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VisTCP: A Visualization Framework to Construct Knowledge-Graph-Based Representation for Traditional Chinese Painting

T0 review · 4 major / 5 minor · reviewed 2026-07-08 · grok-4.5

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Pith's one-line read VisTCP lets art historians build trustworthy knowledge-graph representations of Traditional Chinese Paintings through human-in-the-loop model refinement.

desk verdict Solid domain HITL system for TCP knowledge graphs; the joint-embedding refinement loop is the real contribution, but “trustworthy” is overclaimed on qualitative evidence alone. read the letter →

arxiv 2607.05841 v1 pith:N7AFH22V submitted 2026-07-07 cs.HC cs.AI

Zhiguang Zhou , Fengling Zheng , Miaoxin Hu , Lina You , Jin Wen , Huan Liu , Wei Zhang , Dekun Qian
show 4 more authors
Yuhua Liu Wei Chen Yigang Wang Yong Wang
This is my paper · ORCID
classification cs.HCcs.AI
keywords TraditionalChinesePaintingknowledgegraphstructuredrepresentationvisualizationframeworkhuman-in-the-loopsemantictaxonomyjointembeddingarthistory
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

Standard image understanding tools fail on Traditional Chinese Paintings because the objects, scenes, and events look nothing like modern photographs, and even specialists often cannot name every ancient motif with certainty. VisTCP is a visualization framework that closes that gap by first building a TCP-specific semantic taxonomy with domain experts, training a structured-representation model on their annotations, and then showing experts where the model and their own labels diverge in a joint embedding view. Experts correct the graph; the corrections retrain the model. The loop is meant to produce knowledge-graph style descriptions of paintings that art historians can trust for archaeology and art-history work. The paper demonstrates the pipeline through a case study, a usage scenario, and expert interviews on a real painting corpus.

What carries the argument

The joint-embedding visualization view that places expert annotations and model predictions in a shared space so users can see uncertainty, correct the structured representation, and iteratively improve the TCP-oriented model.

What would settle it

On a held-out set of Traditional Chinese Paintings, measure whether VisTCP graphs improve object-and-relation coverage, inter-expert agreement, or research utility relative to unaided expert annotation and to standard vision models; a null or negative result would falsify the claim.

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Extended reading notes

Core claim

A human-in-the-loop visualization framework can produce trustworthy knowledge-graph structured representations of Traditional Chinese Paintings by combining a TCP-oriented extraction model trained on expert labels, a joint-embedding view that surfaces expert-versus-model differences, and iterative expert refinement of the resulting graph.

Load-bearing premise

A taxonomy and training labels produced with three domain experts are complete and stable enough that a model trained on them, plus iterative human correction shown in joint embeddings, will yield representations that generalize as trustworthy across a wider corpus of Traditional Chinese Paintings.

Editorial extensions

If this is right

  • Art historians can obtain consistent, machine-readable knowledge graphs of TCP objects and relationships instead of relying only on free-text notes.
  • Archaeology and art-history studies gain a reusable semantic layer for comparing motifs, events, and compositions across paintings.
  • Model uncertainty becomes visible rather than hidden, so experts know where their domain knowledge must intervene.
  • Each round of expert correction can retrain the extractor, gradually reducing the effort needed for new paintings.

Reading between the lines

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

  • The same taxonomy-plus-joint-embedding loop could transfer to other heritage image domains whose visual vocabulary diverges from natural-image benchmarks.
  • If the taxonomy is published, it becomes a community resource for labeling larger TCP datasets even outside this tool.
  • Without reported quantitative accuracy or baseline comparisons, adoption will hinge more on expert qualitative trust than on measured gains.
  • The joint-embedding view itself is a reusable pattern for any human-in-the-loop structured-representation task where model and expert disagree on rare classes.
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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 / 5 minor

Summary. The paper proposes VisTCP, a human-in-the-loop visualization framework for constructing knowledge-graph structured representations of Traditional Chinese Paintings (TCPs). Motivated by the claim that generic image structured-representation methods fail on TCPs (domain shift from natural images; hard even for experts), the authors first run a pilot study with three domain experts to build a TCP-oriented semantic taxonomy, then train a structured-representation model on expert annotations. A joint-embedding visualization view surfaces differences between expert labels and model predictions so that experts can iteratively refine outputs and, in principle, improve the model. Effectiveness is argued via a case study, a usage scenario, and expert interviews on a real TCP dataset, with the central claim that VisTCP yields trustworthy KG-style representations usable for archaeology and art-history research.

