Paper Citation Record · LEDGER

Looking Locally: Object-Centric Vision Transformers as Foundation Models for Efficient Segmentation

As of 10 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 1 inbound Pith citation observation for arXiv:2502.02763.

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

pith.paper-citation-record.v1
2502.02763 v3

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T11:19:23.191967Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-10T00:18:06.989944Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-10T00:19:46.820702Z

Reference resolution

31 of 31 outbound references displayed

verified exact0
verified fuzzy17
unresolved14
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation aef2c08a-e2ef-4762-bcaf-da5d24a04762 · outbound

This paper cites Focal sparse convolutional networks for 3d object detection.

Looking Locally: Object-Centric Vision Transformers as Foundation Models for Efficient Segmentation Focal sparse convolutional networks for 3d object detection

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:19:23.532068Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6b9a0d16-b217-4c80-a1e9-e18b7178aeaf · outbound

This paper cites Deformable convolutional networks.

Looking Locally: Object-Centric Vision Transformers as Foundation Models for Efficient Segmentation Deformable convolutional networks

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:19:23.516609Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e692c0da-2b99-498e-ab61-ddf0e784fd93 · outbound

This paper cites Objaverse: A universe of annotated 3d objects.

Looking Locally: Object-Centric Vision Transformers as Foundation Models for Efficient Segmentation Objaverse: A universe of annotated 3d objects

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:19:23.505803Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5a218b3b-ef72-4de6-a4a2-0ea29ab5a7e2 · outbound

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

Looking Locally: Object-Centric Vision Transformers as Foundation Models for Efficient Segmentation An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-09T11:19:23.091877Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ed9c03ac-82bf-4c07-8931-eda19d4fd0ee · outbound

This paper cites C., and Kipf, T.

Looking Locally: Object-Centric Vision Transformers as Foundation Models for Efficient Segmentation C., and Kipf, T

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:19:23.494268Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e355b5a9-61dc-4867-bed8-e3bc0e20e8dd · outbound

This paper cites Lvis: A dataset for large vocabulary instance segmentation.

Looking Locally: Object-Centric Vision Transformers as Foundation Models for Efficient Segmentation Lvis: A dataset for large vocabulary instance segmentation

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-09T11:19:23.099713Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 63c4ade8-74ca-41fa-acb4-3271184e0ee0 · outbound

This paper cites Perceiver IO: A General Architecture for Structured Inputs & Outputs.

Looking Locally: Object-Centric Vision Transformers as Foundation Models for Efficient Segmentation Perceiver IO: A General Architecture for Structured Inputs & Outputs

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-09T11:19:23.103509Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d6085e78-f0da-42b2-80e4-1bc5be682996 · outbound

This paper cites S., Sochenov, A., Leimk \"u hler, T., Okunev, M., Goodall, T., and Rufo, G.

Looking Locally: Object-Centric Vision Transformers as Foundation Models for Efficient Segmentation S., Sochenov, A., Leimk \"u hler, T., Okunev, M., Goodall, T., and Rufo, G

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:19:23.477171Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 348ee7a9-8c1f-46a1-baf1-ac6afdd9af4c · outbound

This paper cites C., Lo, W.-Y., et al.

Looking Locally: Object-Centric Vision Transformers as Foundation Models for Efficient Segmentation C., Lo, W.-Y., et al

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-09T11:19:23.110318Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2727d70f-9aa7-45b2-9a47-13d1e7b60a63 · outbound

This paper cites The open images dataset v4: Unified image classification, object detection, and visual relationship detection at scale.

Looking Locally: Object-Centric Vision Transformers as Foundation Models for Efficient Segmentation The open images dataset v4: Unified image classification, object detection, and visual relationship detection at scale

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-09T11:19:23.113657Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 61d094aa-1c0b-476c-a91f-1cb4287a185e · outbound

This paper cites Kitti-360: A novel dataset and benchmarks for urban scene understanding in 2d and 3d.

Looking Locally: Object-Centric Vision Transformers as Foundation Models for Efficient Segmentation Kitti-360: A novel dataset and benchmarks for urban scene understanding in 2d and 3d

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:19:23.453297Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation dfcf102c-27e3-47a9-b365-35c904a1c5fe · outbound

This paper cites an unresolved cited work.

Looking Locally: Object-Centric Vision Transformers as Foundation Models for Efficient Segmentation Unresolved cited work

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-09T11:19:23.120823Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ecbb7336-e589-441a-8320-b7a771ff23a5 · outbound

This paper cites A convnet for the 2020s.

Looking Locally: Object-Centric Vision Transformers as Foundation Models for Efficient Segmentation A convnet for the 2020s

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-09T11:19:23.124894Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4761056f-94e7-4bdf-86a2-2f0bac12a388 · outbound

This paper cites Object-centric learning with slot attention.

Looking Locally: Object-Centric Vision Transformers as Foundation Models for Efficient Segmentation Object-centric learning with slot attention

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-09T11:19:23.128418Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fa0d062a-7b94-4f7a-95b8-f599a6995fef · outbound

This paper cites Biologically inspired deep learning model for efficient foveal-peripheral vision.

Looking Locally: Object-Centric Vision Transformers as Foundation Models for Efficient Segmentation Biologically inspired deep learning model for efficient foveal-peripheral vision

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:19:23.423820Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 39862b7b-c7af-4362-bf18-b8b4f76699e2 · outbound

This paper cites A., Paczan, N., Webb, R., and Susskind, J.

