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Paper Citation Record · LEDGER

MOCHA: Multi-modal Objects-aware Cross-arcHitecture Alignment

As of 14 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 0 inbound Pith citation observations for arXiv:2509.14001.

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

pith.paper-citation-record.v1
2509.14001 v5

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T16:34:07.063560Z

measured 47 of 47 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

47 of 47 outbound references displayed

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Outbound references

Observation 855d096e-1e39-4b2d-994f-5981407d45db · outbound

This paper cites Flamingo: a visual language model for few-shot learning.

MOCHA: Multi-modal Objects-aware Cross-arcHitecture Alignment Flamingo: a visual language model for few-shot learning

Reference 1

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Observation 92f349e3-d0dd-42e3-9bf8-c5f9940ea510 · outbound

This paper cites Continual road-scene semantic segmen- tation via feature-aligned symmetric multi-modal network.

MOCHA: Multi-modal Objects-aware Cross-arcHitecture Alignment Continual road-scene semantic segmen- tation via feature-aligned symmetric multi-modal network

Reference 2

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Observation 36bd31ae-e817-4c12-a11b-4ddd9a3ade56 · outbound

This paper cites Cross-architecture auxiliary fea- ture space translation for efficient few-shot personalized ob- ject detection.

MOCHA: Multi-modal Objects-aware Cross-arcHitecture Alignment Cross-architecture auxiliary fea- ture space translation for efficient few-shot personalized ob- ject detection

Reference 3

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Observation 7a92f379-bc57-4009-be88-31d367dda8c5 · outbound

This paper cites Learn- ing from mistakes: Self-regularizing hierarchical representa- tions in point cloud semantic segmentation.IEEE Transac- tions on Multimedia, 2023.

MOCHA: Multi-modal Objects-aware Cross-arcHitecture Alignment Learn- ing from mistakes: Self-regularizing hierarchical representa- tions in point cloud semantic segmentation.IEEE Transac- tions on Multimedia, 2023

Reference 4

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source=pdf_text observed=2026-08-04T16:34:01.416542Z digest=sha256:148225d2157cf73ae317b70ed27be5fb5eba28ad61155329bf30bf9ee2f0c4ca

Observation 08407589-1ba0-411b-880d-9373b36db6a9 · outbound

This paper cites Bert: Pre-training of deep bidirectional transform- ers for language understanding.

MOCHA: Multi-modal Objects-aware Cross-arcHitecture Alignment Bert: Pre-training of deep bidirectional transform- ers for language understanding

Reference 5

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Observation a987a67e-0d61-450e-a205-ad84632159c7 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

MOCHA: Multi-modal Objects-aware Cross-arcHitecture Alignment An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 6

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Observation 78c2c947-de4a-4ec8-be40-316972cfdb0c · outbound

This paper cites iCub World: Friendly Robots Help Building Good Vision Data-Sets.

MOCHA: Multi-modal Objects-aware Cross-arcHitecture Alignment iCub World: Friendly Robots Help Building Good Vision Data-Sets

Reference 7

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Observation 742fb719-b393-4998-94a5-cc3c129ff94e · outbound

This paper cites Imagebind: One embedding space to bind them all.

MOCHA: Multi-modal Objects-aware Cross-arcHitecture Alignment Imagebind: One embedding space to bind them all

Reference 8

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Observation 3e966d8e-013d-477c-b719-7cda2210a273 · outbound

This paper cites Reciprocal teacher-student learning via forward and feedback knowledge distillation.

MOCHA: Multi-modal Objects-aware Cross-arcHitecture Alignment Reciprocal teacher-student learning via forward and feedback knowledge distillation

Reference 9

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Observation 59d2ba31-5a42-450a-a562-2185afbda63a · outbound

This paper cites Vild: Open-vocabulary object detection via vision and lan- guage knowledge distillation.International Conference on learning Representations (ICLR), 2022.