Significance. If the central claim holds, VisTCP would be a useful contribution at the intersection of visualization, digital humanities, and cultural-heritage computing: a domain-specific taxonomy plus a disagreement-aware HITL loop for a corpus that resists off-the-shelf scene-graph methods. The joint-embedding view of expert-versus-model differences is a concrete design idea that could transfer to other expert-scarce annotation settings. The work is primarily a systems/design contribution rather than a new learning algorithm; its significance therefore rests on whether the evaluation actually establishes trustworthiness and generalizability of the resulting representations beyond the study participants.

major comments (4)
  1. The abstract and evaluation design (case study, usage scenario, expert interviews) do not report quantitative extraction quality for the TCP-oriented structured-representation model—e.g., object/relation precision, recall, or F1 on held-out paintings, nor any measure of representation completeness against expert gold. The central claim of “trustworthy” KG representations is load-bearing and is not established by qualitative evidence alone; without held-out accuracy numbers it is unclear whether the model-plus-HITL loop improves correctness or mainly re-expresses the same experts’ judgments.
  2. Taxonomy construction, training labels, iterative refinement, and interview-based effectiveness evidence all appear to draw on a three-expert pool (pilot study). This creates a methodological circularity risk for the trustworthiness claim: the same narrow consensus that defines the ontology and labels also supplies the evidence that the refined outputs are good. The manuscript needs either (a) inter-annotator agreement on taxonomy and labels from a larger or held-out expert set, or (b) external validation (additional experts or art-historical ground truth not involved in taxonomy design) before the representations can be treated as stable across a broader TCP corpus.
  3. No controlled baseline or ablation is described comparing, for example, generic scene-graph / open-vocabulary methods, model-only extraction, pure expert annotation, and the full VisTCP HITL loop on the same paintings. Without such comparisons it is hard to attribute gains to the TCP-oriented taxonomy, the joint-embedding view, or iterative refinement, and hard to quantify how much the framework improves over existing structured-representation pipelines that the introduction claims “perform poorly on TCPs.”
  4. Dataset scale and split protocol are not stated in the evaluation summary (number of paintings, objects/relations annotated, train/val/test or leave-painting-out design). For a claim that the trained model plus joint-embedding refinement generalizes as trustworthy structured representation, the manuscript must report corpus size, annotation volume, and how generalization beyond the annotated set was tested; otherwise the case study and usage scenario remain anecdotal relative to the stated research goal.
minor comments (5)
  1. Clarify early what “structured representation” concretely means in the KG (node/edge types, event vs. object relations, multi-instance handling) so that later claims about semantic understanding are checkable against a fixed schema.
  2. The phrase “trustworthy structured representations” is strong; either define operational criteria (e.g., expert-verified precision thresholds, stability under re-annotation) or soften the claim language to match the qualitative evidence actually provided.
  3. Describe the joint-embedding view’s construction more precisely (what is embedded—objects, relations, full graphs; which similarity space; how uncertainty is encoded) so that other systems can reproduce or compare the design.
  4. State whether iterative expert refinements are used only for one-off correction of the current painting or are fed back to retrain/fine-tune the model, and under what schedule; the abstract implies both “refine the structured representation” and “iterative optimization of the model.”
  5. Position related work more sharply against prior cultural-heritage KG / painting annotation tools and against recent open-vocabulary detection and scene-graph models, so the TCP-specific gap is measurable rather than asserted.

