Toward Reliable VLM: A Fine-Grained Benchmark and Framework for Exposure, Bias, and Inference in Korean Street Views

Bo Shao; Hansaem Kim; Xiaonan Wang

REVIEW 4 major objections 5 minor 17 references

Toward Reliable VLM: A Fine-Grained Benchmark and Framework for Exposure, Bias, and Inference in Korean Street Views

T0 review · 4 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read KoreaGEO Bench: input modality — especially a place-naming caption — drives VLM geolocation accuracy and privacy leakage more than model scale or architecture, with prediction errors converging on Seoul.

desk verdict A genuinely new Korean street-view geolocation benchmark, but the headline claim that modality beats scale rests on a leakage confound: high-risk captions contain the answer. read the letter →

arxiv 2506.03371 v1 pith:O6I5IBTK submitted 2025-06-03 cs.CV

Xiaonan Wang , Bo Shao , Hansaem Kim This is my paper

classification cs.CV

keywords KoreaGEOBenchgeolocationbenchmarkKoreanstreetviewsvision-languagemodelslocationprivacymultimodalevaluationspatialbiasexposure

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

KoreaGEO Bench is a new benchmark for asking whether vision-language models can pinpoint where a Korean street photo was taken, and what leaks that ability. The paper collects 1,080 street-view images across four urban clusters and nine place types, attaches two styles of Korean social-media captions, and runs ten models through image-only, caption-without-place-name, and caption-with-place-name conditions. Its central claim is that input modality decides geolocation accuracy and privacy exposure more than model scale or architecture: once a caption names the place, even small or open-source models localize precisely, while images alone are weak and vague captions can mislead. The authors also document a structural spatial bias: mispredictions flow disproportionately into Seoul, so accuracy declines along the urban hierarchy. If the claim is right, location-privacy defense should center on text redaction, and fine-grained benchmarks are needed to expose spatial bias.

What carries the argument

The load-bearing object is the KoreaGEO Bench dataset itself: 1,080 Google Street View images, sampled from four KMeans-derived urban clusters and nine place types, with eleven manually annotated contextual dimensions and two Korean caption styles — functional captions that suppress place names and high-risk captions that include them. The evaluation protocol runs each image through three paths — image-only, image plus functional caption, and image plus high-risk caption — and scores predictions by Haversine distance at 0.1 km, 1 km, 20 km, and 100 km thresholds. The mechanism behind the central result is the contrast between these paths: a caption containing a place name supplies the model with explicit location tokens, overcoming visual ambiguity, while a vague caption without proper nouns can mislead the model into matching irrelevant cues.

What would settle it

Re-run the three-path protocol on freshly fetched Street View images from the released coordinates with independently regenerated captions; if the high-risk-caption lift at 1 km does not reproduce at roughly the reported magnitudes (o3: 9.91% to 46.48%; Gemini: 15.93% to 56.48%), the modality-dominance claim fails.

Watch

Extended reading notes

Core claim

The paper's discovery is a modality-driven shift in localization precision. On image-only input, the best model reaches 3.52% at 100 m and 15.93% at 1 km (Gemini 2.5 Pro), and open-source models are near zero at fine scales. Functional captions — natural Korean captions without proper place names — do not reliably help and sometimes reduce accuracy. High-risk captions that name the place change the picture: the three leading closed models jump to roughly 46–56% at 1 km and above 95% at 20 km, and open-source LLaMA reaches 95% at 100 km while Qwen reaches 67% at 20 km. Because the same pattern appears across architectures and scales, the paper concludes that modality sensitivity matters more than model size or provider. A second finding is spatial: accuracy is highest in the Seoul cluster, confusion matrices show errors from growth hubs and satellites flowing into Seoul, and city-level mistakes converge on Seoul.

Load-bearing premise

The benchmark's validity depends on the retained 1,080 images being a representative sample of Korean street scenes after the 7,200-to-1,080 filter, and on the released coordinates allowing anyone to recover exactly the same Street View images.

Editorial extensions

If this is right

Location-bearing captions are a concrete geolocation vulnerability: at the 1 km threshold, high-risk captions lift leading models to roughly 46–56% accuracy, compared with under 16% for image-only input.
Privacy protections that strip EXIF metadata or blur faces are insufficient; text that names a place is itself a high-precision leak channel.
Coarse country- or city-level scores hide the reliability gap: at 100 m most models are near zero, and only landmarks and a few functional zones are reliably identifiable.
Model rankings depend on input modality, so an image-only leaderboard does not predict privacy risk once social-media captions are present.
Spatial-bias reporting, not just aggregate accuracy, is needed because errors systematically converge on the capital cluster.

