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Paper Citation Record · LEDGER

VespaSeg: A Resource-Aware Ground-then-Segment Pipeline for Referring Expression Segmentation

As of 17 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 0 inbound Pith citation observations for arXiv:2608.01077.

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

pith.paper-citation-record.v1
2608.01077 v1

Coverage vector

measured 15 of 15 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T15:15:10.347638Z

measured 15 of 15 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+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

15 of 15 outbound references displayed

  • verified exact2
  • verified fuzzy0
  • unresolved13
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 622eab94-91ef-458c-9b61-b9aff2252193 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

VespaSeg: A Resource-Aware Ground-then-Segment Pipeline for Referring Expression Segmentation LoRA: Low-Rank Adaptation of Large Language Models

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-15T15:15:10.295965Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T15:15:10.295965Z digest=sha256:ac1e6e69b282c3b7a6fdc63bb22f8c9aa3f7f78ec1895bbc3ec26d0a1da1772c

Observation 38776d49-69bd-4e8b-abf9-809e0249b6ec · outbound

This paper cites ReSTR: Convolution-free Referring Image Segmentation Using Transformers.

VespaSeg: A Resource-Aware Ground-then-Segment Pipeline for Referring Expression Segmentation ReSTR: Convolution-free Referring Image Segmentation Using Transformers

Reference 2

Resolution
verified exact
local_arxiv, observed 2026-08-15T15:15:10.550133Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T15:15:10.300034Z digest=sha256:47962c838a0a65d9ee6091de6760e7d20253b3f57d27c4e8f3ed7f58312545bb

Observation 69c70d6c-9bed-4fee-8501-d5d3c3003cdb · outbound

This paper cites Segment Anything.

VespaSeg: A Resource-Aware Ground-then-Segment Pipeline for Referring Expression Segmentation Segment Anything

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-15T15:15:10.303523Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T15:15:10.303523Z digest=sha256:b50f5d3257d33b4addaeee046992d1220a12176c49cfb699d98bf5553bbffab2

Observation 204614ce-a7e4-41b2-8a3a-fbf6062195d2 · outbound

This paper cites LISA: Reasoning Segmentation via Large Language Model.

VespaSeg: A Resource-Aware Ground-then-Segment Pipeline for Referring Expression Segmentation LISA: Reasoning Segmentation via Large Language Model

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-15T15:15:10.307291Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T15:15:10.307291Z digest=sha256:cb01682fc8adc40aa346fb0aa79137d3cb37a86277be5a1c40df55b17d97fc5c

Observation 3f6b9e4f-0f84-4003-a0d1-148eb3001d2d · outbound

This paper cites Microsoft COCO: Common Objects in Context.

VespaSeg: A Resource-Aware Ground-then-Segment Pipeline for Referring Expression Segmentation Microsoft COCO: Common Objects in Context

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-15T15:15:10.311268Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T15:15:10.311268Z digest=sha256:22972c451c1c9a230b44f511cadd0c431b8d83d5da17a03104fb1f7f9bd29601

Observation 7695ad2f-1b46-4689-ab1e-ac93434a7212 · outbound

This paper cites Decoupled Weight Decay Regularization.

VespaSeg: A Resource-Aware Ground-then-Segment Pipeline for Referring Expression Segmentation Decoupled Weight Decay Regularization

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-15T15:15:10.315598Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T15:15:10.315598Z digest=sha256:c70e93184e942d13428c99b7a4706324b5e60304a2904938e810926348b1b84e

Observation 62daa215-5ece-4aab-bdb6-7b5c8efaca9b · outbound

This paper cites an unresolved cited work.

VespaSeg: A Resource-Aware Ground-then-Segment Pipeline for Referring Expression Segmentation Unresolved cited work

Reference 7

Resolution
unresolved
raw_fallback, observed 2026-08-15T15:15:10.586290Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T15:15:10.319539Z digest=sha256:cebddd89543c867dc6a96203d14b56bc3dfae775f3d2e945950c27695f87d68a

Observation 70bac72a-b5e9-442f-9e4c-f3ea02b13e19 · outbound

This paper cites CRIS: CLIP-Driven Referring Image Segmentation.

