Pith. sign in

Paper Citation Record · LEDGER

High Performance Visual Object Tracking with Unified Convolutional Networks

As of 16 August 2026, this Paper Citation Record lists 74 of 74 outbound references and 0 inbound Pith citation observations for arXiv:1908.09445.

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

pith.paper-citation-record.v1
1908.09445 v1

Coverage vector

measured 74 of 74 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T11:17:08.029962Z

measured 74 of 74 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+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

74 of 74 outbound references displayed

  • verified exact7
  • verified fuzzy59
  • unresolved8
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 87dc72cb-08a0-4c84-82e4-859635e6bcce · outbound

This paper cites Sparse representation for crowd attributes recognition,.

High Performance Visual Object Tracking with Unified Convolutional Networks Sparse representation for crowd attributes recognition,

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:09.368870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:17:07.635132Z digest=sha256:9fa2264d650561d496e2e4a97836da2b52583a89ed26359365358325b52b1f27

Observation 20d2518f-e000-4161-b815-7c491f421d85 · outbound

This paper cites Research on automatic parking systems based on parking scene recognition,.

High Performance Visual Object Tracking with Unified Convolutional Networks Research on automatic parking systems based on parking scene recognition,

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:09.353944Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:17:07.640460Z digest=sha256:27e54f5aeea952212f99996573fe9afc04ad6eb6deae964f6b862ef3d457c826

Observation c6183c24-6565-4203-b43c-9b3bd6af0c17 · outbound

This paper cites FastPose: Towards Real-time Pose Estimation and Tracking via Scale-normalized Multi-task Networks.

High Performance Visual Object Tracking with Unified Convolutional Networks FastPose: Towards Real-time Pose Estimation and Tracking via Scale-normalized Multi-task Networks

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-14T11:17:07.645826Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T11:17:07.645826Z digest=sha256:bc939cb16cd29b09c585b9a5ecfd9f86a34d24fdb26e9ecca581ca6c68d54524

Observation 47025c46-861c-4583-a4df-5e9198e3a48a · outbound

This paper cites Exploiting Offset-guided Network for Pose Estimation and Tracking.

High Performance Visual Object Tracking with Unified Convolutional Networks Exploiting Offset-guided Network for Pose Estimation and Tracking

Reference 4

Resolution
verified exact
local_arxiv, observed 2026-08-14T11:17:08.317181Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:17:07.651448Z digest=sha256:347e22362894721922981c5c669eb0d960df28e0689db6c0ed1a8ece21145cac

Observation 91b9b435-4e14-4bdf-922a-5d60658753c5 · outbound

This paper cites State-aware re-identification feature for multi-target multi-camera tracking,.

High Performance Visual Object Tracking with Unified Convolutional Networks State-aware re-identification feature for multi-target multi-camera tracking,

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:09.339149Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:17:07.656999Z digest=sha256:f36095ba73e41191aebfadbd6a4a88a902445a1bcc4ed2420648d41a5d7a70b7

Observation 7919de92-26c8-47bc-be3f-91d198c53631 · outbound

This paper cites Selection of observation position and orientation in visual servoing with eye-in-vehicle configuration for manipulator,.

High Performance Visual Object Tracking with Unified Convolutional Networks Selection of observation position and orientation in visual servoing with eye-in-vehicle configuration for manipulator,

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:09.323369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:17:07.662106Z digest=sha256:860a9ca96ef8f0dcabe044cc32599cf3441de099a1271be24b962fff23534816

Observation a7acd3d5-c318-4316-9ed9-26925e1372f5 · outbound

This paper cites Adaptive tra- jectory tracking of wheeled mobile robots based on a fish-eye camera,.

High Performance Visual Object Tracking with Unified Convolutional Networks Adaptive tra- jectory tracking of wheeled mobile robots based on a fish-eye camera,

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:09.307412Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:17:07.667240Z digest=sha256:351299ae0a445958d80e9a5a37c6bf4d7f7893f784e379a7eb27bbf5cea1e7d9

Observation e3953693-da33-493d-a578-4fdf7e34b253 · outbound

This paper cites A velocity compensation visual servo method for oculomotor con- trol of bionic eyes,.

High Performance Visual Object Tracking with Unified Convolutional Networks A velocity compensation visual servo method for oculomotor con- trol of bionic eyes,

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:09.291262Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:17:07.672351Z digest=sha256:a06d479bb5a01ad9bbc7b769abfeeef6c5ef2624840eb1f6580a25da4e845a69

Observation c9c406f5-8b45-429d-9476-70475acb0db1 · outbound

This paper cites Motion control in saccade and smooth pursuit for bionic eye based on three- dimensional coordinates,.

High Performance Visual Object Tracking with Unified Convolutional Networks Motion control in saccade and smooth pursuit for bionic eye based on three- dimensional coordinates,

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:09.274310Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:17:07.677829Z digest=sha256:4cb6dae3e64bbea34007d4333483d58bdf23ecc8dc57ba29aa55ffede5360bd2

Observation 0e75d2bd-e359-4887-895d-df3aaadd3840 · outbound

This paper cites Optical Flow Based Real-time Moving Object Detection in Unconstrained Scenes.