Reading between the lines

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

Editorial extension: the modality-dominance result suggests current VLM geolocation is partly text-reading; cropping or masking storefront text and signs in the images should lower image-only accuracy while leaving caption-driven accuracy unchanged, a direct test of that reading.
Editorial extension: Seoul-convergence bias may be a general property of monocentric countries; replicating the protocol in another single-dominant-city country would show whether the centralization pattern is structural rather than Korean-specific.
Editorial extension: because the release contains coordinates rather than images, scores have a shelf life — Street View imagery updates over time, so future re-runs may measure different scenes; pinning image hashes or capture timestamps would make the benchmark repeatable.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Quick take: the resource is real and worth engaging; the central interpretive claim is not supported as stated. KoreaGEO Bench is a first: a fine-grained, single-country multimodal geolocation benchmark with 1,080 Korean street-view images stratified over four socio-spatial clusters and nine place types, plus two caption styles and rich context annotations. That fills a genuine gap. The sampling design (GRDP/density clustering, place-type taxonomy, contextual labels) is thoughtful, and the Seoul-centric bias analysis via confusion matrices is a useful diagnostic. Running ten VLMs across three input paths is a reasonable evaluation protocol. I would not desk-reject this.

The big soft spot is the 'input modality beats model scale/architecture' conclusion. In the high-risk caption condition, the generation prompt (Figure 13) explicitly requires the place name to appear in the caption. So the model is handed the answer in text; the image is almost superfluous. That condition is answer leakage, not multimodal inference. The appropriate contrast is the functional-caption path, where proper nouns are banned, and there the gains over image-only are modest and inconsistent across models. The paper does not report a text-only baseline for high-risk captions, so it cannot separate leakage from genuine visual-text integration. The privacy-risk observation itself—captions revealing place names make localization dramatically easier—is plausible and valuable, but that is a statement about text leakage, not about modality dominating scale.

Other soft spots, in descending order: the filtering from 7,200 to 1,080 images is described only as 'low-quality or semantically irrelevant' (Section 3.4), so representativeness is not established; no raw images are released and the exact API parameters (heading, pitch, date, zoom) are missing, so exact reproducibility is not currently possible; accuracy numbers come without confidence intervals or repeated-run variance, which matters for small per-cell samples. The human caption evaluation covers only 120 captions, but that is a peripheral validation issue, not a load-bearing flaw. The limitations and ethics statements are honest and should be taken at face value.

Net: cite it as a dataset/resource, but not for the 'modality beats scale' claim until the authors add a no-leakage control or a text-only high-risk baseline and release code. It deserves a serious referee. I would send it out with a request for major revision.

Referee Report

4 major / 5 minor

Summary. The paper introduces KoreaGEO Bench, a Korean street-view geolocation benchmark of 1,080 images sampled across four socio-spatial clusters and nine place types, with manual contextual annotations and two styles of Korean social-media-style captions (functional and high-risk). Ten vision-language models are evaluated under three input conditions—image only, image with functional caption, and image with high-risk caption—using distance-threshold accuracy, cluster/city confusion matrices, and reasoning-output analysis. The authors report that input modality has a greater effect on localization performance and privacy exposure than model scale or architecture, and that predictions are biased toward Seoul and the core cluster.

Significance. If the central claims were supported, KoreaGEO Bench would fill a clear gap: existing geolocation benchmarks are coarse-grained, English-centric, and rarely consider multimodal social-media captions or fine-grained intra-country bias. The dataset construction is thoughtful in its cluster stratification, place-type taxonomy, and contextual annotation, and the three-path evaluation protocol is a useful template for studying privacy leakage. The bias analysis via confusion matrices is a concrete contribution. However, the paper's headline claim about modality dominance rests on an experimental condition that leaks the ground-truth location through the caption, and the dataset filtering procedure is under-specified. These issues are fixable, but they currently prevent the results from supporting the stated conclusion.

major comments (4)

