Pith. sign in

Paper Citation Record · LEDGER

Subjective Portrait Region Cropping in Landscape Videos with Temporal Annotation Smoothing

As of 13 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2604.24947.

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

pith.paper-citation-record.v1
2604.24947 v1

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-08T04:25:11.428981Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+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

32 of 32 outbound references displayed

  • verified exact1
  • verified fuzzy31
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 409ee0d3-e11c-4f91-a20b-d70855701c46 · outbound

This paper cites A fast smart-cropping method and dataset for video retargeting.

Subjective Portrait Region Cropping in Landscape Videos with Temporal Annotation Smoothing A fast smart-cropping method and dataset for video retargeting

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-05-26T21:03:01.986417Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-08T04:25:11.428981Z digest=sha256:3d35cc0eecb22a2276233f86f9e0701a84b7a343fc656a4cdb19f69ebb9f0cbb

Observation 42be40ba-a3d0-4c0b-a17b-0b1f40dd6eb8 · outbound

This paper cites Dynamic beauty is easy to find: A large-scale composition-aware dataset and an end-to- end framework for video reframing.

Subjective Portrait Region Cropping in Landscape Videos with Temporal Annotation Smoothing Dynamic beauty is easy to find: A large-scale composition-aware dataset and an end-to- end framework for video reframing

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-05-26T21:03:01.982150Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-08T04:25:11.428981Z digest=sha256:f8e24ed7a3986fc2c8f12d9582f6e6bf31f09964c25dc3033b8e2ab3cc86e661

Observation e8120cb1-b2c0-4c89-bbcd-89f03b6c9d0f · outbound

This paper cites Youtube ugc dataset for video compression research.

Subjective Portrait Region Cropping in Landscape Videos with Temporal Annotation Smoothing Youtube ugc dataset for video compression research

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-05-26T21:03:01.962123Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-08T04:25:11.428981Z digest=sha256:1138da03910b61d1dea900913c9abe03d60c868fb50bf9531749f0e7e01fa5e8

Observation 2f5f80c1-fdaa-4074-bf9e-412c00d01575 · outbound

This paper cites Patch-vq:’patching up’the video quality problem.

Subjective Portrait Region Cropping in Landscape Videos with Temporal Annotation Smoothing Patch-vq:’patching up’the video quality problem

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-05-26T21:03:01.970494Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-08T04:25:11.428981Z digest=sha256:7d4f3c2e22c39c5a6ba747335d990a7fa8c64cc28bbe3a1c9bba8f0d4ae3da23

Observation a381ed60-58c0-4ce4-ae0b-d564c1107ba1 · outbound

This paper cites A2-rl: Aesthetics aware reinforcement learning for image cropping.

Subjective Portrait Region Cropping in Landscape Videos with Temporal Annotation Smoothing A2-rl: Aesthetics aware reinforcement learning for image cropping

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-05-26T21:03:01.885036Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-08T04:25:11.428981Z digest=sha256:6103f97916c064aecb6d2dbabaad53e274f30a873f66fc39c849921537978f3b

Observation b8db78a1-6e27-47cd-b4da-d23ecce0ab49 · outbound

This paper cites Deep cropping via attention box prediction and aesthetics assessment.

Subjective Portrait Region Cropping in Landscape Videos with Temporal Annotation Smoothing Deep cropping via attention box prediction and aesthetics assessment

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-05-26T21:03:01.881225Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-08T04:25:11.428981Z digest=sha256:0d4f0b9f1f0383dc882eeac19bb7e40f86cd07a9096cb0a0f0d06109d477d559

Observation e883ec03-28ec-43f6-afe3-13924b53c168 · outbound

This paper cites Reliable and efficient image cropping: A grid anchor based approach.

Subjective Portrait Region Cropping in Landscape Videos with Temporal Annotation Smoothing Reliable and efficient image cropping: A grid anchor based approach

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-05-26T21:03:01.862118Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-08T04:25:11.428981Z digest=sha256:16e8accfdf9ee87e525742d7e5eee51758e095306b5ba62f76d4c1d5c73850a6

Observation 9e9fa17a-95b7-4647-926c-6a5f80152905 · outbound

This paper cites Fast-at: Fast automatic thumbnail generation using deep neural networks.

