Paper Citation Record · LEDGER

ProCrop: Learning Aesthetic Image Cropping from Professional Compositions

As of 8 August 2026, this Paper Citation Record lists 59 of 59 outbound references and 3 inbound Pith citation observations for arXiv:2505.22490.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2505.22490 v1

Coverage vector

measured 59 of 59 reference resolution

Typed states for the displayed outbound observations.

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measured 62 of 62 standing notices

One-hop event checks from named stored sources.

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measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-28T02:32:21.923771Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-02T12:06:55.768409Z

Reference resolution

59 of 59 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f3ab1f52-aecb-4621-a95b-a86233945560 · outbound

This paper cites GPT-4 Technical Report.

ProCrop: Learning Aesthetic Image Cropping from Professional Compositions GPT-4 Technical Report

Reference 1

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Observation e9c3ca95-d00c-433a-8f63-c1795a2f1707 · outbound

This paper cites Retrieval-based language models and applications.

ProCrop: Learning Aesthetic Image Cropping from Professional Compositions Retrieval-based language models and applications

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:12.672605Z

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Observation 576e5acf-dcb6-4084-bff3-d7c0a953fbb9 · outbound

This paper cites Retrieval-augmented diffusion models.Advances in Neural Information Processing Sys- tems, 35:15309–15324, 2022.

ProCrop: Learning Aesthetic Image Cropping from Professional Compositions Retrieval-augmented diffusion models.Advances in Neural Information Processing Sys- tems, 35:15309–15324, 2022

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:12.410302Z

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Observation 10293536-1dd1-405b-9665-42749c0651b0 · outbound

This paper cites Improving language models by retriev- ing from trillions of tokens.

ProCrop: Learning Aesthetic Image Cropping from Professional Compositions Improving language models by retriev- ing from trillions of tokens

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:12.159890Z

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Observation 2ceb39e6-8867-4b64-8f70-e6c2045d8385 · outbound

This paper cites Quantitative analysis of automatic image cropping algorithms:a dataset and comparative study.

ProCrop: Learning Aesthetic Image Cropping from Professional Compositions Quantitative analysis of automatic image cropping algorithms:a dataset and comparative study

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:11.912329Z

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Observation 8084572e-38c5-4287-9eb8-886ca320aa5f · outbound

This paper cites Quantitative analysis of automatic image cropping algorithms: A dataset and comparative study.

ProCrop: Learning Aesthetic Image Cropping from Professional Compositions Quantitative analysis of automatic image cropping algorithms: A dataset and comparative study

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:11.685051Z

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Observation cbfd81c7-b767-4860-bdda-ae4e9a6934df · outbound

This paper cites Learning to compose with professional pho- tographs on the web.

ProCrop: Learning Aesthetic Image Cropping from Professional Compositions Learning to compose with professional pho- tographs on the web

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:11.479845Z

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Observation c177a151-dcb9-44b4-8807-2f462d1f4437 · outbound

This paper cites Reproducible scal- ing laws for contrastive language-image learning.

ProCrop: Learning Aesthetic Image Cropping from Professional Compositions Reproducible scal- ing laws for contrastive language-image learning

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:11.168088Z

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Observation 7dd5f6c7-59b5-4277-adb6-fb89a214c917 · outbound

This paper cites Au- tomatic image cropping using visual composition, boundary simplicity and content preservation models.

ProCrop: Learning Aesthetic Image Cropping from Professional Compositions Au- tomatic image cropping using visual composition, boundary simplicity and content preservation models

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:10.968810Z

Source-reported events for the cited work

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Observation 843c1005-f643-4d1c-b045-8c03629da735 · outbound

This paper cites Dream- sim: Learning new dimensions of human visual similarity using synthetic data.Advances in Neural Information Pro- cessing Systems, 36, 2024.

ProCrop: Learning Aesthetic Image Cropping from Professional Compositions Dream- sim: Learning new dimensions of human visual similarity using synthetic data.Advances in Neural Information Pro- cessing Systems, 36, 2024

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:10.756116Z

Source-reported events for the cited work

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Observation 01c32184-ddd7-4fbe-8426-668450d7930c · outbound

This paper cites Automatic image cropping for vi- sual aesthetic enhancement using deep neural networks and cascaded regression.IEEE Transactions on Multimedia, 20 (8):2073–2085, 2018.