[§5.1.1, Fig. 13, Table 2] The central claim that input modality has a greater influence on localization and privacy exposure than model scale or architecture is not supported by the experimental design. The high-risk caption prompt (Figure 13) explicitly instructs the generator to include the place name or address in the caption, so the VLM receives the ground-truth location as text; the 'Visual with High-risk Caption' condition therefore measures text-based geocoding of a revealed proper noun rather than multimodal inference at constant information content. No text-only baseline (high-risk caption without the image) is reported, so the large accuracy jumps in Table 2 (e.g., o3 from 9.91% to 46.48% at 1km and Gemini from 15.93% to 56.48% at 1km) cannot be attributed to the visual modality or to visual-textual fusion. The functional-caption condition, which removes proper nouns, shows only modest and inconsistent gains—for instance, GPT-4o's 1km accuracy drops from 7.59% to 5.83%—which is consistent with answer leakage rather than a general modality advantage. The authors should add a text-only high-risk-caption baseline and/or a fourth condition in which proper nouns are redacted from high-risk captions, and should re-state the conclusion in terms of privacy leakage from explicit textual cues rather than 'input modality'.
[§3.4] The filtering from 7,200 collected images to 1,080 retained samples is described only as removing 'low-quality or semantically irrelevant samples' and applying 'the spatial and contextual coverage strategy,' with no operational definition of these criteria. If the filtering is non-random, the resulting benchmark may not represent the population of Korean street scenes, which undermines the generalizability of both the accuracy figures and the spatial-bias analysis. The authors should specify the exclusion criteria, report exclusion counts by cluster and place type, provide inter-annotator agreement for the quality filtering, and document the coverage-matching algorithm used to retain 30 images per place type per cluster.
[§3.2, Ethics Statement] Because the released dataset contains only coordinates and the paper states that no raw images are stored, the benchmark's reproducibility depends on the Google Street View API returning the same imagery at those coordinates at evaluation time. Street View imagery is updated over time, so the exact images used for the reported results may not be recoverable. The authors should either release image hashes or thumbnails if the terms of use permit, document capture dates, or provide an archiving mechanism; otherwise the benchmark should be treated as a dynamic rather than static resource, and the reported numbers may not be exactly reproducible.
[§5.1.1, Table 2] The claim that modality is more influential than model scale or architecture is a comparative ranking claim, but the paper reports no statistical significance tests, confidence intervals, or repeated runs. With 1,080 images, differences of a few percentage points at the coarser thresholds (e.g., 65.00% vs. 63.52% at 100km for GPT-4o vs. o3 under image-only input) may be within sampling error. The 'greater influence' conclusion should be supported by paired significance tests or effect-size estimates across the ten models.

minor comments (5)

[Appendix A.1] The phrase 'we employe both the elbow method' contains a typo; it should read 'we employ both the elbow method.'
[§1] The claim that KoreaGEO Bench is 'the first fine-grained and multimodal geolocation benchmark specifically designed for a single country' should be qualified, because existing city-level benchmarks such as SF-XL already provide fine-grained single-city evaluation; the claim should be narrowed to Korea or to the specific combination of fine-grained granularity, multimodal captions, and a single-country scope.
[§5.3.2] The modality-attribution analysis uses Gemini as an external judge; while the self-preference mitigation is cited, the analysis would be more robust with a second judge or with human-annotated modality labels on a sample of outputs.
[§3.3] The model name 'GPT-o3' appears in Section 3.3 but the model is elsewhere called 'o3'; the naming should be made consistent throughout the paper.
[Fig. 9] The modality attribution is reported only for the functional-caption setting; reporting the analogous attribution for high-risk captions would help clarify whether the large accuracy gains are actually driven by visual-textual integration or by text-only location lookup.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the accuracy and bias claims are measured outcomes, and the high-risk caption condition, while confounded with answer leakage, is not a definitional reduction of the paper's derivation.

full rationale

The paper's core results are empirical measurements over model outputs rather than quantities derived from fitted parameters. Accuracy tables, CDFs, confusion matrices, and word clouds are reported outcomes; no parameter is fitted to the data and then renamed as a prediction. The only self-referential elements are that captions are generated from the same place-name/address metadata that defines the ground truth, and that Gemini is used as the modality judge. Neither is load-bearing in a circular sense: the high-risk caption condition is intended to simulate a specific exposure scenario, and the model's ability to exploit an explicit place name is itself the measured phenomenon. The larger concern—that the conclusion 'input modality has a greater influence on localization performance and privacy exposure than model scale or architecture' conflates the presence of the answer in the caption with modality—is a validity or confound threat, not a circularity. The numeric results are not constructed from the paper's inputs via a definitional identity, and no self-citation chain or imported uniqueness theorem is load-bearing. Accordingly, no circular step meets the evidentiary bar. The high-risk caption leakage should be addressed with a text-only baseline, but that is a correctness risk, not a circularity.