VespaSeg: A Resource-Aware Ground-then-Segment Pipeline for Referring Expression Segmentation CRIS: CLIP-Driven Referring Image Segmentation

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-15T15:15:10.322922Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T15:15:10.322922Z digest=sha256:e87d703a0e5f0f352a004515730db7030fcbcb5341ef2e23158c4d26a765b58b

Observation d2537655-5c50-478d-9d2b-61bb105583a8 · outbound

This paper cites TinyViT: Fast Pretraining Distillation for Small Vision Transformers.

VespaSeg: A Resource-Aware Ground-then-Segment Pipeline for Referring Expression Segmentation TinyViT: Fast Pretraining Distillation for Small Vision Transformers

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-15T15:15:10.326386Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T15:15:10.326386Z digest=sha256:fc244b07a41b59a1abfd084dbdc06665e1ec9433f7a7bf880df59a48a760d0c9

Observation 98be8d4a-0865-4403-bd6c-e7b840b7780f · outbound

This paper cites Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks.

VespaSeg: A Resource-Aware Ground-then-Segment Pipeline for Referring Expression Segmentation Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-15T15:15:10.330227Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T15:15:10.330227Z digest=sha256:aa44cec5975010e3109c9dc9c6cbea02ae2da13d16e799796382efe10b34543c

Observation c8aab9fe-0d3b-492f-9cb2-2752dea4d230 · outbound

This paper cites LAVT: Language-Aware Vision Transformer for Referring Image Segmentation.

VespaSeg: A Resource-Aware Ground-then-Segment Pipeline for Referring Expression Segmentation LAVT: Language-Aware Vision Transformer for Referring Image Segmentation

Reference 11

Resolution
verified exact
local_arxiv, observed 2026-08-15T15:15:10.421695Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T15:15:10.333552Z digest=sha256:4ad28e29a9f0897dc278ec582aba10bd6fbb8244afe4d2144b7aede6b0b0f62c

Observation 1d57b678-fb09-42fb-a3e0-9420b9555cea · outbound

This paper cites Berg, and Tamara L.

VespaSeg: A Resource-Aware Ground-then-Segment Pipeline for Referring Expression Segmentation Berg, and Tamara L

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-15T15:15:10.337098Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T15:15:10.337098Z digest=sha256:b9141a2debfd83232313e36132ced35064ccdc7a2b2f5eadfc2e1fce13527094

Observation d960672e-a78c-44fa-a661-8b3654ae7976 · outbound

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

VespaSeg: A Resource-Aware Ground-then-Segment Pipeline for Referring Expression Segmentation Faster Segment Anything: Towards Lightweight SAM for Mobile Applications

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-15T15:15:10.344169Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T15:15:10.344169Z digest=sha256:c90fec1b11ffe98a36294f68f2bf064ea792d066d04868db91521939fb878359

Observation f353c423-f385-4a9a-bbfd-89f66e99cce6 · outbound

This paper cites EVF-SAM: Early Vision-Language Fusion for Text-Prompted Segment Anything Model.

VespaSeg: A Resource-Aware Ground-then-Segment Pipeline for Referring Expression Segmentation EVF-SAM: Early Vision-Language Fusion for Text-Prompted Segment Anything Model

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-15T15:15:10.347638Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T15:15:10.347638Z digest=sha256:9e467408f8ed0060fd2a4f3351fe8b28739019c5bac82b8ff131b0152150bb52

Observation 55de1dab-8de0-4b83-bceb-9b358103fb5c · outbound

This paper cites Modeling Context in Referring Expressions.

VespaSeg: A Resource-Aware Ground-then-Segment Pipeline for Referring Expression Segmentation Modeling Context in Referring Expressions

Reference 2016

Resolution
unresolved
no resolver link, observed 2026-08-15T15:15:10.340740Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T15:15:10.340740Z digest=sha256:b6b7e87ccdf1608aa1bd4498b7e5266079f3a104b786eb5d23f99a7f2582138b

Pith citing papers

No inbound Pith citation observations are available.