith/arXiv arXiv 2024

  18. [18]

    In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (July 2017)

    Ramanishka, V., Das, A., Zhang, J., Saenko, K.: Top-down visual saliency guided by captions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (July 2017)

    2017

  19. [19]

    In: Proceedings of the IEEE conference on computer vision and pattern recognition

    Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 779–788 (2016)

    2016

  20. [20]

    arXiv preprint arXiv:1908.10084 (2019)

    Reimers, N., Gurevych, I.: Sentence-bert: Sentence embeddings using siamese bert- networks. arXiv preprint arXiv:1908.10084 (2019)

    Pith/arXiv arXiv 1908

  21. [21]

    IEEE Transactions on Image Processing33, 5392– 5407 (2024)

    Sun, Y., Min, X., Duan, H., Zhai, G.: How is visual attention influenced by text guidance? database and model. IEEE Transactions on Image Processing33, 5392– 5407 (2024)

    2024

  22. [22]

    arXiv preprint arXiv:2410.14072 (2024)

    Wen,Y., Cao, Q.,Fu,Q., Mehta, S., Najibi,M.: Efficient vision-language models by summarizing visual tokens into compact registers. arXiv preprint arXiv:2410.14072 (2024)

    Pith/arXiv arXiv 2024

  23. [23]

    In: International Conference on Pattern Recognition

    Zhang, N., Xiong, M., Zhu, D., Zhu, K., Zhai, G.: Tdiffsal: Text-guided diffu- sion saliency prediction model for images. In: International Conference on Pattern Recognition. pp. 15–31. Springer (2024)

    2024