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

The benchmark's central claims rest on sampling and filtering choices (K=4, 30 images per cell, undefined quality filter) and on domain assumptions about street-view coverage and caption authenticity. No new physical entities are introduced.

free parameters (4)

Number of socio-spatial clusters (K) = 4
Chosen via elbow method and silhouette score on population density and GRDP features; not fitted to the evaluation results, but it shapes all downstream sampling.
Caption cosine similarity threshold = 0.85
Cosine similarity threshold for regenerating captions to ensure diversity; introduced ad hoc in Section 3.3.
Images per cluster-place-type cell = 30
Number of retained images per cell after filtering; a design choice with no formal justification.
Distance accuracy thresholds = 0.1, 1, 20, 100 km
Accuracy thresholds defining precision tiers; standard in geolocation literature but chosen by the authors.

assumptions (5)

standard math Haversine distance formula
Used to compute spherical distance between predicted and true coordinates (Eq. 2-3).
standard math KMeans clustering on density and GRDP
Applied to population density and GRDP feature vectors; assumes Euclidean distance in feature space is meaningful.
domain assumption Google Street View coverage is representative
Assumes the API returns images representative of Korean street scenes; acknowledged as limited in the Limitations section.
domain assumption Generated captions approximate real social media posts
Supported only by a 120-caption human evaluation (63.3% fooling rate), extrapolated to all 2,160 captions.
domain assumption Gemini can reliably attribute which input modality drove each prediction
Used as external judge in Section 5.3.2; mitigation for self-preference is partial.

how reviews work

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Cite this review

Pith. "Pith review of Toward Reliable VLM: A Fine-Grained Benchmark and Framework for Exposure, Bias, and Inference in Korean Street Views." pith.science (2026). https://pith.science/paper/O6I5IBTK

@misc{pith2026250603371,
  author       = {Pith},
  title        = {Pith review of: Toward Reliable VLM: A Fine-Grained Benchmark and Framework for Exposure, Bias, and Inference in Korean Street Views},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/O6I5IBTK}},
  note         = {Machine review of arXiv:2506.03371}
}

read the original abstract

Recent advances in vision-language models (VLMs) have enabled accurate image-based geolocation, raising serious concerns about location privacy risks in everyday social media posts. However, current benchmarks remain coarse-grained, linguistically biased, and lack multimodal and privacy-aware evaluations. To address these gaps, we present KoreaGEO Bench, the first fine-grained, multimodal geolocation benchmark for Korean street views. Our dataset comprises 1,080 high-resolution images sampled across four urban clusters and nine place types, enriched with multi-contextual annotations and two styles of Korean captions simulating real-world privacy exposure. We introduce a three-path evaluation protocol to assess ten mainstream VLMs under varying input modalities and analyze their accuracy, spatial bias, and reasoning behavior. Results reveal modality-driven shifts in localization precision and highlight structural prediction biases toward core cities.

Figures

Figures reproduced from arXiv: 2506.03371 by the authors.

**Figure 2.** An example entry from KoreaGEO Bench (English translations are provided when the original text is in Korean.). the type of text displayed (e.g., building names or traffic signs), and the language in which the text appears. To better capture regional variation in Korea’ s visual styles, we incorporate a semantic classification of cultural styles, distinguishing between moderngeneric architecture, Korean traditional… view at source ↗

**Figure 3.** Geographical Distribution of Sampled Coor [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

**Figure 4.** Cumulative Distribution Function (CDF) of geolocation error across input modalities. Vertical dashed lines denote distance thresholds (0.1 km, 1 km, 20 km, 100 km). 005). For open-source baselines, we evaluate two state-of-the-art multimodal models: LLaMA-3.2- 90B-Vision-Instruct-Turbo and Qwen2-VL-72BInstruct. All model versions used in this study correspond to their official releases as of April 2025. Metrics (1… view at source ↗

Figures from the paper (10 more)

**Figure 5.** Figure 5: Accuracy distribution across different place types under four distance thresholds. Radar plots illustrate [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]

**Figure 6.** Figure 6: Confusion matrix between real and predicted [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]