MOCHA: Multi-modal Objects-aware Cross-arcHitecture Alignment Vild: Open-vocabulary object detection via vision and lan- guage knowledge distillation.International Conference on learning Representations (ICLR), 2022

Reference 10

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source=pdf_text observed=2026-08-04T16:34:02.167817Z digest=sha256:20094716390da3d70c279f23c1fa09815e64b4f413dbea2588bb73ddddaa7fc3

Observation 509117ab-7c1a-4a1e-a06d-4a2cabe2b21e · outbound

This paper cites CDFKD-MFS: Collaborative Data-Free Knowledge Distilla- tion via Multi-Level Feature Sharing.IEEE Transactions on Multimedia, 24:4262–4274, 2022.

MOCHA: Multi-modal Objects-aware Cross-arcHitecture Alignment CDFKD-MFS: Collaborative Data-Free Knowledge Distilla- tion via Multi-Level Feature Sharing.IEEE Transactions on Multimedia, 24:4262–4274, 2022

Reference 11

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Observation 173bcff3-867f-429c-b237-83bf2bd226b0 · outbound

This paper cites One-for-all: Bridge the gap be- tween heterogeneous architectures in knowledge distillation.

MOCHA: Multi-modal Objects-aware Cross-arcHitecture Alignment One-for-all: Bridge the gap be- tween heterogeneous architectures in knowledge distillation

Reference 12

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Observation 8ec161ad-6c93-407c-a659-9cb6ef48a303 · outbound

This paper cites The platonic representation hypothesis.Proceedings of Machine Learning Research (PMLR), 2024.

MOCHA: Multi-modal Objects-aware Cross-arcHitecture Alignment The platonic representation hypothesis.Proceedings of Machine Learning Research (PMLR), 2024

Reference 13

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Observation 2a33b96f-5c4c-4dc3-b6df-cf6dc78fa11d · outbound

This paper cites xmuda: Cross-modal unsu- pervised domain adaptation for 3d semantic segmentation.

MOCHA: Multi-modal Objects-aware Cross-arcHitecture Alignment xmuda: Cross-modal unsu- pervised domain adaptation for 3d semantic segmentation

Reference 14

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source=pdf_text observed=2026-08-04T16:34:02.644748Z digest=sha256:374386d31d43f3953f6b6baf71ddb8e687c577624b15414820a172319abe6713

Observation 30b1dd86-f2a7-48c0-8841-9bdcd804b661 · outbound

This paper cites Cross-modal learning for domain adaptation in 3d semantic segmentation.IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 45(2): 1533–1544, 2022.

MOCHA: Multi-modal Objects-aware Cross-arcHitecture Alignment Cross-modal learning for domain adaptation in 3d semantic segmentation.IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 45(2): 1533–1544, 2022

Reference 15

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Observation a70a435e-513d-4f62-a9a9-e3ee08234bd6 · outbound

This paper cites Ultralytics yolo11, 2024.

MOCHA: Multi-modal Objects-aware Cross-arcHitecture Alignment Ultralytics yolo11, 2024

Reference 16

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source=pdf_text observed=2026-08-04T16:34:02.911971Z digest=sha256:0addf2bf55e45876dfaf7e911379cf2e246118fc77565d0b28aeae0220eb5089

Observation da4b52e6-f4d9-40b7-995b-e19b924ffd86 · outbound

This paper cites Ultralytics yolov8 [computer software].

MOCHA: Multi-modal Objects-aware Cross-arcHitecture Alignment Ultralytics yolov8 [computer software]

Reference 17

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source=pdf_text observed=2026-08-04T16:34:03.004804Z digest=sha256:aa66eecd3aac9b74486149e2dc1bfa21624dedbe1c406bf56f8d0b1f7407ea1a

Observation 895137bb-6b61-45ff-ac04-0063d1e67875 · outbound

This paper cites Segment any- thing.

MOCHA: Multi-modal Objects-aware Cross-arcHitecture Alignment Segment any- thing

Reference 18

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source=pdf_text observed=2026-08-04T16:34:03.164260Z digest=sha256:6097a4e115f6c454c7508b486216c3b647a5e0a22ea7cfa431cf797c67b6de86

Observation e3458ab4-6f6c-45cf-a1df-acae0abe9233 · outbound

This paper cites Neural collapse: A review on mod- elling principles and generalization.Transactions on Machine Learning Research, 2022.