Subjective Portrait Region Cropping in Landscape Videos with Temporal Annotation Smoothing Fast-at: Fast automatic thumbnail generation using deep neural networks

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-05-26T21:03:01.928888Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-08T04:25:11.428981Z digest=sha256:dfe2361343bbf6859c370ae4d639bd21ad0dc8a783265cc6c97010407d0e67eb

Observation f217fdd0-4720-483a-bda0-642253723736 · outbound

This paper cites User constrained thumbnail generation using adaptive convolutions.

Subjective Portrait Region Cropping in Landscape Videos with Temporal Annotation Smoothing User constrained thumbnail generation using adaptive convolutions

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-05-26T21:03:01.954438Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-08T04:25:11.428981Z digest=sha256:7c691f9497d0ae956ec422eb1f242500414b2d0395f853dd8bcb6bc2a191e574

Observation ef031b82-66a5-4125-8951-af564a6b6f00 · outbound

This paper cites Learning to learn cropping models for different aspect ratio requirements.

Subjective Portrait Region Cropping in Landscape Videos with Temporal Annotation Smoothing Learning to learn cropping models for different aspect ratio requirements

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-05-26T21:03:01.974331Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-08T04:25:11.428981Z digest=sha256:7accfbd5da71555db20b1e9d186b61327bab2dfdad578c0de38afae0748ceefb

Observation 607a9be2-7184-4e51-8932-41a407ae8309 · outbound

This paper cites Non-homogeneous content- driven video-retargeting.

Subjective Portrait Region Cropping in Landscape Videos with Temporal Annotation Smoothing Non-homogeneous content- driven video-retargeting

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-05-26T21:03:01.876425Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-08T04:25:11.428981Z digest=sha256:fa56ecfb0e807f49cc75479877626698baa79728cb57bc8685b95c6076022be8

Observation 6686ec84-8d4e-4e94-9860-298dfa5bcc1d · outbound

This paper cites A system for retargeting of streaming video.

Subjective Portrait Region Cropping in Landscape Videos with Temporal Annotation Smoothing A system for retargeting of streaming video

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-05-26T21:03:01.941486Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-08T04:25:11.428981Z digest=sha256:94aac7ffd5944a7b37cbe9c7f13a91e8693c5bfb3a69c06cdfd0f34109e317d8

Observation 5f882a3d-8003-4453-91cf-84fd977cedab · outbound

This paper cites Improved seam carving for video retargeting.

Subjective Portrait Region Cropping in Landscape Videos with Temporal Annotation Smoothing Improved seam carving for video retargeting

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-05-26T21:03:01.937173Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-08T04:25:11.428981Z digest=sha256:ea078b865b1d79ce301bbb5bff3933e79c87df1508e5408b8c9e4078c31f4335

Observation 09ba9cca-89f4-4fd7-b232-e01154578c3c · outbound

This paper cites Multi-operator media retargeting.

Subjective Portrait Region Cropping in Landscape Videos with Temporal Annotation Smoothing Multi-operator media retargeting

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-05-26T21:03:01.893532Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-08T04:25:11.428981Z digest=sha256:0ec984545724da8876aaee9f142c10374c763e17a5fd82c73790c40cc07d117b

Observation 6cd2e3ad-f991-408d-9bc1-8adbd67a8ebc · outbound

This paper cites Yfcc100m: The new data in multimedia research.

Subjective Portrait Region Cropping in Landscape Videos with Temporal Annotation Smoothing Yfcc100m: The new data in multimedia research

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-05-26T21:03:01.912833Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-08T04:25:11.428981Z digest=sha256:c77696e4fa944bcd479de14488718fdb8410685ca71e4b8403eba8c3a6d5ebbb

Observation c5bdba65-f54a-4527-8432-c8d541be3fce · outbound

This paper cites Trigonometric interpolation of empirical and analytical functions.

Subjective Portrait Region Cropping in Landscape Videos with Temporal Annotation Smoothing Trigonometric interpolation of empirical and analytical functions

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-05-26T21:03:01.897369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-08T04:25:11.428981Z digest=sha256:204e2d010a4803412f2ce484068ddbdb40acc03902780826eba3017139453cca

Observation 6a35fbbc-8ab2-42f8-bd96-2c268c7aa6ff · outbound

This paper cites Revisiting video saliency: A large-scale benchmark and a new model.

