Paper Citation Record · LEDGER

Predicting Video Saliency with Object-to-Motion CNN and Two-layer Convolutional LSTM

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:1709.06316.

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

pith.paper-citation-record.v1
1709.06316 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T14:08:41.151037Z

measured 1 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

69
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 52e7e35c-c49d-47ea-857e-7f92c27fd006 · inbound

Predicting video saliency using crowdsourced mouse-tracking data cites this paper.

Predicting video saliency using crowdsourced mouse-tracking data Predicting Video Saliency with Object-to-Motion CNN and Two-layer Convolutional LSTM

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-05-25T12:25:48.015888Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9bb8c33d-3884-46a8-a04d-7bf2dfaf54ae · inbound

Simple vs complex temporal recurrences for video saliency prediction cites this paper.

Simple vs complex temporal recurrences for video saliency prediction Predicting Video Saliency with Object-to-Motion CNN and Two-layer Convolutional LSTM

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-05-25T10:36:54.208689Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0760e796-0f6e-4ba6-bac3-8c6e5a3d77f3 · inbound

Exploiting temporal consistency for real-time video depth estimation cites this paper.

Exploiting temporal consistency for real-time video depth estimation Predicting Video Saliency with Object-to-Motion CNN and Two-layer Convolutional LSTM

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-14T14:08:41.151037Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2ec4a66c-8df8-46b0-a98b-ccf9e2879977 · inbound

TASED-Net: Temporally-Aggregating Spatial Encoder-Decoder Network for Video Saliency Detection cites this paper.

TASED-Net: Temporally-Aggregating Spatial Encoder-Decoder Network for Video Saliency Detection Predicting Video Saliency with Object-to-Motion CNN and Two-layer Convolutional LSTM

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-14T13:07:52.117520Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ac582457-184f-4e59-8199-8d7bb8bb4535 · inbound

ViASNet: A Video Ad Saliency Network for Predicting Dynamic Saliency and Viewer Engagement cites this paper.

ViASNet: A Video Ad Saliency Network for Predicting Dynamic Saliency and Viewer Engagement Predicting Video Saliency with Object-to-Motion CNN and Two-layer Convolutional LSTM

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-06-29T08:53:15.432525Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.