Looking Locally: Object-Centric Vision Transformers as Foundation Models for Efficient Segmentation A., Paczan, N., Webb, R., and Susskind, J

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:19:23.412652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3de9cd10-298a-4932-87b4-247187451080 · outbound

This paper cites Simple unsupervised object-centric learning for complex and naturalistic videos.

Looking Locally: Object-Centric Vision Transformers as Foundation Models for Efficient Segmentation Simple unsupervised object-centric learning for complex and naturalistic videos

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:19:23.401449Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f73b75ad-9853-491b-b4fb-24f6c98c6f8b · outbound

This paper cites Fovea: Foveated image magnification for autonomous navigation.

Looking Locally: Object-Centric Vision Transformers as Foundation Models for Efficient Segmentation Fovea: Foveated image magnification for autonomous navigation

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:19:23.389788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5ebfc136-c11f-4d3a-8233-7baeeafc4e15 · outbound

This paper cites an unresolved cited work.

Looking Locally: Object-Centric Vision Transformers as Foundation Models for Efficient Segmentation Unresolved cited work

Reference 19

Resolution: unresolved; raw_fallback, observed 2026-08-09T11:19:23.378406Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ad15cafb-baf6-44eb-a9fa-b5ec089c81c3 · outbound

This paper cites an unresolved cited work.

Looking Locally: Object-Centric Vision Transformers as Foundation Models for Efficient Segmentation Unresolved cited work

Reference 20

Resolution: unresolved; raw_fallback, observed 2026-08-09T11:19:23.366867Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 11f94f2c-7b34-40ac-829c-2a84283a96fc · outbound

This paper cites an unresolved cited work.

Looking Locally: Object-Centric Vision Transformers as Foundation Models for Efficient Segmentation Unresolved cited work

Reference 21

Resolution: unresolved; raw_fallback, observed 2026-08-09T11:19:23.355602Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation de4422ff-b813-4f9b-8bd6-1d03b448c22e · outbound

This paper cites Tinyvit: Fast pretraining distillation for small vision transformers.

Looking Locally: Object-Centric Vision Transformers as Foundation Models for Efficient Segmentation Tinyvit: Fast pretraining distillation for small vision transformers

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:19:23.344416Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b1807004-4610-4e87-9e7d-d8620f8f41f1 · outbound

This paper cites EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything.

Looking Locally: Object-Centric Vision Transformers as Foundation Models for Efficient Segmentation EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-09T11:19:23.161236Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5022d81f-a49d-4d69-bf07-c58193a110cc · outbound

This paper cites Efficient deformable convnets: Rethinking dynamic and sparse operator for vision applications.

Looking Locally: Object-Centric Vision Transformers as Foundation Models for Efficient Segmentation Efficient deformable convnets: Rethinking dynamic and sparse operator for vision applications

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:19:23.333517Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1283680c-c063-4688-ac1a-a4cec288a2b4 · outbound

This paper cites Lape: Layer-adaptive position embedding for vision transformers with independent layer normalization.

Looking Locally: Object-Centric Vision Transformers as Foundation Models for Efficient Segmentation Lape: Layer-adaptive position embedding for vision transformers with independent layer normalization

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:19:23.321516Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 572d0087-8a8b-49e8-9367-5a618df5a46b · outbound

This paper cites Hdri haven, 2016.

Looking Locally: Object-Centric Vision Transformers as Foundation Models for Efficient Segmentation Hdri haven, 2016

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:19:23.309605Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3526f2cc-6731-4960-a531-8761ca17a5f1 · outbound

This paper cites Object-centric learning for real-world videos by predicting temporal feature similarities.

Looking Locally: Object-Centric Vision Transformers as Foundation Models for Efficient Segmentation Object-centric learning for real-world videos by predicting temporal feature similarities

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:19:23.297696Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 456ce933-29a1-431c-87ba-1339b93fbc60 · outbound

This paper cites Faster Segment Anything: Towards Lightweight SAM for Mobile Applications.

Looking Locally: Object-Centric Vision Transformers as Foundation Models for Efficient Segmentation Faster Segment Anything: Towards Lightweight SAM for Mobile Applications

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-09T11:19:23.179663Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ecee854f-05e6-4dcc-8e8e-7774c690e1d5 · outbound

This paper cites Fast segment anything, 2023.

Looking Locally: Object-Centric Vision Transformers as Foundation Models for Efficient Segmentation Fast segment anything, 2023

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:19:23.286527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 698f2ba0-d7ed-4fcd-9f6d-e244b795f22b · outbound

This paper cites Deformable convnets v2: More deformable, better results.

Looking Locally: Object-Centric Vision Transformers as Foundation Models for Efficient Segmentation Deformable convnets v2: More deformable, better results

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:19:23.275476Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a8a9c764-eca2-407d-87bc-0a74a9bf7549 · outbound

This paper cites write newline.

Looking Locally: Object-Centric Vision Transformers as Foundation Models for Efficient Segmentation write newline

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-09T11:19:23.191967Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation be67138d-9cdd-4c25-973e-ac8abc8f5a1d · inbound

Self-supervised pretraining for an iterative image size agnostic vision transformer cites this paper.

Self-supervised pretraining for an iterative image size agnostic vision transformer Looking Locally: Object-Centric Vision Transformers as Foundation Models for Efficient Segmentation

Reference 53

Resolution: verified exact; arxiv_id, observed 2026-07-13T01:17:40.500289Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.