MOCHA: Multi-modal Objects-aware Cross-arcHitecture Alignment Neural collapse: A review on mod- elling principles and generalization.Transactions on Machine Learning Research, 2022

Reference 19

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Observation 6e0427b6-c275-45c4-8033-bdf7c5c356da · outbound

This paper cites an unresolved cited work.

MOCHA: Multi-modal Objects-aware Cross-arcHitecture Alignment Unresolved cited work

Reference 20

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source=pdf_text observed=2026-08-04T16:34:03.574745Z digest=sha256:68827fe8cb242ae89e57b3607344af0e5dece1a47ae200ba9ddb1a49e0bea3d2

Observation d17aba79-f882-48cc-ad33-80f87a0c7271 · outbound

This paper cites Lightweight model pre- training via language guided knowledge distillation.IEEE Transactions on Multimedia, 26:10720–10730, 2024.

MOCHA: Multi-modal Objects-aware Cross-arcHitecture Alignment Lightweight model pre- training via language guided knowledge distillation.IEEE Transactions on Multimedia, 26:10720–10730, 2024

Reference 21

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Observation fc6b1760-a7b1-450c-95bd-21d9940dd471 · outbound

This paper cites Microsoft coco: Common objects in context.

MOCHA: Multi-modal Objects-aware Cross-arcHitecture Alignment Microsoft coco: Common objects in context

Reference 22

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Observation f96ede73-c0f6-424d-9058-a4d3ef994a45 · outbound

This paper cites Visual instruction tuning.Advances in Neural Information Processing Systems, 36:34892–34916, 2023.

MOCHA: Multi-modal Objects-aware Cross-arcHitecture Alignment Visual instruction tuning.Advances in Neural Information Processing Systems, 36:34892–34916, 2023

Reference 23

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Observation f7e56fad-c186-4b5a-91c8-76c130f0b387 · outbound

This paper cites Cross-architecture knowledge distillation.

MOCHA: Multi-modal Objects-aware Cross-arcHitecture Alignment Cross-architecture knowledge distillation

Reference 24

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Observation 8b7a5f2d-c958-482c-95d6-beabc7053226 · outbound

This paper cites Matcher: Segment anything with one shot using all-purpose feature matching.International Conference on Learning Representations (ICLR), 2023.

MOCHA: Multi-modal Objects-aware Cross-arcHitecture Alignment Matcher: Segment anything with one shot using all-purpose feature matching.International Conference on Learning Representations (ICLR), 2023

Reference 25

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Observation c6b00503-2258-4347-be5e-44773059c261 · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

MOCHA: Multi-modal Objects-aware Cross-arcHitecture Alignment Swin transformer: Hierarchical vision transformer using shifted windows

Reference 26

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Observation ada1eb87-70a0-4152-a346-b75aa41b314e · outbound

This paper cites Core50: a new dataset and benchmark for continuous object recognition,.

MOCHA: Multi-modal Objects-aware Cross-arcHitecture Alignment Core50: a new dataset and benchmark for continuous object recognition,

Reference 27

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Observation 6b1c6a10-49c7-4117-a46f-9acdf7c45d7c · outbound

This paper cites Knowledge amalgamation from hetero- geneous networks by common feature learning.International Joint Conference on Artificial Intelligence (IJCAI), 2019.

MOCHA: Multi-modal Objects-aware Cross-arcHitecture Alignment Knowledge amalgamation from hetero- geneous networks by common feature learning.International Joint Conference on Artificial Intelligence (IJCAI), 2019

Reference 28

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Observation 998ee18a-13b8-4de0-8f64-9b2c567612b1 · outbound

This paper cites Rtdetrv2: All-in-one detection transformer beats yolo and dino, 2024.

MOCHA: Multi-modal Objects-aware Cross-arcHitecture Alignment Rtdetrv2: All-in-one detection transformer beats yolo and dino, 2024

Reference 29

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Observation 11a49e2d-f4da-444c-8b3b-8e0e553f1239 · outbound

This paper cites Toward founda- tion models for inclusive object detection: Geometry- and category-aware feature extraction across road user categories.