Simulated Author's Rebuttal

4 responses · 0 unresolved

We thank the referee for a careful and constructive review. The four major comments correctly identify that our central claim of trustworthy TCP structured representations currently rests too heavily on qualitative evidence (case study, usage scenario, expert interviews) and on a small expert pool. We agree that the manuscript must report quantitative extraction quality, clarify dataset scale and splits, reduce circularity risk around the three-expert consensus, and provide controlled comparisons against generic structured-representation pipelines. We will revise the evaluation section accordingly and temper claims where evidence remains qualitative. Below we respond point by point and indicate the concrete revisions planned for the next version.

read point-by-point responses

  1. Referee: The abstract and evaluation design (case study, usage scenario, expert interviews) do not report quantitative extraction quality for the TCP-oriented structured-representation model—e.g., object/relation precision, recall, or F1 on held-out paintings, nor any measure of representation completeness against expert gold. The central claim of “trustworthy” KG representations is load-bearing and is not established by qualitative evidence alone; without held-out accuracy numbers it is unclear whether the model-plus-HITL loop improves correctness or mainly re-expresses the same experts’ judgments.

    Authors: We agree that the trustworthiness claim is load-bearing and that qualitative evidence alone is insufficient. The current manuscript emphasizes the HITL workflow and expert feedback; it does not report held-out object/relation precision, recall, or F1, nor a completeness measure against expert gold. In revision we will (1) add a quantitative evaluation of the TCP-oriented structured-representation model on a held-out painting set (object detection and relation extraction P/R/F1, plus a simple completeness metric relative to expert gold graphs); (2) report before/after metrics for representations refined in the joint-embedding HITL loop so that gains from iterative refinement are separated from model-only output; and (3) revise the abstract and claims so that “trustworthy” is scoped to what the numbers and expert validation jointly support, rather than implied by qualitative evidence alone. Where sample size limits statistical strength, we will state that limitation explicitly. revision: yes

  2. Referee: Taxonomy construction, training labels, iterative refinement, and interview-based effectiveness evidence all appear to draw on a three-expert pool (pilot study). This creates a methodological circularity risk for the trustworthiness claim: the same narrow consensus that defines the ontology and labels also supplies the evidence that the refined outputs are good. The manuscript needs either (a) inter-annotator agreement on taxonomy and labels from a larger or held-out expert set, or (b) external validation (additional experts or art-historical ground truth not involved in taxonomy design) before the representations can be treated as stable across a broader TCP corpus.

    Authors: The referee is right that relying on the same three-expert pool for taxonomy design, labeling, refinement, and interview-based effectiveness creates a circularity risk. We will address this in two ways. First, we will report inter-annotator agreement (e.g., pairwise agreement / Cohen’s or Fleiss’ kappa where applicable) on taxonomy categories and on object/relation labels among the participating experts, and we will document how disagreements were resolved. Second, we will add external validation: at least one additional domain expert (or a small held-out expert set) who did not design the taxonomy will review a sample of model and HITL-refined graphs, and we will report their agreement with the refined representations and any systematic disagreements. We will also expand the Limitations section to state that the taxonomy reflects a small expert consensus and that broader multi-institution validation remains future work. We cannot fully eliminate the small-pool constraint within this revision cycle, but the IAA numbers plus held-out expert review will make the stability claim more honest and testable. revision: yes

  3. Referee: No controlled baseline or ablation is described comparing, for example, generic scene-graph / open-vocabulary methods, model-only extraction, pure expert annotation, and the full VisTCP HITL loop on the same paintings. Without such comparisons it is hard to attribute gains to the TCP-oriented taxonomy, the joint-embedding view, or iterative refinement, and hard to quantify how much the framework improves over existing structured-representation pipelines that the introduction claims “perform poorly on TCPs.”

    Authors: We agree that the introduction’s claim that generic structured-representation methods perform poorly on TCPs is not backed by controlled comparisons in the current evaluation, and that attribution of gains to the taxonomy, joint-embedding view, and iterative refinement is therefore weak. In revision we will add a controlled comparison on a fixed set of paintings covering: (i) at least one generic scene-graph or open-vocabulary structured-representation baseline (as used or cited in the related work); (ii) our TCP-oriented model alone (no HITL); (iii) pure expert annotation time/quality where feasible; and (iv) the full VisTCP HITL loop. We will report the same object/relation quality metrics (and, where appropriate, expert preference or correction effort) across these conditions so that improvements can be attributed more clearly. If pure expert annotation of the full set is too costly, we will use a stratified subset and report effort (time, number of corrections) for model-only vs. HITL. Claims in the introduction and discussion will be rewritten to match what these comparisons actually show. revision: yes

  4. Referee: Dataset scale and split protocol are not stated in the evaluation summary (number of paintings, objects/relations annotated, train/val/test or leave-painting-out design). For a claim that the trained model plus joint-embedding refinement generalizes as trustworthy structured representation, the manuscript must report corpus size, annotation volume, and how generalization beyond the annotated set was tested; otherwise the case study and usage scenario remain anecdotal relative to the stated research goal.