Subjective Portrait Region Cropping in Landscape Videos with Temporal Annotation Smoothing Revisiting video saliency: A large-scale benchmark and a new model

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-05-26T21:03:01.889084Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-08T04:25:11.428981Z digest=sha256:fff9dfdb75327447f50ae4589c17c4939b5fbab24ed25f725042bbd9e490fcd7

Observation 15d8faae-f63c-404e-8009-0b8487011f1a · outbound

This paper cites Measuring colorfulness in natural images.

Subjective Portrait Region Cropping in Landscape Videos with Temporal Annotation Smoothing Measuring colorfulness in natural images

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-05-26T21:03:01.945766Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-08T04:25:11.428981Z digest=sha256:41ecb72e37d9822b582631a356c74481beac050cbcbe31d640ae3d5e6be71847

Observation 3a99fd1c-6bf3-4611-9f77-509194e7e779 · outbound

This paper cites Analysis of public image and video databases for quality assessment.

Subjective Portrait Region Cropping in Landscape Videos with Temporal Annotation Smoothing Analysis of public image and video databases for quality assessment

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-05-26T21:03:01.924824Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-08T04:25:11.428981Z digest=sha256:e6809e45466b9856450b25b56ab130df1fc85950dee49984439b2a00ecae5cc4

Observation f1b05d25-29a1-4296-9f05-1295520b5da8 · outbound

This paper cites Itu-t recommendation p.910: Subjective video quality assessment methods for multimedia applica- tions.

Subjective Portrait Region Cropping in Landscape Videos with Temporal Annotation Smoothing Itu-t recommendation p.910: Subjective video quality assessment methods for multimedia applica- tions

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-05-26T21:03:01.932492Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-08T04:25:11.428981Z digest=sha256:33ff8f2c155fd97b18a8029858167c697779da82859c708ebf7b5283b463982b

Observation 1bbb3da8-f3cd-4b40-afb7-e63f896a662a · outbound

This paper cites Lof: Identifying density- based local outliers.

Subjective Portrait Region Cropping in Landscape Videos with Temporal Annotation Smoothing Lof: Identifying density- based local outliers

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-05-26T21:03:01.867613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-08T04:25:11.428981Z digest=sha256:b2f79376ee7cf76b3113225693a07bbf2c4ba2ffabf257ad57425c7abf5704a7

Observation 165505fb-6907-4f52-b1ac-20128f824829 · outbound

This paper cites V olume 16: How to Detect and Handle Outliers.

Subjective Portrait Region Cropping in Landscape Videos with Temporal Annotation Smoothing V olume 16: How to Detect and Handle Outliers

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-05-26T21:03:01.905284Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-08T04:25:11.428981Z digest=sha256:0133c92681e809f9defa565abab87259b1be79b84a46c5f02ffe5203f509a781

Observation 3a4c5ceb-3941-48fc-ac70-9627bac4a923 · outbound

This paper cites Lucas-kanade 20 years on: A unifying framework.

Subjective Portrait Region Cropping in Landscape Videos with Temporal Annotation Smoothing Lucas-kanade 20 years on: A unifying framework

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-05-26T21:03:01.908702Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-08T04:25:11.428981Z digest=sha256:e3d5811a6552579e28618fd7b5fa48ce62df0b00934fbc9a21756545ece45f78

Observation e5dcc6da-df4c-46ac-84f7-628798a0a5b9 · outbound

This paper cites Laion- 5b: An open large-scale dataset for training next generation image-text models.

Subjective Portrait Region Cropping in Landscape Videos with Temporal Annotation Smoothing Laion- 5b: An open large-scale dataset for training next generation image-text models

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-05-26T21:03:01.950328Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-08T04:25:11.428981Z digest=sha256:5dd9df20f3f973170497da6cd3d6eef6edf976276b644e115341be7a89ba0e5e

Observation af099ebd-b08e-4987-b183-d8a8d33cd0d9 · outbound

This paper cites Unified image and video saliency modeling.