MOCHA: Multi-modal Objects-aware Cross-arcHitecture Alignment Toward founda- tion models for inclusive object detection: Geometry- and category-aware feature extraction across road user categories

Reference 30

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Observation 5058fa67-be35-4a6b-819c-939982e4129e · outbound

This paper cites Object-conditioned bag of instances for few-shot personalized instance recognition.

MOCHA: Multi-modal Objects-aware Cross-arcHitecture Alignment Object-conditioned bag of instances for few-shot personalized instance recognition

Reference 31

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Observation 1c4af3f5-2b80-4cf7-a8b8-12ee650d49fe · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

MOCHA: Multi-modal Objects-aware Cross-arcHitecture Alignment DINOv2: Learning Robust Visual Features without Supervision

Reference 32

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Observation eeea43bc-8f27-47f8-8570-abbaaa11eb2d · outbound

This paper cites an unresolved cited work.

MOCHA: Multi-modal Objects-aware Cross-arcHitecture Alignment Unresolved cited work

Reference 33

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Observation 692cdf27-0d00-4ea0-ba14-d879896c7835 · outbound

This paper cites Swiss dino: Efficient and versatile vision framework for on-device personal object search.

MOCHA: Multi-modal Objects-aware Cross-arcHitecture Alignment Swiss dino: Efficient and versatile vision framework for on-device personal object search

Reference 34

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Observation f469eda4-3e79-40b3-af24-3ae0dbd92deb · outbound

This paper cites Het- erogeneous knowledge distillation using information flow modeling.

MOCHA: Multi-modal Objects-aware Cross-arcHitecture Alignment Het- erogeneous knowledge distillation using information flow modeling

Reference 35

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source=pdf_text observed=2026-08-04T16:34:05.190941Z digest=sha256:cd625110f2faafd611ba01247ba8c42550bbf58fa2b4f979327cb47a8bb745fa

Observation 8495e591-d87c-452f-bca1-f3ca0b29ee42 · outbound

This paper cites Learning transferable visual models from natural language supervision.Proceedings of Machine Learning Research (PMLR), 2021.

MOCHA: Multi-modal Objects-aware Cross-arcHitecture Alignment Learning transferable visual models from natural language supervision.Proceedings of Machine Learning Research (PMLR), 2021

Reference 36

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Observation 7101b296-882b-4cf2-a25b-ccb0bc821e6c · outbound

This paper cites an unresolved cited work.

MOCHA: Multi-modal Objects-aware Cross-arcHitecture Alignment Unresolved cited work

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source=pdf_text observed=2026-08-04T16:34:05.501903Z digest=sha256:9bfec904bfb10b704d9ed81c4731a2c3471f169cbaefe538d42cf7d368fbbe75

Observation 0720e353-2c2d-4190-82ab-624f6e186e29 · outbound

This paper cites Prototypi- cal networks for few-shot learning.

MOCHA: Multi-modal Objects-aware Cross-arcHitecture Alignment Prototypi- cal networks for few-shot learning

Reference 38

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source=pdf_text observed=2026-08-04T16:34:05.610910Z digest=sha256:e78dce66bc5730d461f6b28ba1f4c7b3004cdd59477274aeff1b6ecfcf3a6a1f

Observation e9ccecff-3d87-456b-a928-809f8d189afc · outbound

This paper cites Training data-efficient image transformers & distillation through at- tention.

MOCHA: Multi-modal Objects-aware Cross-arcHitecture Alignment Training data-efficient image transformers & distillation through at- tention

Reference 39

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source=pdf_text observed=2026-08-04T16:34:05.744749Z digest=sha256:75601873f61580337042e781d346153400f84e141fafd6db423cd940d9ecc35c

Observation 055f7f82-6462-4224-a48e-9a708e4d56c8 · outbound

This paper cites Hybrid knowledge distillation network for RGB-D co- salient object detection.IEEE Transactions on Systems, Man, and Cybernetics: Systems, pages 1–12, 2025.