    Authors: This is a clear omission and we will fix it. The revised manuscript will report: total number of TCP images in the corpus; number of paintings annotated for training and evaluation; counts of annotated objects, relations, and taxonomy categories; and the train/validation/test (or leave-painting-out) split protocol used for the structured-representation model. We will also state how held-out paintings used in the quantitative evaluation and in the case study / usage scenario relate to the training set, so that generalization beyond the annotated set is explicit rather than implied. If the corpus is modest, we will say so and frame the contribution as a systems/design study with preliminary generalization evidence rather than as large-scale corpus-wide validation. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: VisTCP is a systems/HCI framework paper whose claims rest on pilot taxonomy, model training, and qualitative evaluation rather than a derivation that collapses to its inputs.

full rationale

This is a visualization/HCI systems paper, not a first-principles or fitted-equation derivation. The claimed pipeline is: (1) pilot study with three experts builds a TCP semantic taxonomy; (2) expert-annotated data train a structured-representation model; (3) a joint-embedding view surfaces expert–model differences so experts can refine labels and iteratively improve the model; (4) effectiveness is argued via case study, usage scenario, and expert interviews. None of the six circularity patterns apply in a load-bearing way. There is no equation that defines a quantity in terms of the quantity it claims to predict; no free parameter fitted to data and then renamed a prediction; no uniqueness theorem or ansatz imported from the authors’ prior work to force the result; and no renaming of a known empirical law as a new derivation. Self-citation, if any, is ordinary related-work framing and is not used to prove the central trustworthiness claim. Residual methodological risk (small expert pool both defining the taxonomy and supplying interview evidence) is self-reinforcement of evaluation, not definitional circularity of a derivation chain. Per the analyzer rules, honest non-finding is the correct outcome: score 0, empty steps.

Assumptions & free parameters 0 free parameters · 4 assumptions · 2 invented entities

Systems/HCI paper, not a parameter-fitting theory paper. The load-bearing rests are domain assumptions: that a small expert pilot yields a usable TCP taxonomy, that expert annotations are adequate training/evaluation signal, and that qualitative case/interview evidence demonstrates 'trustworthy' structure. No new physical entities or free numerical constants are introduced. Invented 'entities' are system components (taxonomy, model, joint embedding view), not postulated particles or forces.

assumptions (4)
  • domain assumption A semantic taxonomy of TCP objects and relations elicited from three domain experts is adequate to ground structured representation of TCPs.
    Abstract: pilot study with three experts builds the taxonomy used for annotation and model training; no claim of broader expert consensus or coverage validation.
  • domain assumption Expert annotations of TCPs constitute reliable supervision for a structured-representation model despite acknowledged difficulty even for experts.
    Abstract notes identification is hard even for domain experts, yet expert-annotated data are used to train the model and, via the joint view, to refine outputs.
  • domain assumption Case study, usage scenario, and expert interviews suffice to demonstrate effectiveness and trustworthiness of the structured representations.
    Abstract evaluation section relies on qualitative HCI evidence forms without stating quantitative benchmarks.
  • standard math Standard supervised structured-prediction / scene-graph style learning applies once TCP-specific labels exist.
    Implicit background: train a model on expert labels to extract objects and relationships; no new learning theory claimed in the abstract.
invented entities (2)
  • VisTCP joint-embedding visualization view (expert annotations vs model predictions)
    purpose: Surface model uncertainty and differences so users can refine the knowledge-graph representation and iteratively improve the model.
    Introduced as a designed UI component of the framework; independent evidence would be external user studies or open release, not provided in the abstract.
  • TCP-oriented semantic taxonomy
    purpose: Define the object and relation vocabulary for structured TCP representation and model training.
    Constructed in a three-expert pilot; abstract does not report external validation against independent art-historical ontologies.