ProCrop: Learning Aesthetic Image Cropping from Professional Compositions Automatic image cropping for vi- sual aesthetic enhancement using deep neural networks and cascaded regression.IEEE Transactions on Multimedia, 20 (8):2073–2085, 2018

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:10.498097Z

Source-reported events for the cited work

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Observation 3f470633-709f-499b-b2e6-ee6ec1c3cce6 · outbound

This paper cites Retrieval augmented language model pre- training.

ProCrop: Learning Aesthetic Image Cropping from Professional Compositions Retrieval augmented language model pre- training

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:10.332910Z

Source-reported events for the cited work

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Observation e1c878ad-bdf3-4714-8edb-5f91b038ba34 · outbound

This paper cites Salient-centeredness and saliency size in computational aes- thetics.ACM Transactions on Applied Perception, 20(2):1– 23, 2023.

ProCrop: Learning Aesthetic Image Cropping from Professional Compositions Salient-centeredness and saliency size in computational aes- thetics.ACM Transactions on Applied Perception, 20(2):1– 23, 2023

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:10.029584Z

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Observation 13c45062-21b2-4533-8844-ec832c6eb311 · outbound

This paper cites Composing photos like a photographer.

ProCrop: Learning Aesthetic Image Cropping from Professional Compositions Composing photos like a photographer

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:09.844352Z

Source-reported events for the cited work

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Observation 9a574320-2b28-4a71-ab12-83702dc5e6fe · outbound

This paper cites Learning subject-aware cropping by outpainting professional photos.

ProCrop: Learning Aesthetic Image Cropping from Professional Compositions Learning subject-aware cropping by outpainting professional photos

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:09.629313Z

Source-reported events for the cited work

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Observation 9df0b139-09e7-45e2-a662-8f0b5960ef51 · outbound

This paper cites Retrieval-augmented layout transformer for content-aware layout generation.

ProCrop: Learning Aesthetic Image Cropping from Professional Compositions Retrieval-augmented layout transformer for content-aware layout generation

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:09.431785Z

Source-reported events for the cited work

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Observation 8effb9c8-155d-4506-8fbd-f2211ff9b8fc · outbound

This paper cites Elasticsearch.https://www.elastic.

ProCrop: Learning Aesthetic Image Cropping from Professional Compositions Elasticsearch.https://www.elastic

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:09.139259Z

Source-reported events for the cited work

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Observation 7540461d-2da2-4d16-b6e4-6d77d37b1bea · outbound

This paper cites Re- thinking image cropping: Exploring diverse compositions from global views.

ProCrop: Learning Aesthetic Image Cropping from Professional Compositions Re- thinking image cropping: Exploring diverse compositions from global views

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:08.832265Z

Source-reported events for the cited work

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Observation 73ce191e-5f84-4219-b988-dba76e36c04c · outbound

This paper cites Segment any- thing.

ProCrop: Learning Aesthetic Image Cropping from Professional Compositions Segment any- thing

Reference 19

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Source-reported events for the cited work

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Observation 7500f010-4c9c-4fff-9119-e4541509ff13 · outbound

This paper cites Semantic Line Combination Detector.

ProCrop: Learning Aesthetic Image Cropping from Professional Compositions Semantic Line Combination Detector

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:15:00.512755Z

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Observation a717acd4-d3de-4192-befd-f9da37d91fee · outbound

This paper cites Photographic composition classification and dominant geo- metric element detection for outdoor scenes.Journal of Vi- 9 sual Communication and Image Representation, 55:91–105,.

ProCrop: Learning Aesthetic Image Cropping from Professional Compositions Photographic composition classification and dominant geo- metric element detection for outdoor scenes.Journal of Vi- 9 sual Communication and Image Representation, 55:91–105,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:08.602124Z

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Observation cdb21cbf-f63f-47cb-8d64-259966425163 · outbound

This paper cites A2- rl: Aesthetics aware reinforcement learning for image crop- ping.

ProCrop: Learning Aesthetic Image Cropping from Professional Compositions A2- rl: Aesthetics aware reinforcement learning for image crop- ping

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:08.376183Z

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Observation 9e04a81d-a6f6-4cfb-a2e5-327144461998 · outbound

This paper cites Learning to learn cropping models for different aspect ratio require- ments.