High Performance Visual Object Tracking with Unified Convolutional Networks Optical Flow Based Real-time Moving Object Detection in Unconstrained Scenes

Reference 10

Resolution
verified exact
local_arxiv, observed 2026-08-14T11:17:08.292092Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:17:07.683091Z digest=sha256:f536bf354995a89fbf58196e7f9dcdb55f13494221b7619cdf97a7b6277ff350

Observation 4f9a07b4-85de-4da3-90d4-d4031cdd22d8 · outbound

This paper cites Optical Flow Based Online Moving Foreground Analysis.

High Performance Visual Object Tracking with Unified Convolutional Networks Optical Flow Based Online Moving Foreground Analysis

Reference 11

Resolution
verified exact
local_arxiv, observed 2026-08-14T11:17:08.269324Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:17:07.689160Z digest=sha256:0d1cb570bace238fe19787e12b0ef176c7fdab47e06ff20b73b399f9ef8c5aff

Observation 7d5e4bde-e85f-4f50-9a79-a797c37f8497 · outbound

This paper cites An Efficient Optical Flow Based Motion Detection Method for Non-stationary Scenes.

High Performance Visual Object Tracking with Unified Convolutional Networks An Efficient Optical Flow Based Motion Detection Method for Non-stationary Scenes

Reference 12

Resolution
verified exact
local_arxiv, observed 2026-08-14T11:17:08.243766Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:17:07.694470Z digest=sha256:61e3639e2a639e2c2dceb0e5415b7f0c975675ce037c1dc93a982aa46d8d7adb

Observation 06d3cafe-3ec7-487a-b43e-bab80db6bde6 · outbound

This paper cites Motion cue based instance-level moving object detection,.

High Performance Visual Object Tracking with Unified Convolutional Networks Motion cue based instance-level moving object detection,

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:09.257150Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:17:07.700268Z digest=sha256:81999489a1806ab4eae717653aceac00ac68aa70f84aacb3ddec324830882001

Observation 158cf080-787a-4385-a2fe-fb02f628ca85 · outbound

This paper cites Std: A stereo tracking dataset for evaluating binocular tracking algorithms,.

High Performance Visual Object Tracking with Unified Convolutional Networks Std: A stereo tracking dataset for evaluating binocular tracking algorithms,

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:09.240638Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:17:07.705035Z digest=sha256:4ab19ffdac6fa6d5a6c392d7cd33d3ccda4f6bb3c80dba9ef66c5484b8c62792

Observation 4b2bd6df-fc70-47a9-a0fe-7b17d6f2dc0c · outbound

This paper cites Object tracking benchmark,.

High Performance Visual Object Tracking with Unified Convolutional Networks Object tracking benchmark,

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:09.224037Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:17:07.709919Z digest=sha256:ddf4915604e2e28a8d686126a5369125ea2670b7ec26e3183cae54bb4926123e

Observation 0c018eed-2d76-420e-9e52-18203ec62069 · outbound

This paper cites Visual tracking: An exper- imental survey,.

High Performance Visual Object Tracking with Unified Convolutional Networks Visual tracking: An exper- imental survey,

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:09.207490Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:17:07.715709Z digest=sha256:1d3a501d40e7bb21b2004ab6b154a12023516f8c63a7cbde660067a3ced494d7

Observation 525c7741-5105-45d0-9145-8361cb4cf6a6 · outbound

This paper cites The sixth visual object tracking vot2018 challenge results,.

High Performance Visual Object Tracking with Unified Convolutional Networks The sixth visual object tracking vot2018 challenge results,

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:09.190241Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:17:07.720584Z digest=sha256:917be7e608dffbfbe497b698fed23bf001926f1170c5fbd5cbc159aeb1fe4965

Observation d1b55746-5770-45a5-9cd9-211d560535ad · outbound

This paper cites The visual object tracking vot2015 challenge results,.

High Performance Visual Object Tracking with Unified Convolutional Networks The visual object tracking vot2015 challenge results,

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:09.172345Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:17:07.725437Z digest=sha256:f100ccf452b0925de66c3d9349f368bc91c9e0ba7026563ee2e4782107e9dfb8

Observation 1fc7a8ce-02b7-4636-9c3e-be2c63c3355b · outbound

This paper cites Online object tracking with sparse prototypes,.

High Performance Visual Object Tracking with Unified Convolutional Networks Online object tracking with sparse prototypes,

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:09.155253Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:17:07.730455Z digest=sha256:2b521e587ae9da4d5ad14ccd0146b7fe567fb0b4d90da67f7fef95f4eceb5feb

Observation 117aefe4-78d9-4ff4-87fe-ff4d20779410 · outbound

This paper cites Inverse sparse tracker with a locally weighted distance metric,.

High Performance Visual Object Tracking with Unified Convolutional Networks Inverse sparse tracker with a locally weighted distance metric,

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:09.138375Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:17:07.736265Z digest=sha256:726471d8c80e9662b13fefdd309ce9e2bc0d2277701032ef130ce58aa0df6e4a

Observation 3594f549-7a50-4f6d-a811-025ef1389b56 · outbound

This paper cites Struck: Structured output tracking with kernels,.

