Pith. sign in

Paper Citation Record · LEDGER

TACO: Trash Annotations in Context for Litter Detection

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2003.06975.

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

pith.paper-citation-record.v1
2003.06975 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:07:56.649851Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T15:58:37.365140Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 9db9946b-ea74-41c0-ad1c-6a7262d9fee1 · inbound

A Roadmap for Climate-Relevant Robotics Research cites this paper.

A Roadmap for Climate-Relevant Robotics Research TACO: Trash Annotations in Context for Litter Detection

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-06T17:07:56.649851Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T17:07:56.649851Z digest=sha256:16137d1b673e62a03b6216e4972874790275646afba458940942b2fc098c0cd1

Observation f9658f0c-dd5d-4da4-9044-43a74dfb29f6 · inbound

Robust and Label-Efficient Deep Waste Detection cites this paper.

Robust and Label-Efficient Deep Waste Detection TACO: Trash Annotations in Context for Litter Detection

Reference 2020

Resolution
unresolved
no resolver link, observed 2026-08-05T16:15:07.084746Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T16:15:07.084746Z digest=sha256:ae3eac42a859fbba8fb5a847ff43d7042a3a9dac64097d01854100008b59c382

Observation 6fca63fd-18a4-47e5-865c-6908ec54cb8c · inbound

WS$^2$: Weakly Supervised Segmentation using Before-After Supervision in Waste Sorting cites this paper.

WS$^2$: Weakly Supervised Segmentation using Before-After Supervision in Waste Sorting TACO: Trash Annotations in Context for Litter Detection

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-04T23:33:21.195060Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T23:33:21.195060Z digest=sha256:4368152edf53c0dbf950c62f2992306205366de2d7df4abd604b52f25ec6b69d

Observation 01c69c99-10f2-45cf-983a-bec268ba38f1 · inbound

YOLO-SAT: A Data-based and Model-based Enhanced YOLOv12 Model for Desert Waste Detection and Classification cites this paper.

YOLO-SAT: A Data-based and Model-based Enhanced YOLOv12 Model for Desert Waste Detection and Classification TACO: Trash Annotations in Context for Litter Detection

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-03T23:51:48.948366Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T23:51:48.948366Z digest=sha256:25144b9dd4f5b2fe1813f64f2c007c81d40905bb4d11d7b989fcc91b2436b449

Observation 99af21e8-fbec-415a-855c-33d773e7d680 · inbound

SteelDS: A High-Resolution Video Dataset of E40 Steel Scrap for Object Detection and Instance Segmentation cites this paper.

SteelDS: A High-Resolution Video Dataset of E40 Steel Scrap for Object Detection and Instance Segmentation TACO: Trash Annotations in Context for Litter Detection

Reference 4

Resolution
verified exact
arxiv_id, observed 2026-06-29T17:13:44.545567Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-06-29T17:11:50.483337Z digest=sha256:1fca153ef89995777519c829302a5e70020686a95438813fce9a2852b52ae767

Observation bbf15354-b3f1-4495-87d7-ed01421ef685 · inbound

Towards Effective Waste Segmentation for Automated Waste Recycling in Cluttered Background cites this paper.

Towards Effective Waste Segmentation for Automated Waste Recycling in Cluttered Background TACO: Trash Annotations in Context for Litter Detection

Reference 61

Resolution
metadata mismatch
arxiv_id, observed 2026-07-03T14:28:31.427672Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-06-27T07:04:14.194680Z digest=sha256:ef7e438c9485ad002775c92919d698cfbd196970d71195802e8c585c10aa3594

Observation 8949b8d7-227c-468f-9adb-cba76ded6682 · inbound

Efficient Waste Sorting for Circular Economy: A Confidence-guided comparison between One-Vs-All and One-Vs-Rest Classification Strategies with Human-in-the-Loop for Automated Waste Sorting cites this paper.

Efficient Waste Sorting for Circular Economy: A Confidence-guided comparison between One-Vs-All and One-Vs-Rest Classification Strategies with Human-in-the-Loop for Automated Waste Sorting TACO: Trash Annotations in Context for Litter Detection

Reference 1

Resolution
verified exact
arxiv_id, observed 2026-07-03T15:58:37.366812Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-07-03T15:54:05.239116Z digest=sha256:3a79aef8cee056de5fdaa26ced1fac1814cf4ee7cc24608708b29125a3333229

Observation 30a4c9f0-8367-4bac-b34a-a050642c592d · inbound

Synthetic and Derived Training Images for Campus Waste Detection: A Multi-Seed Evaluation with YOLOv8n cites this paper.

Synthetic and Derived Training Images for Campus Waste Detection: A Multi-Seed Evaluation with YOLOv8n TACO: Trash Annotations in Context for Litter Detection

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-01T12:30:53.707042Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T12:30:53.707042Z digest=sha256:b704b8baee243e801f0ff947de31bb54a15dc9081168d20cff3165b683629e30

Observation c42dfd64-5513-45a2-8f8c-6ba72aff4a1a · inbound

MDWD: A Street-Level Dataset for Municipal Solid Waste Detection in Dense Urban Environments cites this paper.

MDWD: A Street-Level Dataset for Municipal Solid Waste Detection in Dense Urban Environments TACO: Trash Annotations in Context for Litter Detection

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-04T00:56:20.241334Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T00:56:20.241334Z digest=sha256:37ec4420f496e7fbfcaf64f1f5a1a8e78d8758bf3454be85a6a18f9231599ec8