ProCrop: Learning Aesthetic Image Cropping from Professional Compositions Learning to learn cropping models for different aspect ratio require- ments

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:08.095174Z

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Observation 1163613d-5542-4ea5-af5d-ac2beb417500 · outbound

This paper cites Composing good shots by exploiting mutual relations.

ProCrop: Learning Aesthetic Image Cropping from Professional Compositions Composing good shots by exploiting mutual relations

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:07.876422Z

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Observation 8b52bd32-f5fe-46d1-8124-18a7321057e8 · outbound

This paper cites Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation.

ProCrop: Learning Aesthetic Image Cropping from Professional Compositions Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:07.549122Z

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Observation ca3d2737-2631-4885-8aa1-21b1eeca9935 · outbound

This paper cites Context-aware candidates for image cropping.

ProCrop: Learning Aesthetic Image Cropping from Professional Compositions Context-aware candidates for image cropping

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:07.197130Z

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Observation b71353e0-68df-46e0-a7fa-6dc0806e66c3 · outbound

This paper cites Optimizing photo composition.

ProCrop: Learning Aesthetic Image Cropping from Professional Compositions Optimizing photo composition

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:07.026027Z

Source-reported events for the cited work

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Observation fdf9e91b-3267-4747-ba70-0007827b5f01 · outbound

This paper cites Beyond image borders: Learn- ing feature extrapolation for unbounded image composition.

ProCrop: Learning Aesthetic Image Cropping from Professional Compositions Beyond image borders: Learn- ing feature extrapolation for unbounded image composition

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:06.797660Z

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Observation c06ade45-534c-48ac-bd39-74dbe97feb9f · outbound

This paper cites Listwise view ranking for image cropping.IEEE Access, 7: 91904–91911, 2019.

ProCrop: Learning Aesthetic Image Cropping from Professional Compositions Listwise view ranking for image cropping.IEEE Access, 7: 91904–91911, 2019

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:06.557195Z

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Observation d6d2c721-1743-49ef-8c63-b4e49e39d78f · outbound

This paper cites Conditional detr for fast training convergence.

ProCrop: Learning Aesthetic Image Cropping from Professional Compositions Conditional detr for fast training convergence

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:06.426604Z

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Observation 76a7f25d-4ad6-4b36-a36a-cbdde024004f · outbound

This paper cites Ava: A large-scale database for aesthetic visual analysis.

ProCrop: Learning Aesthetic Image Cropping from Professional Compositions Ava: A large-scale database for aesthetic visual analysis

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:06.183197Z

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Observation 1f19baff-a022-49f6-aa85-3d39c149e1b5 · outbound

This paper cites Learning to photograph: A compositional perspective.IEEE Transactions on Multime- dia, 15(5):1138–1151, 2013.

ProCrop: Learning Aesthetic Image Cropping from Professional Compositions Learning to photograph: A compositional perspective.IEEE Transactions on Multime- dia, 15(5):1138–1151, 2013

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:05.921381Z

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Observation b4febbeb-0f67-4120-b119-a74e0b5c9d8e · outbound

This paper cites Sensation-based photo cropping.

ProCrop: Learning Aesthetic Image Cropping from Professional Compositions Sensation-based photo cropping

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:05.725029Z

Source-reported events for the cited work

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Observation a3a8b333-c40a-4e6c-a6d4-842b6bed3942 · outbound

This paper cites The role of image composition in image aesthetics.

ProCrop: Learning Aesthetic Image Cropping from Professional Compositions The role of image composition in image aesthetics

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:05.452550Z

Source-reported events for the cited work

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Observation 4f1d89ef-921c-4c77-be51-a9c34eff7eee · outbound

This paper cites Transview: Inside, outside, and across the cropping view boundaries.

ProCrop: Learning Aesthetic Image Cropping from Professional Compositions Transview: Inside, outside, and across the cropping view boundaries

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:05.272400Z

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Observation ce2775f7-a2d4-458a-9d05-e6305ea14217 · outbound

This paper cites Retrieve-then-adapt: Example-based automatic generation for proportion-related infographics.IEEE Transactions on Visualization and Com- puter Graphics, 27(2):443–452, 2020.

ProCrop: Learning Aesthetic Image Cropping from Professional Compositions Retrieve-then-adapt: Example-based automatic generation for proportion-related infographics.IEEE Transactions on Visualization and Com- puter Graphics, 27(2):443–452, 2020

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:05.072487Z

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Observation f12bfa83-570f-4a0d-980f-19bf7652fca6 · outbound

This paper cites Highly accurate dichotomous im- age segmentation.

