Paper Citation Record · LEDGER

AutoQ-VIS: Improving Unsupervised Video Instance Segmentation via Automatic Quality Assessment

As of 19 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 0 inbound Pith citation observations for arXiv:2508.19808.

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

pith.paper-citation-record.v1
2508.19808 v1

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T15:33:16.098084Z

measured 26 of 26 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

26 of 26 outbound references displayed

verified exact0
verified fuzzy23
unresolved3
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a808d3c0-cac7-4ea4-9a97-08b83890a4c2 · outbound

This paper cites Dense unsupervised learning for video segmentation.

AutoQ-VIS: Improving Unsupervised Video Instance Segmentation via Automatic Quality Assessment Dense unsupervised learning for video segmentation

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:33:16.857697Z

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 500da900-2e9b-4573-94d9-6a9d882cd9a5 · outbound

This paper cites Mask2Former for Video Instance Segmentation.

AutoQ-VIS: Improving Unsupervised Video Instance Segmentation via Automatic Quality Assessment Mask2Former for Video Instance Segmentation

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-05T15:33:15.913482Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a7913915-53f5-45c4-8efb-246112791e0f · outbound

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

AutoQ-VIS: Improving Unsupervised Video Instance Segmentation via Automatic Quality Assessment Masked-attention mask transformer for universal image segmentation

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:33:16.829692Z

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 603bf1bf-983f-4a10-99e3-ad1a5c026eee · outbound

This paper cites Guess What Moves: Unsupervised Video and Image Segmentation by Anticipat- ing Motion.

AutoQ-VIS: Improving Unsupervised Video Instance Segmentation via Automatic Quality Assessment Guess What Moves: Unsupervised Video and Image Segmentation by Anticipat- ing Motion

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:33:16.802811Z

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 7ae55875-cdcb-4ec9-8b79-52273cc978a9 · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

AutoQ-VIS: Improving Unsupervised Video Instance Segmentation via Automatic Quality Assessment Imagenet: A large-scale hierarchical image database

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:33:16.775261Z

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 43e73567-2a9d-4567-84a0-5791d08b2088 · outbound

This paper cites Unsupervised ob- ject segmentation in video by efficient selection of highly probable positive features.

AutoQ-VIS: Improving Unsupervised Video Instance Segmentation via Automatic Quality Assessment Unsupervised ob- ject segmentation in video by efficient selection of highly probable positive features

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:33:16.744968Z

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 53939869-b623-48fc-b8ac-2c5e69aac93d · outbound

This paper cites Deep residual learning for image recognition.

AutoQ-VIS: Improving Unsupervised Video Instance Segmentation via Automatic Quality Assessment Deep residual learning for image recognition

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:33:16.720050Z

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 20ef19d6-61e4-4172-852f-6efb6cdd1bc4 · outbound

This paper cites Vita: Video instance segmentation via object token association.

AutoQ-VIS: Improving Unsupervised Video Instance Segmentation via Automatic Quality Assessment Vita: Video instance segmentation via object token association

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:33:16.691471Z

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 fbd17947-30ec-410d-ab20-45b10d4c1d46 · outbound

This paper cites Mask scoring r-cnn.

AutoQ-VIS: Improving Unsupervised Video Instance Segmentation via Automatic Quality Assessment Mask scoring r-cnn

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:33:16.655784Z

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 ae595a0f-9852-4cf4-91ef-e4cfea8b2aed · outbound

This paper cites Bootstrapping objectness from videos by relaxed common fate and visual grouping.

AutoQ-VIS: Improving Unsupervised Video Instance Segmentation via Automatic Quality Assessment Bootstrapping objectness from videos by relaxed common fate and visual grouping

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:33:16.634634Z

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 369d8cd4-30f0-453e-8172-aadba13f60e4 · outbound

This paper cites Video instance segmentation tracking with a modified vae architecture.

AutoQ-VIS: Improving Unsupervised Video Instance Segmentation via Automatic Quality Assessment Video instance segmentation tracking with a modified vae architecture

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:33:16.612168Z

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 abc7bc3e-4ee9-46fa-b8c9-1299f1fb2921 · outbound

This paper cites Em-driven unsupervised learning for efficient motion seg- mentation.

AutoQ-VIS: Improving Unsupervised Video Instance Segmentation via Automatic Quality Assessment Em-driven unsupervised learning for efficient motion seg- mentation

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:33:16.577293Z

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 d90563ae-4ece-49e1-ba20-3b32b14cbbb6 · outbound

This paper cites Raft: Recurrent all-pairs field transforms for optical flow.

