Paper Citation Record · LEDGER

Exploiting Temporality for Semi-Supervised Video Segmentation

As of 15 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:1908.11309.

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

pith.paper-citation-record.v1
1908.11309 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T10:22:05.910169Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+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

28 of 28 outbound references displayed

verified exact6
verified fuzzy3
unresolved19
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 155d870d-ebfb-4b2c-ba51-763689626338 · outbound

This paper cites SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation.

Exploiting Temporality for Semi-Supervised Video Segmentation SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-14T10:22:05.777189Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1fdf1a99-4bc3-4abd-9515-57cd7b00e092 · outbound

This paper cites An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling.

Exploiting Temporality for Semi-Supervised Video Segmentation An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-14T10:22:05.782974Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 46aa8142-f88e-4456-b673-03237d4a362c · outbound

This paper cites Delving Deeper into Convolutional Networks for Learning Video Representations.

Exploiting Temporality for Semi-Supervised Video Segmentation Delving Deeper into Convolutional Networks for Learning Video Representations

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-14T10:22:05.788502Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a32f0100-4f8d-41f0-9cbb-e2e09b35f062 · outbound

This paper cites Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation.

Exploiting Temporality for Semi-Supervised Video Segmentation Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-14T10:22:05.793559Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d526bc2f-b508-47c6-9e1b-c371bf032936 · outbound

This paper cites SegFlow: Joint Learning for Video Object Segmentation and Optical Flow.

Exploiting Temporality for Semi-Supervised Video Segmentation SegFlow: Joint Learning for Video Object Segmentation and Optical Flow

Reference 5

Resolution: verified exact; local_arxiv, observed 2026-08-14T10:22:06.289281Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 343b16df-bbd3-4440-b88e-d96f626d872c · outbound

This paper cites The Cityscapes Dataset for Semantic Urban Scene Understanding.

Exploiting Temporality for Semi-Supervised Video Segmentation The Cityscapes Dataset for Semantic Urban Scene Understanding

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-14T10:22:05.805765Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2318635f-984f-487c-a323-64c5cee407c1 · outbound

This paper cites STFCN: Spatio-Temporal FCN for Semantic Video Segmentation.

Exploiting Temporality for Semi-Supervised Video Segmentation STFCN: Spatio-Temporal FCN for Semantic Video Segmentation

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-08-14T10:22:06.241545Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 83a97a5c-6f6f-471f-ac4c-464b5e673dee · outbound

This paper cites Deep Residual Learning for Image Recognition.

Exploiting Temporality for Semi-Supervised Video Segmentation Deep Residual Learning for Image Recognition

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-14T10:22:05.816350Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b9072088-8902-48d8-9f0c-5a885f290d2b · outbound

This paper cites Hochreiter and J.

Exploiting Temporality for Semi-Supervised Video Segmentation Hochreiter and J

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:22:06.569659Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c4c2dfcf-8567-4af3-84fb-7bbd0dd42ac5 · outbound

This paper cites Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift.

Exploiting Temporality for Semi-Supervised Video Segmentation Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-14T10:22:05.825764Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4bafeaa0-9210-4433-a711-994080a7118d · outbound

This paper cites FusionSeg: Learning to combine motion and appearance for fully automatic segmention of generic objects in videos.

Exploiting Temporality for Semi-Supervised Video Segmentation FusionSeg: Learning to combine motion and appearance for fully automatic segmention of generic objects in videos

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-14T10:22:05.830639Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7afc91a2-f147-442a-be4a-81b597e2d0b3 · outbound

This paper cites an unresolved cited work.

Exploiting Temporality for Semi-Supervised Video Segmentation Unresolved cited work

Reference 12

Resolution: unresolved; raw_fallback, observed 2026-08-14T10:22:06.554013Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 6e805679-05a1-45bc-b170-662bca487f9f · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Exploiting Temporality for Semi-Supervised Video Segmentation Adam: A Method for Stochastic Optimization

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-14T10:22:05.840209Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9cff567e-9d0c-4216-ac60-a012b06e24b6 · outbound

This paper cites Temporal Convolutional Networks for Action Segmentation and Detection.

