Paper Citation Record · LEDGER

UIFormer: A Unified Transformer-based Framework for Incremental Few-Shot Object Detection and Instance Segmentation

As of 19 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2411.08569.

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

pith.paper-citation-record.v1
2411.08569 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T21:36:13.888116Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

31 of 31 outbound references displayed

verified exact1
verified fuzzy21
unresolved9
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d6a62a09-bc3b-401e-b81e-3ee2e03cdd9a · outbound

This paper cites Incremental few-shot object detection.

UIFormer: A Unified Transformer-based Framework for Incremental Few-Shot Object Detection and Instance Segmentation Incremental few-shot object detection

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:36:14.315756Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2d1167ae-3678-4cfa-8b6c-a3f6dd8d99ea · outbound

This paper cites Class-Incremental Few-Shot Object Detection.

UIFormer: A Unified Transformer-based Framework for Incremental Few-Shot Object Detection and Instance Segmentation Class-Incremental Few-Shot Object Detection

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-08-12T21:36:13.978249Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d733596b-654b-4897-a429-a2bbc0899f92 · outbound

This paper cites Incremental-detr: Incremental few-shot object detection via self-supervised learning.

UIFormer: A Unified Transformer-based Framework for Incremental Few-Shot Object Detection and Instance Segmentation Incremental-detr: Incremental few-shot object detection via self-supervised learning

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:36:14.300496Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d84b532b-e418-4fbd-a6b1-bbaab0847c1c · outbound

This paper cites Sylph: A hypernetwork framework for incremental few-shot object detection.

UIFormer: A Unified Transformer-based Framework for Incremental Few-Shot Object Detection and Instance Segmentation Sylph: A hypernetwork framework for incremental few-shot object detection

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:36:14.286432Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8b768960-5983-4a7d-b315-e8b79f352ed4 · outbound

This paper cites Incremental few-shot instance segmentation.

UIFormer: A Unified Transformer-based Framework for Incremental Few-Shot Object Detection and Instance Segmentation Incremental few-shot instance segmentation

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:36:14.272586Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation bfe51357-0573-4c7d-b458-96cf19c9362c · outbound

This paper cites ifs-rcnn: An incremental few-shot instance segmenter.

UIFormer: A Unified Transformer-based Framework for Incremental Few-Shot Object Detection and Instance Segmentation ifs-rcnn: An incremental few-shot instance segmenter

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:36:14.259521Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 095b025c-5bfb-4c6c-829b-7a9981a88369 · outbound

This paper cites Mask dino: Towards a unified transformer-based framework for object detection and segmentation.

UIFormer: A Unified Transformer-based Framework for Incremental Few-Shot Object Detection and Instance Segmentation Mask dino: Towards a unified transformer-based framework for object detection and segmentation

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-12T21:36:13.786809Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d4eff4ad-69ae-4b28-bf1f-58de0d35a96a · outbound

This paper cites DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection.

UIFormer: A Unified Transformer-based Framework for Incremental Few-Shot Object Detection and Instance Segmentation DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-12T21:36:13.792085Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0dce85e9-bcf8-495c-afd7-e46577940ad8 · outbound

This paper cites Few-shot object detection with attention-rpn and multi- relation detector.

UIFormer: A Unified Transformer-based Framework for Incremental Few-Shot Object Detection and Instance Segmentation Few-shot object detection with attention-rpn and multi- relation detector

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:36:14.239523Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation fed8c13d-4af7-4e0f-817a-63d79ce13996 · outbound

This paper cites Few-shot object detection via feature reweighting.

UIFormer: A Unified Transformer-based Framework for Incremental Few-Shot Object Detection and Instance Segmentation Few-shot object detection via feature reweighting

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:36:14.227534Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5b9e3ccd-460d-471d-bc14-57fac6fc8fdb · outbound

This paper cites Meta r-cnn: Towards general solver for instance-level low-shot learning.

UIFormer: A Unified Transformer-based Framework for Incremental Few-Shot Object Detection and Instance Segmentation Meta r-cnn: Towards general solver for instance-level low-shot learning

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:36:14.215236Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 722d7dcb-870e-4ccf-8646-82352a94bfe0 · outbound

This paper cites an unresolved cited work.

UIFormer: A Unified Transformer-based Framework for Incremental Few-Shot Object Detection and Instance Segmentation Unresolved cited work

Reference 12

Resolution: unresolved; raw_fallback, observed 2026-08-12T21:36:14.202711Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0b6852c0-542d-4a70-ba9e-dfac8d419c52 · outbound

This paper cites Fine-grained prototypes distillation for few-shot object detection.

UIFormer: A Unified Transformer-based Framework for Incremental Few-Shot Object Detection and Instance Segmentation Fine-grained prototypes distillation for few-shot object detection

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:36:14.190134Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation cf9437bd-efe0-41c2-a549-fe4bf38f1bc9 · outbound

This paper cites Few-shot object detection with fully cross-transformer.

UIFormer: A Unified Transformer-based Framework for Incremental Few-Shot Object Detection and Instance Segmentation Few-shot object detection with fully cross-transformer

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:36:14.177323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b1f65809-201e-44d5-99ee-2404706d6e96 · outbound

This paper cites Matching networks for one shot learning.

UIFormer: A Unified Transformer-based Framework for Incremental Few-Shot Object Detection and Instance Segmentation Matching networks for one shot learning

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:36:14.164372Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2d264b25-e34a-4154-b9cc-ad733e5bd521 · outbound

This paper cites LSTD: A low-shot transfer detector for object detection.