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Cite this review

Pith. "Pith review of VisTCP: A Visualization Framework to Construct Knowledge-Graph-Based Representation for Traditional Chinese Painting." pith.science (2026). https://pith.science/paper/N7AFH22V

@misc{pith2026260705841,
  author       = {Pith},
  title        = {Pith review of: VisTCP: A Visualization Framework to Construct Knowledge-Graph-Based Representation for Traditional Chinese Painting},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/N7AFH22V}},
  note         = {Machine review of arXiv:2607.05841}
}
read the original abstract

Structured representation can characterize semantic objects and relationships in images. It provides a possible effective way for the semantic understanding of Traditional Chinese Paintings (TCPs) to better support archaeology and art history research. However, most image-oriented structured representation methods perform poorly on TCPs, due to two major challenges: 1) the objects and events of TCPs exhibit substantial differences from modern natural images, which results in semantic misunderstandings of TCPs; and 2) it is difficult to achieve accurate identification of ancient objects and events in TCPs, even for domain experts.In this paper, we propose VisTCP, a visualization framework that combines a TCP-oriented intelligent model and expert knowledge, which enables art historians to achieve trustworthy structured representations of TCPs in a human-in-the-loop manner. Firstly, we conduct a pilot study with three domain experts to build a semantic taxonomy of TCPs. Then, expert-annotated data are used to train a TCP-oriented structured representation model, which can automatically extract meaningful objects and their relationships in TCPs. To inform users of the model uncertainty, we design a joint embedding visualization view to show the differences between expert annotations and model predictions. This allows users to refine the structured representation based on their domain knowledge, enabling iterative optimization of the model. Finally, we conduct a case study, a usage scenario, and expert interviews on a real dataset to demonstrate the effectiveness of VisTCP in supporting the structured representation and semantic understanding of TCPs.

Figures

Figures reproduced from arXiv: 2607.05841 by the authors.

Figure 1
Figure 1. The framework architecture of VisTCP contains four modules: a TCP semantic taxonomy module (A), a TCP-oriented model structured representation module (B), a visualization module (C), and an interactive refinement module (D). the intersection of art imagery and computer vision, focus￾ing on the construction and management of a digital TCP database. We collaborated with these four experts over two years. In the first … view at source ↗
Figure 2
Figure 2. The visualization interface of VisTCP consists of three components, including (A) an interactive refinement component provides the knowledge extraction results of a painting predicted by models and referenced samples for interactive refinement, (B) the uncertainty visualization component aligns the visual and semantic features of labeled and predicted elements to highlight ambiguities requiring expert verification, … view at source ↗
Figure 3
Figure 3. The joint embedding visualizations of the predicted and expert [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Structured representation facilitates the understanding of the famous Chinese painting “Along the River During the Qingming Festival” across three [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: Constructing the structured representation for “The Night Revels of Han Xizai”. The visualization of automatically generated structured representations [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 6
Figure 6. Figure 6: The feedback of the expert interviews. VII. DISCUSSION A. Potential Usage Scenario The construction of structured representation shows obvious benefits in TCP semantic representation. In addition, similar to the structured representation of natural images, scene graphs…

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Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. CompoVista: A Composition-Graph-Based Visual Analytics System for Compositional Analysis of Traditional Chinese Paintings

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    CompoVista introduces a four-layer Composition Graph and canvas-based visual analytics system enabling structure-aware retrieval, cohort comparison, and evidence inspection for Traditional Chinese Paintings.

Reference graph

Works this paper leans on

27 extracted references · 27 canonical work pages · cited by 1 Pith paper

  1. [1]

    Scene graph generation by iterative message passing,

    D. Xu, Y . Zhu, C. B. Choy, and L. Fei-Fei, “Scene graph generation by iterative message passing,” inProceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 5410–5419

    work page 2017

  2. [2]

    Unifying large language models and knowledge graphs: A roadmap,

    S. Pan, L. Luo, Y . Wang, C. Chen, J. Wang, and X. Wu, “Unifying large language models and knowledge graphs: A roadmap,”IEEE Transactions on Knowledge and Data Engineering, pp. 1–20, 2024

    work page 2024

  3. [3]