ProCrop: Learning Aesthetic Image Cropping from Professional Compositions Highly accurate dichotomous im- age segmentation

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:04.822442Z

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Observation b2fabbe7-dad8-4072-8dc2-43628f856a9c · outbound

This paper cites Learning transferable visual models from natural language supervi- sion.

ProCrop: Learning Aesthetic Image Cropping from Professional Compositions Learning transferable visual models from natural language supervi- sion

Reference 38

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Source-reported events for the cited work

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Observation bdd36073-649c-4005-8d3e-9836e4bb8505 · outbound

This paper cites A comparative study of image retargeting.

ProCrop: Learning Aesthetic Image Cropping from Professional Compositions A comparative study of image retargeting

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:04.674532Z

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Observation abc70819-6e07-49da-b368-c801702ab0cb · outbound

This paper cites Automatic image retargeting.

ProCrop: Learning Aesthetic Image Cropping from Professional Compositions Automatic image retargeting

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:04.439209Z

Source-reported events for the cited work

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Observation d8614fd5-cd9c-47bb-9369-8929cd463f7a · outbound

This paper cites Spatial-semantic collaborative cropping for user generated content.

ProCrop: Learning Aesthetic Image Cropping from Professional Compositions Spatial-semantic collaborative cropping for user generated content

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:04.259498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b8c67e44-f72a-4c48-9653-a6c908fa89dd · outbound

This paper cites Image cropping with composition and saliency aware aes- thetic score map.

ProCrop: Learning Aesthetic Image Cropping from Professional Compositions Image cropping with composition and saliency aware aes- thetic score map

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:04.050851Z

Source-reported events for the cited work

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Observation df956248-1346-4360-b65c-0e0fa2d015dc · outbound

This paper cites Unsplash-lite dataset.https://unsplash.

ProCrop: Learning Aesthetic Image Cropping from Professional Compositions Unsplash-lite dataset.https://unsplash

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:03.780635Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7acd25d8-6ff8-496e-b061-33ccaffeae9e · outbound

This paper cites Large-scale optimization of hierarchical features for saliency prediction in natural images.

ProCrop: Learning Aesthetic Image Cropping from Professional Compositions Large-scale optimization of hierarchical features for saliency prediction in natural images

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:03.525284Z

Source-reported events for the cited work

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Observation 60d97e3e-0cb8-4862-b520-271e902bb197 · outbound

This paper cites PosSAM: Panoptic Open-vocabulary Segment Anything.

ProCrop: Learning Aesthetic Image Cropping from Professional Compositions PosSAM: Panoptic Open-vocabulary Segment Anything

Reference 45

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Source-reported events for the cited work

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Observation 333f2d50-ea94-4f12-97e3-1ef038209775 · outbound

This paper cites Image cropping with spatial-aware feature and rank consistency.

ProCrop: Learning Aesthetic Image Cropping from Professional Compositions Image cropping with spatial-aware feature and rank consistency

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:03.196529Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8cb32950-0e1c-4dc7-ae22-f7dd28298800 · outbound

This paper cites Deep cropping via at- tention box prediction and aesthetics assessment.

ProCrop: Learning Aesthetic Image Cropping from Professional Compositions Deep cropping via at- tention box prediction and aesthetics assessment

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:02.838731Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a77b103e-605c-4f43-a545-d7346827e2f3 · outbound

This paper cites Good 10 view hunting: Learning photo composition from dense view pairs.

ProCrop: Learning Aesthetic Image Cropping from Professional Compositions Good 10 view hunting: Learning photo composition from dense view pairs

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:02.363562Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e42666cd-7558-42de-9778-d1cb13eeb807 · outbound

This paper cites Learning the change for automatic image cropping.

ProCrop: Learning Aesthetic Image Cropping from Professional Compositions Learning the change for automatic image cropping

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:02.041245Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 561f9cd5-51e8-4df5-af6a-2ca09f0e177d · outbound

This paper cites Focusing on your subject: Deep subject-aware image composition recommendation net- works.Computational Visual Media, 9(1):87–107, 2023.

ProCrop: Learning Aesthetic Image Cropping from Professional Compositions Focusing on your subject: Deep subject-aware image composition recommendation net- works.Computational Visual Media, 9(1):87–107, 2023

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:01.706028Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation fda477e4-c57d-4025-b261-e4ba17fc0dfb · outbound

This paper cites Reliable and efficient image cropping: A grid anchor based approach.

