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arxiv: 2606.17430 · v1 · pith:3MS36KGQnew · submitted 2026-06-16 · 💻 cs.CV

CIAN: Multi-Stage Framework for Event-Enriched Image Captioning via Retrieval-Augmented Generation

Trinh Thi Thu Hien , Trung-Nghia Le This is my paper

Pith reviewed 2026-06-27 02:22 UTC · model grok-4.3

classification 💻 cs.CV
keywords event-enriched image captioningretrieval-augmented generationmulti-stage frameworkcontext-aware captionsOpenEvents-V1SigLIP retrievalQwen fine-tuningN-Gram refinement
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The pith

CIAN retrieves relevant articles to generate image captions that include event timing, location and participants.

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

The paper presents CIAN as a multi-stage system that first retrieves articles matching an image using SigLIP, then summarizes those articles to condition a LoRA-fine-tuned Qwen model for narrative generation, and finally applies N-Gram refinement. This pipeline is designed to produce captions that describe not only visible pixels but also surrounding event context that standard models miss. On the OpenEvents-V1 benchmark the method records retrieval mAP of 0.979 and raises CIDEr from 0.030 to 0.094. A reader would care because most image captioners remain limited to what is directly visible and therefore omit facts that make descriptions informative and human-like. The results indicate that retrieval-augmented reasoning plus linguistic cleanup can measurably close that gap.

Core claim

CIAN achieves retrieval mAP 0.979 and lifts CIDEr from 0.030 to 0.094 on OpenEvents-V1 by retrieving articles with SigLIP, summarizing them to guide a LoRA-fine-tuned Qwen model in a Narrative Generation stage, and applying N-Gram-based Refinement for fluency and coherence.

What carries the argument

The CIAN multi-stage pipeline in which SigLIP article retrieval supplies external narratives that are summarized to condition LoRA-fine-tuned Qwen generation, followed by N-Gram refinement.

If this is right

  • Image captions can incorporate timing, location and participants that are not visible in the pixels.
  • Retrieval-augmented generation plus n-gram refinement yields more coherent context-aware text.
  • High retrieval precision (mAP 0.979) is attainable on event-focused benchmarks.
  • The same staged process improves standard automatic metrics such as CIDEr.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The framework could be tested on video or social-media event data to handle time-varying context.
  • Replacing the article corpus with domain-specific sources might extend the method to specialized fields such as sports or historical imagery.
  • Measuring caption accuracy against ground-truth event logs would directly test whether retrieval noise propagates into hallucinations.

Load-bearing premise

Articles retrieved by SigLIP contain accurate event details that the summarization and fine-tuned model can integrate without adding factual errors.

What would settle it

A controlled test in which deliberately inaccurate or off-topic articles are fed into the pipeline and the resulting captions are checked for introduced factual errors or hallucinations.

Figures

Figures reproduced from arXiv: 2606.17430 by Trinh Thi Thu Hien, Trung-Nghia Le.

Figure 1
Figure 1. Figure 1: Overview of the proposed Contextual Image-Article Narrator (CIAN) [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Examples from the OpenEvents-V1 dataset with corresponding event [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Qualitative examples generated by the proposed framework. [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Failure cases of the retrieval results produced by our framework. [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Our method accurately captures the visual content but fails to recognize [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗
read the original abstract

Event-enriched image captioning describes not only visible content but also the broader context of events, including timing, location, and participants, capabilities missing in most pixel-bound models. We propose the Contextual Image-Article Narrator (CIAN), a multi-stage framework that enriches captions with external narratives. CIAN retrieves relevant articles using SigLIP, summarizes them to guide a Narrative Generation stage with a LoRA-fine-tuned Qwen model, and applies N-Gram-based Refinement for fluency and coherence. On the OpenEvents-V1 benchmark, CIAN achieves high retrieval performance (mAP 0.979) and improves caption quality, increasing CIDEr from 0.030 to 0.094. These results highlight the effectiveness of retrieval-augmented reasoning combined with linguistic refinement for generating context-aware, human-like captions.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The paper proposes CIAN, a multi-stage retrieval-augmented framework for event-enriched image captioning. It retrieves articles via SigLIP, summarizes them to condition a LoRA-fine-tuned Qwen model for narrative generation, and applies N-Gram refinement. On OpenEvents-V1 it reports retrieval mAP of 0.979 and a CIDEr increase from 0.030 to 0.094, claiming this demonstrates effective context-aware captioning.

Significance. If the factual correctness of the added event details can be established, the work would offer a concrete demonstration that retrieval-augmented generation can supply timing, location, and participant information missing from standard vision-language models. The explicit multi-stage pipeline and the use of an open-weight LLM with LoRA are positive elements that could be reproduced.

major comments (2)
  1. [Abstract] Abstract: The central quantitative claim (CIDEr rising from 0.030 to 0.094) is presented without naming the baseline model, reporting statistical significance, or providing error bars, so it is impossible to determine whether the gain supports the claim of improved event enrichment.
  2. [Abstract] Abstract and evaluation description: No factuality metric, human verification of event claims, or error analysis on generated captions is mentioned. CIDEr only measures n-gram overlap and cannot confirm that timing/location/participant facts supplied by the SigLIP-retrieved articles are accurately integrated rather than hallucinated.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment below and commit to revisions that strengthen the presentation of results and evaluation.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central quantitative claim (CIDEr rising from 0.030 to 0.094) is presented without naming the baseline model, reporting statistical significance, or providing error bars, so it is impossible to determine whether the gain supports the claim of improved event enrichment.

