Paper Citation Record · LEDGER

Advancing TDFN: Precise Fixation Point Generation Using Reconstruction Differences

As of 11 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:2501.15603.

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

pith.paper-citation-record.v1
2501.15603 v1

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T14:11:55.123127Z

measured 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+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

22 of 22 outbound references displayed

verified exact1
verified fuzzy12
unresolved9
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation cf956962-9335-4a20-a313-be25ca3871c0 · outbound

This paper cites Quantifying Attention Flow in Transformers.

Advancing TDFN: Precise Fixation Point Generation Using Reconstruction Differences Quantifying Attention Flow in Transformers

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-10T14:11:55.016082Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dda74636-ef71-4bc4-88a6-c30e6fdbf836 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Advancing TDFN: Precise Fixation Point Generation Using Reconstruction Differences An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-10T14:11:55.022582Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3b14e2fa-b739-4298-a97b-52aac65869c6 · outbound

This paper cites Decision- Theoretic Saliency : Computational Principles , Biological Plausibility , and Implications for Neurophysiology and Psychophysics.

Advancing TDFN: Precise Fixation Point Generation Using Reconstruction Differences Decision- Theoretic Saliency : Computational Principles , Biological Plausibility , and Implications for Neurophysiology and Psychophysics

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:11:55.476380Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 89666c98-38eb-4758-9b1b-9a72da6e976e · outbound

This paper cites SALICON : Reducing the Semantic Gap in Saliency Prediction by Adapting Deep Neural Networks.

Advancing TDFN: Precise Fixation Point Generation Using Reconstruction Differences SALICON : Reducing the Semantic Gap in Saliency Prediction by Adapting Deep Neural Networks

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:11:55.463355Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 7005875a-c1a4-474e-bf11-24de3f0f8134 · outbound

This paper cites an unresolved cited work.

Advancing TDFN: Precise Fixation Point Generation Using Reconstruction Differences Unresolved cited work

Reference 5

Resolution: unresolved; raw_fallback, observed 2026-08-10T14:11:55.448669Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 4bbdd7e0-0743-4493-ae5b-b571bb025549 · outbound

This paper cites Koch and S.

Advancing TDFN: Precise Fixation Point Generation Using Reconstruction Differences Koch and S

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:11:55.437121Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f7642cc5-0d2a-4768-82d3-1b2c5f1bb69b · outbound

This paper cites Deep Gaze I: Boosting Saliency Prediction with Feature Maps Trained on ImageNet.

Advancing TDFN: Precise Fixation Point Generation Using Reconstruction Differences Deep Gaze I: Boosting Saliency Prediction with Feature Maps Trained on ImageNet

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-10T14:11:55.049530Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bf76e355-bdfc-44bc-be69-1520d61a462a · outbound

This paper cites DeepGaze II: Reading fixations from deep features trained on object recognition.

Advancing TDFN: Precise Fixation Point Generation Using Reconstruction Differences DeepGaze II: Reading fixations from deep features trained on object recognition

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-10T14:11:55.054513Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fff12de0-bd08-4c21-bbc9-564bed3b12e3 · outbound

This paper cites Oliva, A.

Advancing TDFN: Precise Fixation Point Generation Using Reconstruction Differences Oliva, A

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:11:55.424438Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation cc3a59bc-b978-41bc-a924-ab2b6a176e20 · outbound

This paper cites SalGAN: Visual Saliency Prediction with Generative Adversarial Networks.

Advancing TDFN: Precise Fixation Point Generation Using Reconstruction Differences SalGAN: Visual Saliency Prediction with Generative Adversarial Networks

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-10T14:11:55.063478Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ab20c6b8-800b-4564-8cec-9fc609da8684 · outbound

This paper cites Peters and Laurent Itti.

Advancing TDFN: Precise Fixation Point Generation Using Reconstruction Differences Peters and Laurent Itti

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:11:55.411640Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 836c8f1a-115b-4147-a234-fc1133d9aa54 · outbound

This paper cites Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps.

Advancing TDFN: Precise Fixation Point Generation Using Reconstruction Differences Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-10T14:11:55.072574Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a1e1708a-ee46-4182-9ad7-65e6868cd7ec · outbound

This paper cites Treisman and Garry Gelade.

Advancing TDFN: Precise Fixation Point Generation Using Reconstruction Differences Treisman and Garry Gelade

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:11:55.396663Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 3f626c71-f6cd-4272-8fb6-e151930b4fe2 · outbound

This paper cites Attention Is All You Need.

Advancing TDFN: Precise Fixation Point Generation Using Reconstruction Differences Attention Is All You Need

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-10T14:11:55.080822Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 04d18b72-4202-4f28-821f-73c8b957bdbd · outbound

This paper cites Large- Scale Optimization of Hierarchical Features for Saliency Prediction in Natural Images.

Advancing TDFN: Precise Fixation Point Generation Using Reconstruction Differences Large- Scale Optimization of Hierarchical Features for Saliency Prediction in Natural Images

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:11:55.383632Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a4e92461-dcfa-43c5-8352-466588c57314 · outbound

This paper cites Task-Driven Fixation Network: An Efficient Architecture with Fixation Selection.

Advancing TDFN: Precise Fixation Point Generation Using Reconstruction Differences Task-Driven Fixation Network: An Efficient Architecture with Fixation Selection

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-08-10T14:11:55.195630Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 9beb365b-9002-43a1-93ca-c76345512619 · outbound

This paper cites Inferring Salient Objects from Human Fixations.

Advancing TDFN: Precise Fixation Point Generation Using Reconstruction Differences Inferring Salient Objects from Human Fixations

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:11:55.371043Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 18492ede-7a4a-4434-8b1b-5b920aa5ed84 · outbound

This paper cites Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions.

Advancing TDFN: Precise Fixation Point Generation Using Reconstruction Differences Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-10T14:11:55.098506Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 47543876-ef74-427b-b157-73497257fdc8 · outbound

This paper cites Review of Visual Saliency Prediction : Development Process from Neurobiological Basis to Deep Models.

Advancing TDFN: Precise Fixation Point Generation Using Reconstruction Differences Review of Visual Saliency Prediction : Development Process from Neurobiological Basis to Deep Models

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:11:55.357462Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation b9ffe061-9a83-46bf-96e5-0e7c22b3655b · outbound

This paper cites Bayesian Saliency via Low and Mid Level Cues.

Advancing TDFN: Precise Fixation Point Generation Using Reconstruction Differences Bayesian Saliency via Low and Mid Level Cues

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:11:55.345018Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation b7796ac7-47a6-43a3-941f-6c55d4ec605c · outbound

This paper cites Tong, Tim K.

Advancing TDFN: Precise Fixation Point Generation Using Reconstruction Differences Tong, Tim K

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:11:55.332613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 0bc59d72-4b6e-4559-afd2-fa724ff678a7 · outbound

This paper cites Learning Deep Features for Discriminative Localization.

Advancing TDFN: Precise Fixation Point Generation Using Reconstruction Differences Learning Deep Features for Discriminative Localization

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:11:55.316788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Pith citing papers

No inbound Pith citation observations are available.