ProCrop: Learning Aesthetic Image Cropping from Professional Compositions Reliable and efficient image cropping: A grid anchor based approach

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:01.393343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ea3f9d0f-cf1c-4c8a-bed3-75e42a78aede · outbound

This paper cites Grid anchor based image cropping: A new benchmark and an efficient model.IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(3):1304–1319, 2020.

ProCrop: Learning Aesthetic Image Cropping from Professional Compositions Grid anchor based image cropping: A new benchmark and an efficient model.IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(3):1304–1319, 2020

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:01.370630Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c11172d9-d03b-4304-bb81-e81c92738094 · outbound

This paper cites Human- centric image cropping with partition-aware and content- preserving features.

ProCrop: Learning Aesthetic Image Cropping from Professional Compositions Human- centric image cropping with partition-aware and content- preserving features

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:01.242212Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3a9493ac-e936-41bd-8f3a-236dad9c2b7c · outbound

This paper cites Detecting and removing visual distractors for video aesthetic enhancement.IEEE Transactions on Multimedia, 20(8):1987–1999, 2018.

ProCrop: Learning Aesthetic Image Cropping from Professional Compositions Detecting and removing visual distractors for video aesthetic enhancement.IEEE Transactions on Multimedia, 20(8):1987–1999, 2018

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:01.016412Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation df35f90e-c014-4900-aa1e-b1fab8a3e982 · outbound

This paper cites Weakly supervised photo cropping.IEEE Transactions on Multimedia, 16(1):94–107, 2013.

ProCrop: Learning Aesthetic Image Cropping from Professional Compositions Weakly supervised photo cropping.IEEE Transactions on Multimedia, 16(1):94–107, 2013

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:00.876484Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4cd47429-1385-40b9-b388-ae682f081b96 · outbound

This paper cites Adding conditional control to text-to-image diffusion models.

ProCrop: Learning Aesthetic Image Cropping from Professional Compositions Adding conditional control to text-to-image diffusion models

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-07T13:15:00.040344Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e28f97e8-5798-48d6-a815-b0d76e397d36 · outbound

This paper cites Auto cropping for digital photographs.

ProCrop: Learning Aesthetic Image Cropping from Professional Compositions Auto cropping for digital photographs

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:00.714139Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b079594c-aafa-4952-988e-6e54ab1421a3 · outbound

This paper cites The unreasonable effectiveness of deep features as a perceptual metric.

ProCrop: Learning Aesthetic Image Cropping from Professional Compositions The unreasonable effectiveness of deep features as a perceptual metric

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-07T13:15:00.195655Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c42476c4-7d2f-4536-806f-e97375c02e43 · outbound

This paper cites Composition-aware Graphic Layout GAN for Visual-textual Presentation Designs.

ProCrop: Learning Aesthetic Image Cropping from Professional Compositions Composition-aware Graphic Layout GAN for Visual-textual Presentation Designs

Reference 59

Resolution: malformed identifier; no resolver link, observed 2026-08-07T13:15:00.289939Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 41b5b83e-c2c0-417c-938d-d45be9c76c0a · inbound

PhotoFramer: Multi-modal Image Composition Instruction cites this paper.

PhotoFramer: Multi-modal Image Composition Instruction ProCrop: Learning Aesthetic Image Cropping from Professional Compositions

Reference 73

Resolution: verified exact; arxiv_id, observed 2026-05-17T02:48:54.168331Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 87304e31-28e2-4481-be77-7e3d24a2b2bf · inbound

CROP: Expert-Aligned Image Cropping via Compositional Reasoning and Optimizing Preference cites this paper.

CROP: Expert-Aligned Image Cropping via Compositional Reasoning and Optimizing Preference ProCrop: Learning Aesthetic Image Cropping from Professional Compositions

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-14T21:38:00.845711Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 882952da-6d79-47a1-83c5-35a2a8cc43f6 · inbound

ShotCrop$^3$: Cropping Human-Centric Images into Cinematic Triple-Shot Compositions cites this paper.

ShotCrop$^3$: Cropping Human-Centric Images into Cinematic Triple-Shot Compositions ProCrop: Learning Aesthetic Image Cropping from Professional Compositions

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-07-02T12:06:55.769858Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.