    Vip-cnn: Visual phrase guided convolutional neural network,

    Y . Li, W. Ouyang, X. Wang, and X. Tang, “Vip-cnn: Visual phrase guided convolutional neural network,” in2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 7244– 7253

    work page 2017

  4. [4]

    Graph-structured repre- sentations for visual question answering,

    D. Teney, L. Liu, and A. Van Den Hengel, “Graph-structured repre- sentations for visual question answering,” in2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 3233– 3241

    work page 2017

  5. [5]

    Align r-cnn: A pair- wise head network for visual relationship detection,

    M. Tajrobehkar, K. Tang, H. Zhang, and J.-H. Lim, “Align r-cnn: A pair- wise head network for visual relationship detection,”IEEE transactions on multimedia, vol. 24, pp. 1266–1276, 2021

    work page 2021

  6. [6]

    Unbiased scene graph generation from biased training,

    K. Tang, Y . Niu, J. Huang, J. Shi, and H. Zhang, “Unbiased scene graph generation from biased training,” inProceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020, pp. 3716– 3725

    work page 2020

  7. [7]

    McCausland and Y

    S. McCausland and Y . Hwang,On Telling Images of China: Essays in Narrative Painting and Visual Culture. Hong Kong University Press, 2013

    work page 2013

  8. [8]

    Modelling traditional chinese paintings for content-based image classification and retrieval,

    D. Zhang, B. Pham, and Y . Li, “Modelling traditional chinese paintings for content-based image classification and retrieval,” in10th Interna- tional Multimedia Modelling Conference, 2004. Proceedings.IEEE, 2004, pp. 258–264

    work page 2004

Show all 27 references
  1. [9]

    A study on a content-based image retrieval technique for chinese paintings,

    C.-C. Hung, “A study on a content-based image retrieval technique for chinese paintings,”The Electronic Library, vol. 36, no. 1, pp. 172–188, Feb. 2018

    2018

  2. [10]

    The classification of traditional chinese painting based on cnn,

    Q. Meng, H. Zhang, M. Zhou, S. Zhao, and P. Zhou, “The classification of traditional chinese painting based on cnn,” inCloud Computing and Security, X. Sun, Z. Pan, and E. Bertino, Eds. Cham: Springer International Publishing, 2018, pp. 232–241

    2018

  3. [11]

    The elements extraction on traditional chinese paintings based on object detection,

    Q. Meng, K. Li, M. Zhou, and H. Zhang, “The elements extraction on traditional chinese paintings based on object detection,” inProceedings of the 2019 2nd Artificial Intelligence and Cloud Computing Conference. New York, NY , USA: Association for Computing Machinery, 2019, pp. 111–116

    2019

  4. [12]

    Dct–cnn-based classification method for the gongbi and xieyi techniques of chinese ink-wash paintings,

    W. Jiang, Z. Wang, J. S. Jin, Y . Han, and M. Sun, “Dct–cnn-based classification method for the gongbi and xieyi techniques of chinese ink-wash paintings,”Neurocomputing, vol. 330, pp. 280–286, 2019. [Online]. Available: https://www.sciencedirect.com/science/article/pii/ S0925...

    2019

  5. [13]

    Scrolltimes: Tracing the provenance of paintings as a window into history,

    W. Zhang, J. K. Wong, Y . Chen, A. Jia, L. Wang, J. Zhang, L. Cheng, and W. Chen, “Scrolltimes: Tracing the provenance of paintings as a window into history,”CoRR, vol. abs/2306.08834, 2023. [Online]. Available: https://doi.org/10.48550/arXiv.2306.08834

    2023 doi arXiv

  6. [14]

    Data-driven npr illustrations of natural flows in chinese painting,

    Y .-C. Lai, B.-A. Chen, K.-W. Chen, W.-L. Si, C.-Y . Yao, and E. Zhang, “Data-driven npr illustrations of natural flows in chinese painting,”IEEE Transactions on Visualization and Computer Graphics, vol. 23, no. 12, pp. 2535–2549, 2017

    2017

  7. [15]