    Authors: We agree that the abstract must explicitly identify the baseline. The reported CIDEr of 0.030 is from the LoRA-tuned Qwen model without retrieval augmentation or N-gram refinement. We will revise the abstract to name this baseline clearly. We will also add statistical significance testing and error bars (computed over multiple runs) to the results section and reference them in the abstract where space permits. revision: yes

  2. Referee: [Abstract] Abstract and evaluation description: No factuality metric, human verification of event claims, or error analysis on generated captions is mentioned. CIDEr only measures n-gram overlap and cannot confirm that timing/location/participant facts supplied by the SigLIP-retrieved articles are accurately integrated rather than hallucinated.

    Authors: We acknowledge that CIDEr measures n-gram overlap and does not directly verify factual correctness of the added event details. Our design relies on the high retrieval mAP (0.979) together with article summarization to condition generation and thereby reduce unsupported claims, but we did not report a dedicated factuality metric or human verification. In the revision we will add an error analysis subsection with qualitative examples, a discussion of potential hallucinations, and a note on this limitation; we will also explore adding a simple factuality check if feasible within the current experimental setup. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical framework with benchmark metrics only

full rationale

The paper describes a multi-stage retrieval-augmented captioning system (SigLIP retrieval, summarization, LoRA-tuned Qwen generation, N-gram refinement) and reports direct empirical scores (mAP 0.979, CIDEr 0.030→0.094) on OpenEvents-V1. No equations, parameter-fitting steps, or self-citations appear in the provided text that could reduce any claimed result to its own inputs by construction. The central claims rest on external benchmark evaluation rather than internal definitions or fitted predictions.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review yields no explicit free parameters, axioms, or invented entities beyond standard machine-learning components such as pre-trained models; no ad-hoc inventions are named.

pith-pipeline@v0.9.1-grok · 5678 in / 1153 out tokens · 46692 ms · 2026-06-27T02:22:24.498609+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

29 extracted references · 3 linked inside Pith

  1. [1]

    Banerjee, S., Lavie, A.: METEOR: An automatic metric for MT evaluation with improved correlation with human judgments. pp. 65–72. ACL, Ann Arbor, Michi- gan (Jun 2005)

    2005

  2. [2]

    CoRRabs/1912.08226(2019)

    Cornia, M., Stefanini, M., Baraldi, L., Cucchiara, R.: M2: Meshed-memory trans- former for image captioning. CoRRabs/1912.08226(2019)

    arXiv 1912

  3. [3]

    Faghri, F., Fleet, D.J., Kiros, J.R., Fidler, S.: Vse++: Improving visual-semantic embeddings with hard negatives (2018)

    2018

  4. [4]

    In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T

    Gong, Y., Wang, L., Hodosh, M., Hockenmaier, J., Lazebnik, S.: Improving image- sentence embeddings using large weakly annotated photo collections. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) Computer Vision – ECCV 2014. pp. 529–545. Springer International Publishing, Cham (2014)

    2014

  5. [5]

    Hessel, J., Holtzman, A., Forbes, M., Bras, R.L., Choi, Y.: Clipscore: A reference- free evaluation metric for image captioning (2022)

    2022

  6. [6]

    CoRRabs/1810.04020(2018)

    Hossain, M.Z., Sohel, F., Shiratuddin, M.F., Laga, H.: A comprehensive survey of deep learning for image captioning. CoRRabs/1810.04020(2018)

    Pith/arXiv arXiv 2018

  7. [7]

    ICLR1(2), 3 (2022)

    Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W., et al.: Lora: Low-rank adaptation of large language models. ICLR1(2), 3 (2022)

    2022

  8. [8]

    Jia, C., Yang, Y., Xia, Y., Chen, Y.T., Parekh, Z., Pham, H., Le, Q.V., Sung, Y., Li, Z., Duerig, T.: Scaling up visual and vision-language representation learning with noisy text supervision (2021)

    2021

  9. [9]

    In: Yarowsky, D., Baldwin, T., Korhonen, A., Livescu, K., Bethard, S

    Kalchbrenner, N., Blunsom, P.: Recurrent continuous translation models. In: Yarowsky, D., Baldwin, T., Korhonen, A., Livescu, K., Bethard, S. (eds.) Proceed- ings of the 2013 Conference on Empirical Methods in Natural Language Processing. pp. 1700–1709. ACL, Seattle, Washington, USA (Oct 2013)

    2013

  10. [10]

