Paper Citation Record · LEDGER

Speed-up of Vision Transformer Models by Attention-aware Token Filtering

As of 8 August 2026, this Paper Citation Record lists 10 of 10 outbound references and 1 inbound Pith citation observation for arXiv:2506.01519.

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

pith.paper-citation-record.v1
2506.01519 v1

Coverage vector

measured 10 of 10 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:45:38.488796Z

measured 11 of 11 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-08T14:55:14.443863Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T18:36:09.118436Z

Reference resolution

10 of 10 outbound references displayed

verified exact0
verified fuzzy9
unresolved1
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1da65384-bf2b-4f70-9124-460792beeed2 · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

Speed-up of Vision Transformer Models by Attention-aware Token Filtering An image is worth 16x16 words: Transformers for image recognition at scale

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:46:58.292790Z

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.

Observation 49d7ed3d-4c1f-4c44-ae46-04ad7d014e97 · outbound

This paper cites Self- support few-shot semantic segmentation.

Speed-up of Vision Transformer Models by Attention-aware Token Filtering Self- support few-shot semantic segmentation

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:46:58.255749Z

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.

Observation ff6becbd-4ddd-4c28-9f86-c252d6282a39 · outbound

This paper cites Deep residual learning for image recognition.

Speed-up of Vision Transformer Models by Attention-aware Token Filtering Deep residual learning for image recognition

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T11:45:38.360395Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation baf1294f-600a-4ce8-a144-ed2fefc9c090 · outbound

This paper cites TextOCR: Towards large- scale end-to-end reasoning for arbitrary-shaped scene text.

Speed-up of Vision Transformer Models by Attention-aware Token Filtering TextOCR: Towards large- scale end-to-end reasoning for arbitrary-shaped scene text

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:46:58.203522Z

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.

Observation 06bb5ac2-1afd-491b-8c9f-752d13440551 · outbound

This paper cites Training data-efficient image transformers & distillation through at- tention.

Speed-up of Vision Transformer Models by Attention-aware Token Filtering Training data-efficient image transformers & distillation through at- tention

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:46:58.164762Z

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.

Observation 9ff10201-00be-4457-9df1-8b81a9bac819 · outbound

This paper cites Attention is all you need.

Speed-up of Vision Transformer Models by Attention-aware Token Filtering Attention is all you need

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:46:58.126136Z

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.

Observation d760dc07-99a2-477a-876d-3fe6a7bf3c6e · outbound

This paper cites Alvarez, Arun Mallya, Jan Kautz, and Pavlo Molchanov.

Speed-up of Vision Transformer Models by Attention-aware Token Filtering Alvarez, Arun Mallya, Jan Kautz, and Pavlo Molchanov

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:46:58.057829Z

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.

Observation 7a7cdcb0-9e6c-4548-9b6e-54f787788243 · outbound

This paper cites Scaling vision transformers.

Speed-up of Vision Transformer Models by Attention-aware Token Filtering Scaling vision transformers

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:46:57.975447Z

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.

Observation edd70139-a132-4940-9d7a-5052c6c79b32 · outbound

This paper cites Sigmoid loss for language image pre-training.

Speed-up of Vision Transformer Models by Attention-aware Token Filtering Sigmoid loss for language image pre-training

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:46:57.913746Z

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.

Observation 8a39d86f-2b83-4a84-90c1-8b0e9bfa7931 · outbound

This paper cites Pyramid scene parsing network.

Speed-up of Vision Transformer Models by Attention-aware Token Filtering Pyramid scene parsing network

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:46:21.633062Z

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.

Pith citing papers

Observation c389c818-723b-4317-a021-f0ec666b7b20 · inbound

Na-IRSTD: Enhancing Infrared Small Target Detection via Native-Resolution Feature Selection and Fusion cites this paper.

Na-IRSTD: Enhancing Infrared Small Target Detection via Native-Resolution Feature Selection and Fusion Speed-up of Vision Transformer Models by Attention-aware Token Filtering

Reference 45

Resolution: verified exact; arxiv_id, observed 2026-05-11T18:36:09.120166Z

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.