**Figure 8.** Figure 8: Word cloud of model inferences under the [PITH_FULL_IMAGE:figures/full_fig_p008_8.png]

**Figure 9.** Figure 9: Modality attribution by Gemini under the Vi [PITH_FULL_IMAGE:figures/full_fig_p008_9.png]

**Figure 10.** Figure 10: Elbow method [PITH_FULL_IMAGE:figures/full_fig_p012_10.png]

**Figure 11.** Figure 11: Scatterplot of Korean cities clustered by pop [PITH_FULL_IMAGE:figures/full_fig_p013_11.png]

**Figure 12.** Figure 12: Prompt for generating functional captions [PITH_FULL_IMAGE:figures/full_fig_p016_12.png]

**Figure 14.** Figure 14: Prompt used for the Image Only input set [PITH_FULL_IMAGE:figures/full_fig_p017_14.png]

**Figure 15.** Figure 15: Prompt used for the Image + Functional Cap [PITH_FULL_IMAGE:figures/full_fig_p017_15.png]

**Figure 16.** Figure 16: Prompt used for the Image + High-risk Cap [PITH_FULL_IMAGE:figures/full_fig_p017_16.png]

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Works this paper leans on

17 extracted references · 11 canonical work pages

[1]

감정 표현은 랜덤: 기쁨, 슬픔, 우울, 무료함, 분노, 짜증, 신남, 후회, 놀람 등 중 하나 (Emotion must be randomly selected from: joy, sadness, melancholy, boredom, anger, irritation, excitement,regret,surprise,etc. )

work page
[2]

문장 유형은 랜덤 : 감탄형, 반문형, 묘사형, 감정형, 단문형 중 하나 선택 (Sentence type must be randomly selected from: exclamatory, rhetorical, descriptive, emotional, or minimal.)

work page
[3]

감정이나 구체적 사물 표현 1개 이상 포함 (Include at least one expression of emotion or one specificobject. )

work page
[4]

No propernounsallowedinhashtags

이 모 지 1 개 이 상, 해 시 태 그 1~3 개 포 함 (고유지명 사용 금지) (Include at least one emoji and 1–3 hashtags. No propernounsallowedinhashtags. )

work page
[5]

In Computer Vision – ECCV 2016: 14th European Conference , pages 37–55, Amster- dam, The Netherlands

Planet-photogeolocationwithconvolutional neuralnetworks. In Computer Vision – ECCV 2016: 14th European Conference , pages 37–55, Amster- dam, The Netherlands. Springer International Pub- lishing. Kyle Wiggers. 2025. The latest viral chatgpt trend is doing ‘reverse location search ’ from photos. TechCrunchnewsarticle,accessed2025-05-11. Scott Workman, Rich...

work page 2016
[6]

아래 장소 정보 및 이미지를 참고해서 장소 분위기 유추 (Use the metadata and image below to infer the moodofthelocation. )

work page
[7]

강남”, “홍대

Judging llm-as-a-judge with mt-bench and chatbot arena. In Advances in Neural Informa- tion Processing Systems, volume 36, pages 46595– 46623. Z. Zheng, Y. Wei, and Y. Yang. 2020.University- 1652: A multi-view multisource benchmark for drone-basedgeo-localization. InProceedings of the 28th ACM International Conference on Multimedia , pages1395–1403,Seattl...

arXiv 2020
[12]

Avoid for- mal/literary/repetitive styles and aim for maximum variation.)

자연스러운 SNS 말투: 문어체 /문학체/반복적 문체 금지, 최대한 다양하게 작성할 것 (Must resemble natural SNS language. Avoid for- mal/literary/repetitive styles and aim for maximum variation.)

work page

Show all 17 references

[14]

스트리트 뷰 이미지도 함께 참고해서 작성할 것 (Also consider the street view image when compos- ingthecaption. ) 참고 장소 정보 (Location Metadata): 지명: row[’Place Name’] 주소: row[’Formatted Address’] 실내/실외: row[’indoor_outdoor’] 시간대: row[’time_of_day’] 계절: row[’season’] 날씨: row[’weather’] 장소 유형: row[’P...
[15]

Address: - Infer the full address based on the visual elements intheimage,including: -Evenifnoexplicitsignsarevisible,makeareason- ableguessusinganyavailablevisualclues
[16]

Coordinates: -Providetheinferredlatitudeandlongitudeofthelo- cation,asaccuratelyaspossible
[17]

Address",

Inference: - Explain in detail how you inferred the address and coordinatesfromtheimage. -Theexplanationshoulddescribeyourfullreasoning process step by step, based solely on the visual ele- mentsintheimage. Returna single-line JSONobjectintheexactformat below,withoutanyextrate...
[2015]

In Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), pages 3961–3969, USA

Wide-area image geolocalization with aerial reference imagery. In Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), pages 3961–3969, USA. IEEE Computer Society. M. Wu and Q. Huang. 2022. Im2city: image geo- localization via multi-modal learning. ...