    Karpukhin, V., O˘ guz, B., Min, S., Lewis, P., Wu, L., Edunov, S., Chen, D., tau Yih, W.: Dense passage retrieval for open-domain question answering (2020)

    2020

  11. [11]

    Lee,K.H.,Chen,X.,Hua,G.,Hu,H.,He,X.:Stackedcrossattentionforimage-text matching (2018) CIAN: Multi-Stage Framework for Event-Enriched Image Captioning 11

    2018

  12. [12]

    arXiv preprint arXiv:1910.13461 (2019)

    Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., Zettlemoyer, L.: Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. arXiv preprint arXiv:1910.13461 (2019)

    Pith/arXiv arXiv 1910

  13. [13]

    Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., K¨ uttler, H., Lewis, M., tau Yih, W., Rockt¨ aschel, T., Riedel, S., Kiela, D.: Retrieval-augmented generation for knowledge-intensive nlp tasks (2021)

    2021

  14. [14]

    Li, J., Li, D., Xiong, C., Hoi, S.: Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation (2022)

    2022

  15. [15]

    IEEE/CAA Journal of Automatica Sinica9(8), 1339–1365 (2022)

    Ming, Y., Hu, N., Fan, C., Feng, F., Zhou, J., Yu, H.: Visuals to text: A compre- hensive review on automatic image captioning. IEEE/CAA Journal of Automatica Sinica9(8), 1339–1365 (2022)

    2022

  16. [16]

    In: ACM International Conference on Multimedia (2025)

    Nguyen, H., Nguyen, P.T., Tran, T.P., Nguyen, M.Q., Nguyen, T.V., Tran, M.T., Le, T.N.: Openevents v1: Large-scale benchmark dataset for multimodal event grounding. In: ACM International Conference on Multimedia (2025)

    2025

  17. [17]

    In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K

    Ordonez, V., Kulkarni, G., Berg, T.: Im2text: Describing images using 1 million captioned photographs. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K. (eds.) Advances in Neural Information Processing Systems. vol. 24. Curran Associates, Inc. (2011)

    2011

  18. [18]

    Papineni, K., Roukos, S., Ward, T., Zhu, W.J.: Bleu: a method for automatic evaluation of machine translation. pp. 311–318. Association for Computational Linguistics, Philadelphia, Pennsylvania, USA (Jul 2002)

    2002

  19. [19]

    Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., Sutskever, I.: Learning transferable visual models from natural language supervision (2021)

    2021

  20. [20]

    Sarto, S., Cornia, M., Cucchiara, R.: Image captioning evaluation in the age of multimodal llms: Challenges and future perspectives (2025)

    2025

  21. [21]

    Stefanini, M., Cornia, M., Baraldi, L., Cascianelli, S., Fiameni, G., Cucchiara, R.: From show to tell: A survey on deep learning-based image captioning (2021)

    2021

  22. [22]

    Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks (2014)

    2014

  23. [23]

    In: ACM International Conference on Multimedia (2025)

    Tran, T.P., Nguyen, M.Q., Tran, M.T., Nguyen, T.V., Do, T.L., Ly, D.N., Huynh, V.T., Le, K.D., Tran, M.K., Le, T.N.: Event-enriched image analysis grand chal- lenge at acm multimedia 2025. In: ACM International Conference on Multimedia (2025)

    2025

  24. [24]

    In: CVPR (June 2015)

    Vedantam, R., Lawrence Zitnick, C., Parikh, D.: Cider: Consensus-based image description evaluation. In: CVPR (June 2015)

    2015

  25. [25]

    Vedantam, R., Zitnick, C.L., Parikh, D.: Cider: Consensus-based image description evaluation (2015)

    2015

  26. [26]

    arXiv preprint arXiv:2409.12191 (2024)

    Wang, P., Bai, S., Tan, S., Wang, S., Fan, Z., Bai, J., Chen, K., Liu, X., Wang, J., Ge, W., Fan, Y., Dang, K., Du, M., Ren, X., Men, R., Liu, D., Zhou, C., Zhou, J., Lin, J.: Qwen2-vl: Enhancing vision-language model’s perception of the world at any resolution. arXiv preprint arXiv:2409.12191 (2024)

    Pith/arXiv arXiv 2024

  27. [27]

    National Science Review11(12) (Nov 2024)

    Yin, S., Fu, C., Zhao, S., Li, K., Sun, X., Xu, T., Chen, E.: A survey on multimodal large language models. National Science Review11(12) (Nov 2024)

    2024

  28. [28]

    Zhai, X., Mustafa, B., Kolesnikov, A., Beyer, L.: Sigmoid loss for language image pre-training (2023)

    2023

  29. [29]

    In: Proceedings of the 62nd Annual Meeting of the ACL (Volume 3: System Demonstrations)

    Zheng, Y., Zhang, R., Zhang, J., Ye, Y., Luo, Z., Feng, Z., Ma, Y.: Llamafactory: Unified efficient fine-tuning of 100+ language models. In: Proceedings of the 62nd Annual Meeting of the ACL (Volume 3: System Demonstrations). ACL, Bangkok, Thailand (2024)

    2024