Exploiting Temporality for Semi-Supervised Video Segmentation Temporal Convolutional Networks for Action Segmentation and Detection

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-14T10:22:05.844878Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1d6b192b-c9af-4405-824d-7caec2966349 · outbound

This paper cites Microsoft COCO: Common Objects in Context.

Exploiting Temporality for Semi-Supervised Video Segmentation Microsoft COCO: Common Objects in Context

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-14T10:22:05.850002Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 48adabcd-1d60-413c-a16c-c08ff236786a · outbound

This paper cites Nair and G.

Exploiting Temporality for Semi-Supervised Video Segmentation Nair and G

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:22:06.537876Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 9b8cd0d9-9ffe-4158-bb3f-7b92af20c0a7 · outbound

This paper cites Semantic Video Segmentation by Gated Recurrent Flow Propagation.

Exploiting Temporality for Semi-Supervised Video Segmentation Semantic Video Segmentation by Gated Recurrent Flow Propagation

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-08-14T10:22:06.129683Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 24c9f2d7-f709-4102-b4af-19313837b9e0 · outbound

This paper cites ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation.

Exploiting Temporality for Semi-Supervised Video Segmentation ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-14T10:22:05.862652Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 63642e82-f013-4dd0-a48e-d25233739b0a · outbound

This paper cites Fast-SCNN: Fast Semantic Segmentation Network.

Exploiting Temporality for Semi-Supervised Video Segmentation Fast-SCNN: Fast Semantic Segmentation Network

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-14T10:22:05.867732Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6404f35b-ac53-4848-9f9b-be716716a8c9 · outbound

This paper cites U-Net: Convolutional Networks for Biomedical Image Segmentation.

Exploiting Temporality for Semi-Supervised Video Segmentation U-Net: Convolutional Networks for Biomedical Image Segmentation

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-14T10:22:05.872249Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 00e47d7b-9077-4bf8-ab54-7afc09e87d42 · outbound

This paper cites Shelhamer, J.

Exploiting Temporality for Semi-Supervised Video Segmentation Shelhamer, J

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:22:06.477278Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 37a4c0fd-7e7c-49b9-8331-55cdc7542bc1 · outbound

This paper cites Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting.

Exploiting Temporality for Semi-Supervised Video Segmentation Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-14T10:22:05.881285Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 22e034e5-1711-455a-a73b-a3cc920a9958 · outbound

This paper cites Human Action Recognition using Factorized Spatio-Temporal Convolutional Networks.

Exploiting Temporality for Semi-Supervised Video Segmentation Human Action Recognition using Factorized Spatio-Temporal Convolutional Networks

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-08-14T10:22:06.049123Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5a01f1c5-2047-4937-aa3c-d987be30b482 · outbound

This paper cites Learning Video Object Segmentation with Visual Memory.

Exploiting Temporality for Semi-Supervised Video Segmentation Learning Video Object Segmentation with Visual Memory

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-08-14T10:22:06.024564Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f2010828-b372-4091-9555-9280edb7ab3e · outbound

This paper cites Show and Tell: A Neural Image Caption Generator.

Exploiting Temporality for Semi-Supervised Video Segmentation Show and Tell: A Neural Image Caption Generator

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-14T10:22:05.895954Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 402b6e49-892f-487e-b671-cdb2a18763ae · outbound

This paper cites CNN-RNN: A Unified Framework for Multi-label Image Classification.

Exploiting Temporality for Semi-Supervised Video Segmentation CNN-RNN: A Unified Framework for Multi-label Image Classification

Reference 26

Resolution: verified exact; local_arxiv, observed 2026-08-14T10:22:05.986511Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 34642a1c-2cb4-4a1a-b434-38464ace2b18 · outbound

This paper cites YouTube-VOS: A Large-Scale Video Object Segmentation Benchmark.

Exploiting Temporality for Semi-Supervised Video Segmentation YouTube-VOS: A Large-Scale Video Object Segmentation Benchmark

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-14T10:22:05.905315Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5ebd7a80-72e8-42f6-a3cd-454042240572 · outbound

This paper cites Pyramid Scene Parsing Network.

Exploiting Temporality for Semi-Supervised Video Segmentation Pyramid Scene Parsing Network

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-14T10:22:05.910169Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.