Subjective Portrait Region Cropping in Landscape Videos with Temporal Annotation Smoothing Unified image and video saliency modeling

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-05-26T21:03:01.977970Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-08T04:25:11.428981Z digest=sha256:ac5237ec1a283dbaed9cb3604fb97514584e3b527ddc49fa0e6cfb2b7baa00d2

Observation 752c3da7-25a5-420a-ab92-4099d5b41ce7 · outbound

This paper cites Human-centric spatio-temporal video grounding with visual transform- ers.

Subjective Portrait Region Cropping in Landscape Videos with Temporal Annotation Smoothing Human-centric spatio-temporal video grounding with visual transform- ers

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-05-26T21:03:01.871758Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-08T04:25:11.428981Z digest=sha256:9db648ba99f21738e69fe4e1cc10d6a880627a169a125086a4f01eb5ac700091

Observation 8aa6c68e-2dc5-489d-b3ff-f689cfb8fb95 · outbound

This paper cites Where does it exist: Spatio-temporal video grounding for multi-form sentences.

Subjective Portrait Region Cropping in Landscape Videos with Temporal Annotation Smoothing Where does it exist: Spatio-temporal video grounding for multi-form sentences

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-05-26T21:03:01.920740Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-08T04:25:11.428981Z digest=sha256:d5010e3830f33ae43875e1d51200c761b288abb7e6d9e8a1af71b3ae5b17fd31

Observation 20e712eb-197e-4844-b284-53821bc8100a · outbound

This paper cites Object-Aware Multi-Branch Relation Networks for Spatio-Temporal Video Grounding.

Subjective Portrait Region Cropping in Landscape Videos with Temporal Annotation Smoothing Object-Aware Multi-Branch Relation Networks for Spatio-Temporal Video Grounding

Reference 28

Resolution
verified exact
arxiv_id, observed 2026-05-11T21:46:40.971195Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-08T04:25:11.428981Z digest=sha256:82d30fd2bbc48022ceade2808b749a927141aab8d7a29aaa26ccef9510e50b7d

Observation ba5e01c4-9e11-4771-84b3-be7e086a54dd · outbound

This paper cites Tubedetr: Spatio- temporal video grounding with transformers.

Subjective Portrait Region Cropping in Landscape Videos with Temporal Annotation Smoothing Tubedetr: Spatio- temporal video grounding with transformers

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-05-26T21:03:01.966114Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-08T04:25:11.428981Z digest=sha256:9643ea385f19feaaaa95c9db572ee7b8f134928a60af2bce010ce1465ceea7d6

Observation c36319b4-e4ff-4100-be0f-1b07648f1c77 · outbound

This paper cites Embracing consistency: A one-stage approach for spatio-temporal video grounding.

Subjective Portrait Region Cropping in Landscape Videos with Temporal Annotation Smoothing Embracing consistency: A one-stage approach for spatio-temporal video grounding

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-05-26T21:03:01.901544Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-08T04:25:11.428981Z digest=sha256:35f889b097eeb765948a0b25f34164a5efed55e9d804d9931da6f09ac95e6d5e

Observation a4a7872a-678e-4366-903c-fa18aa8bb86f · outbound

This paper cites Collaborative static and dynamic vision-language streams for spatio-temporal video grounding.

Subjective Portrait Region Cropping in Landscape Videos with Temporal Annotation Smoothing Collaborative static and dynamic vision-language streams for spatio-temporal video grounding

Reference 31

Resolution
verified fuzzy
raw_fallback, observed 2026-05-26T21:03:01.916671Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-08T04:25:11.428981Z digest=sha256:1724e95fd72ececf475efc28aecb76edcfdcf20ae0511c0ee503c812894a2540

Observation e6b7db0b-cd7c-4044-98b6-83d26e478984 · outbound

This paper cites Context-guided spatio- temporal video grounding.

Subjective Portrait Region Cropping in Landscape Videos with Temporal Annotation Smoothing Context-guided spatio- temporal video grounding

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-05-26T21:03:01.958564Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-08T04:25:11.428981Z digest=sha256:3fbd10c486fac7bbc9b5cdd05fae8c5febf60840ba00af33f9c0959117c7ae36

Pith citing papers

No inbound Pith citation observations are available.