MOCHA: Multi-modal Objects-aware Cross-arcHitecture Alignment Hybrid knowledge distillation network for RGB-D co- salient object detection.IEEE Transactions on Systems, Man, and Cybernetics: Systems, pages 1–12, 2025

Reference 40

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source=pdf_text observed=2026-08-04T16:34:05.864746Z digest=sha256:b9ecd47029fc84a7c8c7e6e78957159da9d40a000daf5128d63cc7d6e113fb7a

Observation be4e1df7-c374-4c64-9d39-921f476e5a4d · outbound

This paper cites Attention is all you need.Advances in Neural Information Processing Systems, 30, 2017.

MOCHA: Multi-modal Objects-aware Cross-arcHitecture Alignment Attention is all you need.Advances in Neural Information Processing Systems, 30, 2017

Reference 41

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no resolver link, observed 2026-08-04T16:34:05.994819Z

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source=pdf_text observed=2026-08-04T16:34:05.994819Z digest=sha256:d856877c9c699bcbdecfdb5c838c0bf89a4d707ae086dd1176c9c24cf003bc5e

Observation 317957d5-11b7-4294-adcb-e147358bb9e6 · outbound

This paper cites Seggpt: Segmenting everything in context.IEEE/CVF International Conference on Computer Vision (ICCV), 2023.

MOCHA: Multi-modal Objects-aware Cross-arcHitecture Alignment Seggpt: Segmenting everything in context.IEEE/CVF International Conference on Computer Vision (ICCV), 2023

Reference 42

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no resolver link, observed 2026-08-04T16:34:06.126465Z

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source=pdf_text observed=2026-08-04T16:34:06.126465Z digest=sha256:1212064f2f80d6cb5d0c118d7b05e740cf6d6b2deb4d8ec19042a0e840898b8b

Observation e092db26-5c12-421a-bc42-5b003f3d0ab8 · outbound

This paper cites Weinberger, and Laurens van der Maaten.

MOCHA: Multi-modal Objects-aware Cross-arcHitecture Alignment Weinberger, and Laurens van der Maaten

Reference 43

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no resolver link, observed 2026-08-04T16:34:06.368827Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-04T16:34:06.368827Z digest=sha256:e86b4388d993cfacf746b4fd25e3f5a50b1aa256b8e465d271c631994fed9e83

Observation 0cde860c-4606-4528-8d96-a53bbf58d0e3 · outbound

This paper cites Cmda: Cross-modality domain adap- tation for nighttime semantic segmentation.

MOCHA: Multi-modal Objects-aware Cross-arcHitecture Alignment Cmda: Cross-modality domain adap- tation for nighttime semantic segmentation

Reference 44

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-04T16:34:06.564749Z digest=sha256:5e34944262eaf2e9fae4ab46aa12e7980278fdb95532f0730bc7d8f071651d48

Observation 10f46754-95ea-4ac6-b6fc-f8108e467c06 · outbound

This paper cites Task-oriented feature distillation.Advances in Neural Information Processing Systems, 33:14759–14771,.

MOCHA: Multi-modal Objects-aware Cross-arcHitecture Alignment Task-oriented feature distillation.Advances in Neural Information Processing Systems, 33:14759–14771,

Reference 45

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no resolver link, observed 2026-08-04T16:34:06.789210Z

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source=pdf_text observed=2026-08-04T16:34:06.789210Z digest=sha256:163386321627e2a95ba7835c042220338edf6b835a7128efddd45e23705ba53f

Observation 171ba98d-dfab-4f41-9c0d-38e90fafefa2 · outbound

This paper cites Personalize segment anything model with one shot.

MOCHA: Multi-modal Objects-aware Cross-arcHitecture Alignment Personalize segment anything model with one shot

Reference 46

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no resolver link, observed 2026-08-04T16:34:06.910479Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-04T16:34:06.910479Z digest=sha256:e1c93ffb9df47f2371c893a53c2627e8b7475fcd630814e06730a74fa8333964

Observation 766c9e87-696f-43f0-9953-670ae984fb62 · outbound

This paper cites Personalized image semantic segmen- tation.

MOCHA: Multi-modal Objects-aware Cross-arcHitecture Alignment Personalized image semantic segmen- tation

Reference 47

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-04T16:34:07.063560Z digest=sha256:dbd99a68c766337d9ff61bebdbd98658d98ff1af060f075a0f978a1ba0546c4b

Pith citing papers

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