Paper Citation Record · LEDGER

Learning to Segment Liquids in Real-world Images

As of 20 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 0 inbound Pith citation observations for arXiv:2601.00940.

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

pith.paper-citation-record.v1
2601.00940 v2

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T13:02:21.038660Z

measured 24 of 24 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

24 of 24 outbound references displayed

verified exact0
verified fuzzy0
unresolved24
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation eabfe4a0-25b4-415d-8c39-2c453e128a8e · outbound

This paper cites DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs.

Learning to Segment Liquids in Real-world Images DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-03T13:02:17.761172Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 861c5193-6a12-4eef-a4af-a0bf38446db9 · outbound

This paper cites Encoder-decoder with atrous separable convolution for semantic image segmentation.

Learning to Segment Liquids in Real-world Images Encoder-decoder with atrous separable convolution for semantic image segmentation

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-03T13:02:17.842367Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5b39af31-1320-48d4-8626-3fa2c49627ad · outbound

This paper cites Per- pixel classification is not all you need for semantic segmenta- tion.

Learning to Segment Liquids in Real-world Images Per- pixel classification is not all you need for semantic segmenta- tion

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-03T13:02:18.000395Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a4b5e9af-e4fd-4a70-9fdf-3d4099b34185 · outbound

This paper cites Schwing, Alexan- der Kirillov, and Rohit Girdhar.

Learning to Segment Liquids in Real-world Images Schwing, Alexan- der Kirillov, and Rohit Girdhar

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-03T13:02:18.059109Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1427d397-24c8-4484-8e94-5654f3022956 · outbound

This paper cites Adaptive pyramid context network for semantic seg- mentation.

Learning to Segment Liquids in Real-world Images Adaptive pyramid context network for semantic seg- mentation

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-03T13:02:18.172257Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e99cd5ca-3f49-4188-9a5b-252328d721c6 · outbound

This paper cites Ccnet: Criss-cross attention for semantic segmentation.

Learning to Segment Liquids in Real-world Images Ccnet: Criss-cross attention for semantic segmentation

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-03T13:02:18.322705Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5b038b28-e549-4728-a867-46568e2fbaed · outbound

This paper cites Habaek: High- performance water segmentation through dataset expansion and inductive bias optimization, 2024.

Learning to Segment Liquids in Real-world Images Habaek: High- performance water segmentation through dataset expansion and inductive bias optimization, 2024

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-03T13:02:18.484768Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6304e262-5dd2-46ad-bc67-2dd756074fef · outbound

This paper cites Your ViT is Secretly an Image Segmentation Model.

Learning to Segment Liquids in Real-world Images Your ViT is Secretly an Image Segmentation Model

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-03T13:02:18.603356Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2abdfba1-2e28-4b35-8c00-13f48289a2a7 · outbound

This paper cites Waternet: An adaptive matching pipeline for segmenting water with volatile appearance.Computational Visual Media, 6(1):65–78, 2020.

Learning to Segment Liquids in Real-world Images Waternet: An adaptive matching pipeline for segmenting water with volatile appearance.Computational Visual Media, 6(1):65–78, 2020

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-03T13:02:18.751665Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation feaffa13-3ff0-4ec0-98b8-436eb33f2029 · outbound

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

Learning to Segment Liquids in Real-world Images Swin transformer: Hierarchical vision transformer using shifted windows

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-03T13:02:18.872084Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2f2c1b6c-0bb0-4a6f-8901-544717aedb2f · outbound

This paper cites Fully convolutional networks for semantic segmentation.

Learning to Segment Liquids in Real-world Images Fully convolutional networks for semantic segmentation

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-03T13:02:19.028045Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9c836ed5-df0c-4362-aecf-570aced56f8b · outbound

This paper cites an unresolved cited work.

Learning to Segment Liquids in Real-world Images Unresolved cited work

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-03T13:02:19.138458Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3b039c96-e146-4aac-b730-83e2dfb663d1 · outbound

This paper cites Perceiving and reason- ing about liquids using fully convolutional networks.Int.

Learning to Segment Liquids in Real-world Images Perceiving and reason- ing about liquids using fully convolutional networks.Int

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-03T13:02:19.254900Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c4528d76-4fe4-434a-8e6b-b9050796ae2c · outbound

This paper cites Vision-based robot manipulation of transparent liquid containers in a laboratory setting, 2024.

Learning to Segment Liquids in Real-world Images Vision-based robot manipulation of transparent liquid containers in a laboratory setting, 2024

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-03T13:02:19.435130Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0a70c55c-32b2-4e33-9b3c-beb1ffca9118 · outbound

This paper cites Segmenter: Transformer for semantic segmentation.

Learning to Segment Liquids in Real-world Images Segmenter: Transformer for semantic segmentation

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-03T13:02:19.616291Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c595a6f0-aa96-49b1-8519-d03ca5bf09e9 · outbound

This paper cites SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features.

Learning to Segment Liquids in Real-world Images SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-03T13:02:19.731262Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dacda3ec-543c-4db0-8cde-141ad515f087 · outbound

This paper cites Segmenting transparent objects in the wild.

Learning to Segment Liquids in Real-world Images Segmenting transparent objects in the wild

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-03T13:02:19.956235Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b4a4a744-75be-44c5-a050-6e089a471785 · outbound

This paper cites Segmenting transparent object in the wild with transformer.

Learning to Segment Liquids in Real-world Images Segmenting transparent object in the wild with transformer

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-03T13:02:20.115109Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5685b8e9-25aa-41d4-99f6-812e168572c1 · outbound

This paper cites Alvarez, and Ping Luo.

Learning to Segment Liquids in Real-world Images Alvarez, and Ping Luo

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-03T13:02:20.268583Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d8b3fe87-9a1b-4580-97f0-c9d629296f02 · outbound

This paper cites an unresolved cited work.

Learning to Segment Liquids in Real-world Images Unresolved cited work

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-03T13:02:20.474205Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9b626334-c1a3-443f-85c7-ca79092c9e3c · outbound

This paper cites K-net: Towards unified image segmentation.

Learning to Segment Liquids in Real-world Images K-net: Towards unified image segmentation

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-03T13:02:20.639638Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e73e47cc-62a9-46ba-9c0c-7d5eada050ce · outbound

This paper cites Pyramid scene parsing network.

Learning to Segment Liquids in Real-world Images Pyramid scene parsing network

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-03T13:02:20.739335Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f637c35e-2d63-41c2-8422-149426202512 · outbound

This paper cites Scene parsing through ade20k dataset.

Learning to Segment Liquids in Real-world Images Scene parsing through ade20k dataset

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-03T13:02:20.891876Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e42c0526-ffac-4cb8-af78-b65d71ca71e6 · outbound

This paper cites Asymmetric non-local neural networks for semantic segmentation.

Learning to Segment Liquids in Real-world Images Asymmetric non-local neural networks for semantic segmentation

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-03T13:02:21.038660Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.