AutoQ-VIS: Improving Unsupervised Video Instance Segmentation via Automatic Quality Assessment Raft: Recurrent all-pairs field transforms for optical flow

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-05T15:33:16.000632Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 399821b9-ec7c-4247-a2c3-c293382484e3 · outbound

This paper cites Cut and learn for unsupervised object detection and instance segmentation.

AutoQ-VIS: Improving Unsupervised Video Instance Segmentation via Automatic Quality Assessment Cut and learn for unsupervised object detection and instance segmentation

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:33:16.525814Z

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 447c3747-5936-43aa-8970-69e81e348322 · outbound

This paper cites Videocutler: Surprisingly simple un- supervised video instance segmentation.

AutoQ-VIS: Improving Unsupervised Video Instance Segmentation via Automatic Quality Assessment Videocutler: Surprisingly simple un- supervised video instance segmentation

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:33:16.504529Z

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 92fce101-1726-4d69-b273-2d5b51c198d0 · outbound

This paper cites End-to-end video instance segmentation with transformers.

AutoQ-VIS: Improving Unsupervised Video Instance Segmentation via Automatic Quality Assessment End-to-end video instance segmentation with transformers

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:33:16.483367Z

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 9688c37f-9969-4e36-95f5-1c62c3c8c5cf · outbound

This paper cites Seqformer: Sequential transformer for video instance segmentation.

AutoQ-VIS: Improving Unsupervised Video Instance Segmentation via Automatic Quality Assessment Seqformer: Sequential transformer for video instance segmentation

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:33:16.458624Z

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 57f1ef30-0985-4f5b-bf26-de666dfb4eb0 · outbound

This paper cites Segment- ing moving objects via an object-centric layered representa- tion.

AutoQ-VIS: Improving Unsupervised Video Instance Segmentation via Automatic Quality Assessment Segment- ing moving objects via an object-centric layered representa- tion

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:33:16.431290Z

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 fdcffdf1-d944-4a63-9614-9a7e2fd2962a · outbound

This paper cites Self-supervised video object segmentation by motion grouping.

AutoQ-VIS: Improving Unsupervised Video Instance Segmentation via Automatic Quality Assessment Self-supervised video object segmentation by motion grouping

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:33:16.402299Z

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 8fe76a46-533b-46a9-9e89-e1c78d39d8e4 · outbound

This paper cites Video instance seg- mentation.

AutoQ-VIS: Improving Unsupervised Video Instance Segmentation via Automatic Quality Assessment Video instance seg- mentation

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:33:16.361497Z

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 eadca614-c125-4ea8-a655-7e0e36165752 · outbound

This paper cites Bdd100k: A diverse driving dataset for heterogeneous multitask learning.

AutoQ-VIS: Improving Unsupervised Video Instance Segmentation via Automatic Quality Assessment Bdd100k: A diverse driving dataset for heterogeneous multitask learning

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:33:16.332279Z

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 19989dd9-fa8b-4eaf-ba7d-f10cfd127afb · outbound

This paper cites Deep transport network for unsupervised video ob- ject segmentation.

AutoQ-VIS: Improving Unsupervised Video Instance Segmentation via Automatic Quality Assessment Deep transport network for unsupervised video ob- ject segmentation

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:33:16.299447Z

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 3bb08480-502d-4737-8cc3-0273414c6370 · outbound

This paper cites Dvis: Decoupled video instance segmentation framework.

AutoQ-VIS: Improving Unsupervised Video Instance Segmentation via Automatic Quality Assessment Dvis: Decoupled video instance segmentation framework

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:33:16.271299Z

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 33d0dc66-0e2c-4bdf-a3d5-9a6b6ec54004 · outbound

This paper cites A survey on deep learning technique for video segmentation.

AutoQ-VIS: Improving Unsupervised Video Instance Segmentation via Automatic Quality Assessment A survey on deep learning technique for video segmentation

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:33:16.246968Z

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 f5de531f-48ae-44e5-beae-95a9ac992b46 · outbound

This paper cites an unresolved cited work.

AutoQ-VIS: Improving Unsupervised Video Instance Segmentation via Automatic Quality Assessment Unresolved cited work

Reference 25

Resolution: unresolved; raw_fallback, observed 2026-08-05T15:33:16.215308Z

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 23c5a061-d6be-4213-9825-835a13190bc5 · outbound

This paper cites 5 and Fig.

AutoQ-VIS: Improving Unsupervised Video Instance Segmentation via Automatic Quality Assessment 5 and Fig

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:33:16.190279Z

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.