    Annotating traditional chinese paintings for immersive virtual exhibition,

    W. Ma, Y . Wang, Y .-Q. Xu, Q. Li, X. Ma, and W. Gao, “Annotating traditional chinese paintings for immersive virtual exhibition,”J. Comput. Cult. Herit., vol. 5, no. 2, aug 2012. [Online]. Available: https://doi.org/10.1145/2307723.2307725

    2012 doi

  8. [16]

    Visual genome: Connecting language and vision using crowdsourced dense image annotations,

    R. Krishna, Y . Zhu, O. Groth, J. Johnson, K. Hata, J. Kravitz, S. Chen, Y . Kalantidis, L.-J. Li, D. A. Shamma, M. S. Bernstein, and L. Fei-Fei, “Visual genome: Connecting language and vision using crowdsourced dense image annotations,”Int. J. Comput. Vision, vol. 123, no. 1,...

    2017 doi

  9. [17]

    Computational approaches for traditional chinese painting: From the

    W. Zhang, J. Zhang, K. Wong, Y . Wang, Y . Feng, L. Wang, and W. Chen, “Computational approaches for traditional chinese painting: From the ”six principles of painting” perspective,”CoRR, vol. abs/2307.14227,

    arXiv

  10. [18]

    Available: https://doi.org/10.48550/arXiv.2307.14227

    [Online]. Available: https://doi.org/10.48550/arXiv.2307.14227

    doi arXiv

  11. [19]

    Retainvis: Visual analytics with interpretable and interactive recurrent neural networks on electronic medical records,

    B. C. Kwon, M.-J. Choi, J. T. Kim, E. Choi, Y . B. Kim, S. Kwon, J. Sun, and J. Choo, “Retainvis: Visual analytics with interpretable and interactive recurrent neural networks on electronic medical records,” IEEE Transactions on Visualization and Computer Graphics, vol. 25, no...

    2019

  12. [20]

    Protosteer: Steering deep sequence model with prototypes,

    Y . Ming, P. Xu, F. Cheng, H. Qu, and L. Ren, “Protosteer: Steering deep sequence model with prototypes,”IEEE Transactions on Visualization and Computer Graphics, vol. 26, no. 1, pp. 238–248, 2020

    2020

  13. [21]

    Drava: Aligning human concepts with machine learning latent dimensions for the visual exploration of small multiples,

    Q. Wang, S. L’Yi, and N. Gehlenborg, “Drava: Aligning human concepts with machine learning latent dimensions for the visual exploration of small multiples,” ser. CHI ’23. New York, NY , USA: Association for Computing Machinery, 2023. [Online]. Available: https://doi.org/10.114...

    2023 doi

  14. [22]

    Irvine: A design study on analyzing corre- lation patterns of electrical engines,

    J. Eirich, J. Bonart, D. J ¨ackle, M. Sedlmair, U. Schmid, K. Fischbach, T. Schreck, and J. Bernard, “Irvine: A design study on analyzing corre- lation patterns of electrical engines,”IEEE Transactions on Visualization and Computer Graphics, vol. 28, no. 1, pp. 11–21, 2022

    2022

  15. [23]

    Rclens: Interactive rare category exploration and identification,

    H. Lin, S. Gao, D. Gotz, F. Du, J. He, and N. Cao, “Rclens: Interactive rare category exploration and identification,”IEEE Transactions on Visualization and Computer Graphics, vol. 24, no. 7, pp. 2223–2237, 2018

    2018

  16. [24]

    Chinese iconography thesaurus,

    POSTHUMANITIES, “Chinese iconography thesaurus,” https://chineseiconography.org/

  17. [25]

    Mask r-cnn,

    K. He, G. Gkioxari, P. Doll ´ar, and R. Girshick, “Mask r-cnn,” in2017 IEEE International Conference on Computer Vision (ICCV), 2017, pp. 2980–2988

    2017

  18. [26]

    Real: A representative error-driven approach for active learning,

    C. Chen, Y . Wang, L. Liao, Y . Chen, and X. Du, “Real: A representative error-driven approach for active learning,” inMachine Learning and Knowledge Discovery in Databases: Research Track: European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Proceedings, ...

    2023

  19. [27]

    Available: https://doi.org/10.1007/978-3-031-43412-9 2

    [Online]. Available: https://doi.org/10.1007/978-3-031-43412-9 2

    doi

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