UIFormer: A Unified Transformer-based Framework for Incremental Few-Shot Object Detection and Instance Segmentation LSTD: A low-shot transfer detector for object detection

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:36:14.149113Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation cc423a1c-5b85-4beb-957d-411ecff3f9f2 · outbound

This paper cites Huang, Joseph Gonzalez, Trevor Darrell, and Fisher Yu.

UIFormer: A Unified Transformer-based Framework for Incremental Few-Shot Object Detection and Instance Segmentation Huang, Joseph Gonzalez, Trevor Darrell, and Fisher Yu

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:36:14.136115Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d23ddb32-ab28-436f-b75a-6dce0c47e62c · outbound

This paper cites FSCE: few-shot object detection via contrastive proposal encoding.

UIFormer: A Unified Transformer-based Framework for Incremental Few-Shot Object Detection and Instance Segmentation FSCE: few-shot object detection via contrastive proposal encoding

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:36:14.121628Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6b3410c8-92da-42f9-a487-6716e64e9b6c · outbound

This paper cites Multi-scale positive sample refinement for few-shot object detection.

UIFormer: A Unified Transformer-based Framework for Incremental Few-Shot Object Detection and Instance Segmentation Multi-scale positive sample refinement for few-shot object detection

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:36:14.103751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b7a5e8f1-5168-4a69-8325-da404e0be5bd · outbound

This paper cites FGN: fully guided network for few-shot instance segmentation.

UIFormer: A Unified Transformer-based Framework for Incremental Few-Shot Object Detection and Instance Segmentation FGN: fully guided network for few-shot instance segmentation

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:36:14.089172Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 09f03049-2e5c-47b7-87ab-4df206207744 · outbound

This paper cites One-Shot Instance Segmentation.

UIFormer: A Unified Transformer-based Framework for Incremental Few-Shot Object Detection and Instance Segmentation One-Shot Instance Segmentation

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-12T21:36:13.846272Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8187c9f5-1dd0-44b5-a04b-ea264ae7955b · outbound

This paper cites FAPIS: A few-shot anchor-free part-based instance segmenter.

UIFormer: A Unified Transformer-based Framework for Incremental Few-Shot Object Detection and Instance Segmentation FAPIS: A few-shot anchor-free part-based instance segmenter

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:36:14.075248Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 474be6e8-ac1d-40da-8855-cb9ca2b69879 · outbound

This paper cites Meta R-CNN: towards general solver for instance-level low-shot learning.

UIFormer: A Unified Transformer-based Framework for Incremental Few-Shot Object Detection and Instance Segmentation Meta R-CNN: towards general solver for instance-level low-shot learning

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:36:14.061283Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e921c1b4-66b2-4692-ba54-1fb1eba739b1 · outbound

This paper cites Nguyen, Trung-Nghia Le, Thanh-Toan Do, Minh N.

UIFormer: A Unified Transformer-based Framework for Incremental Few-Shot Object Detection and Instance Segmentation Nguyen, Trung-Nghia Le, Thanh-Toan Do, Minh N

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:36:14.048217Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ce8dc5df-62d1-4558-aaea-62b9845cd5f1 · outbound

This paper cites Objects as Points.

UIFormer: A Unified Transformer-based Framework for Incremental Few-Shot Object Detection and Instance Segmentation Objects as Points

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-12T21:36:13.862112Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 09171762-0c3e-4b26-a778-ff50d9d20c49 · outbound

This paper cites End-to-end object detection with transformers.

UIFormer: A Unified Transformer-based Framework for Incremental Few-Shot Object Detection and Instance Segmentation End-to-end object detection with transformers

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-12T21:36:13.866203Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e4bbccdd-1542-4e1b-be8f-31a4db38e745 · outbound

This paper cites Mask r-cnn.

UIFormer: A Unified Transformer-based Framework for Incremental Few-Shot Object Detection and Instance Segmentation Mask r-cnn

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-12T21:36:13.870637Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c837380f-2426-48c7-8bc9-b14a582dd71f · outbound

This paper cites Masked-attention mask transformer for universal image segmentation.

UIFormer: A Unified Transformer-based Framework for Incremental Few-Shot Object Detection and Instance Segmentation Masked-attention mask transformer for universal image segmentation

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-12T21:36:13.875604Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f973d563-e31e-4196-bd4d-71ed7be17f3b · outbound

This paper cites Ow-detr: Open-world detection transformer.

UIFormer: A Unified Transformer-based Framework for Incremental Few-Shot Object Detection and Instance Segmentation Ow-detr: Open-world detection transformer

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:36:14.012877Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0befd344-9250-4b1b-a3bf-fe68d3325468 · outbound

This paper cites Microsoft coco: Common objects in context.

UIFormer: A Unified Transformer-based Framework for Incremental Few-Shot Object Detection and Instance Segmentation Microsoft coco: Common objects in context

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-12T21:36:13.884090Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7615387e-d6e6-406b-b908-f4375334a2aa · outbound

This paper cites Reference twice: A simple and unified baseline for few-shot instance segmentation.

UIFormer: A Unified Transformer-based Framework for Incremental Few-Shot Object Detection and Instance Segmentation Reference twice: A simple and unified baseline for few-shot instance segmentation

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:36:13.992086Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

No inbound Pith citation observations are available.