2015 arXiv
[2016]

Automatic description generation from im- ages: a survey of models, datasets, and evaluation measures. J. Artif. Int. Res.,55 (1):409–442. G. Berton, C. Masone, and B. Caputo. 2022. Rethink- ing visual geo-localization for large-scale applica- tions. In Proceedings of the IEEE...

2022 arXiv
[2023]

arXiv preprint arXiv:2301.12597

Blip-2: Bootstrapping language-image pre- training with frozen image encoders and large lan- guagemodels. arXiv preprint arXiv:2301.12597. L.Li,Y.Ye,B.Jiang,andW.Zeng.2024. Georeasoner: Geo-localizationwithreasoninginstreetviewsusing a large vision-language model. InProceeding...

2024 arXiv
[2024]

InAdvances in Neural Information Pro- cessing Systems,volume37,pages68772–68802

Llmevaluatorsrecognizeandfavortheirown generations. InAdvances in Neural Information Pro- cessing Systems,volume37,pages68772–68802. Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh,GabrielGoh,SandhiniAgarwal,GirishSas- try, Amanda Askell, Pamela Mishkin, Jack Clark, ...

2021
[2025]

arXiv preprint arXiv:2501.15558

Ocean-ocr: Towards general ocr applica- tion via a vision-language model. arXiv preprint arXiv:2501.15558. Wei Chow, Jiageng Mao, Boyi Li, Daniel Seita, Vitor Guizilini, and YueWang.2025. Physbench: Bench- marking and enhancing vision-language models for physical world underst...

2025 arXiv

Pith tools

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

[1] [1]

감정 표현은 랜덤: 기쁨, 슬픔, 우울, 무료함, 분노, 짜증, 신남, 후회, 놀람 등 중 하나 (Emotion must be randomly selected from: joy, sadness, melancholy, boredom, anger, irritation, excitement,regret,surprise,etc. )

work page

[2] [2]

문장 유형은 랜덤 : 감탄형, 반문형, 묘사형, 감정형, 단문형 중 하나 선택 (Sentence type must be randomly selected from: exclamatory, rhetorical, descriptive, emotional, or minimal.)

work page

[3] [3]

감정이나 구체적 사물 표현 1개 이상 포함 (Include at least one expression of emotion or one specificobject. )

work page

[4] [4]

No propernounsallowedinhashtags

이 모 지 1 개 이 상, 해 시 태 그 1~3 개 포 함 (고유지명 사용 금지) (Include at least one emoji and 1–3 hashtags. No propernounsallowedinhashtags. )

work page

[5] [5]

In Computer Vision – ECCV 2016: 14th European Conference , pages 37–55, Amster- dam, The Netherlands

Planet-photogeolocationwithconvolutional neuralnetworks. In Computer Vision – ECCV 2016: 14th European Conference , pages 37–55, Amster- dam, The Netherlands. Springer International Pub- lishing. Kyle Wiggers. 2025. The latest viral chatgpt trend is doing ‘reverse location search ’ from photos. TechCrunchnewsarticle,accessed2025-05-11. Scott Workman, Rich...

work page 2016

[6] [6]

아래 장소 정보 및 이미지를 참고해서 장소 분위기 유추 (Use the metadata and image below to infer the moodofthelocation. )

work page

[7] [7]

강남”, “홍대

Judging llm-as-a-judge with mt-bench and chatbot arena. In Advances in Neural Informa- tion Processing Systems, volume 36, pages 46595– 46623. Z. Zheng, Y. Wei, and Y. Yang. 2020.University- 1652: A multi-view multisource benchmark for drone-basedgeo-localization. InProceedings of the 28th ACM International Conference on Multimedia , pages1395–1403,Seattl...

arXiv 2020

[8] [12]

Avoid for- mal/literary/repetitive styles and aim for maximum variation.)

자연스러운 SNS 말투: 문어체 /문학체/반복적 문체 금지, 최대한 다양하게 작성할 것 (Must resemble natural SNS language. Avoid for- mal/literary/repetitive styles and aim for maximum variation.)

work page