High Performance Visual Object Tracking with Unified Convolutional Networks Struck: Structured output tracking with kernels,

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:09.122112Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:17:07.740905Z digest=sha256:df96ae8c56d59a5f8e2333086625adb65da14202d8e10c5f44c114296594181d

Observation 5bce6619-c19b-443c-ba46-0dc14f649b12 · outbound

This paper cites Real-time tracking via on-line boosting,.

High Performance Visual Object Tracking with Unified Convolutional Networks Real-time tracking via on-line boosting,

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:09.105872Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:17:07.745811Z digest=sha256:89cc08d1f5008c212a8187cb8f3059b3cb0f903025779252bc6b29a6d08bfc83

Observation e321f8dc-f5c6-47d0-bc19-af9d7508dd28 · outbound

This paper cites Robust object tracking with online multiple instance learning,.

High Performance Visual Object Tracking with Unified Convolutional Networks Robust object tracking with online multiple instance learning,

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:09.088469Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:17:07.750648Z digest=sha256:073997641c78cd60734bd1e3394e99aff2d044ad49bbf8d35d13369d7664fd0a

Observation 69a653e1-065f-48f2-98fd-ba25adbbefc4 · outbound

This paper cites High-speed tracking with kernelized correlation filters,.

High Performance Visual Object Tracking with Unified Convolutional Networks High-speed tracking with kernelized correlation filters,

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:09.072523Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:17:07.755546Z digest=sha256:233a5fd487da00f8e4fe22c6cdaebcd3d53dd2b1d15261ff685adc316978cb17

Observation d3853206-34b1-45ad-8338-b863104e1dd6 · outbound

This paper cites Learning spatially regularized correlation filters for vi- sual tracking,.

High Performance Visual Object Tracking with Unified Convolutional Networks Learning spatially regularized correlation filters for vi- sual tracking,

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:09.056341Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:17:07.760534Z digest=sha256:ba38fd54e0612c9cda1d9560a43754e1e990c02126786c77debd313a085efca4

Observation 2582b3f7-1300-404a-9d6d-f15cb7f08f0a · outbound

This paper cites Long- term correlation tracking,.

High Performance Visual Object Tracking with Unified Convolutional Networks Long- term correlation tracking,

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:09.039480Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:17:07.765614Z digest=sha256:753096d90d6b33766ff2d3a8041b6144241c1cd080092d1892633b38e0cc1a25

Observation b91dde52-a88d-4fbf-a63c-0dcfc19ccc18 · outbound

This paper cites A scale adaptive kernel correlation filter tracker with feature integration,.

High Performance Visual Object Tracking with Unified Convolutional Networks A scale adaptive kernel correlation filter tracker with feature integration,

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:09.021381Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:17:07.771410Z digest=sha256:bbd2cd812a47fd9150b4e0a915478614022342722d0e11774a73a5e807f7dc01

Observation dff9d339-d178-4edc-a1f6-082205d28fb3 · outbound

This paper cites Ex- ploiting the circulant structure of tracking-by-detection with kernels,.

High Performance Visual Object Tracking with Unified Convolutional Networks Ex- ploiting the circulant structure of tracking-by-detection with kernels,

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:09.004501Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:17:07.777293Z digest=sha256:5e22b3796e33f42b60d5427bc2ba57d3d84e89dd99b0706d425d2a5c1762fd80

Observation 598da227-3b51-4f44-a509-e50354d77173 · outbound

This paper cites Discriminative scale space tracking,.

High Performance Visual Object Tracking with Unified Convolutional Networks Discriminative scale space tracking,

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:08.987592Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:17:07.783289Z digest=sha256:cabf5d593851797f00bfe1a832af21c02f8d6eb88b5b1fd19b12c8de8b929e2a

Observation edb419b0-39b5-4b62-9739-7aa0b84ec67f · outbound

This paper cites Learning background-aware correlation filters for visual tracking,.

High Performance Visual Object Tracking with Unified Convolutional Networks Learning background-aware correlation filters for visual tracking,

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:08.970448Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:17:07.788680Z digest=sha256:512cb758d645cf76e175155dca797ff06ef7a111256257398b3e693076800a3e

Observation 079640e9-feef-4286-a946-46589021d212 · outbound

This paper cites Imagenet classification with deep convolutional neural networks,.

High Performance Visual Object Tracking with Unified Convolutional Networks Imagenet classification with deep convolutional neural networks,

Reference 31

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:08.953446Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:17:07.793787Z digest=sha256:c3a0831d3e61e3a18eb82b81e120fa11c8ecbf7695386feb0405cea0471930ab

Observation ece2480c-b43a-4f71-beb8-c1149564701a · outbound

This paper cites Deep resid- ual learning for image recognition,.

High Performance Visual Object Tracking with Unified Convolutional Networks Deep resid- ual learning for image recognition,

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-14T11:17:07.798602Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T11:17:07.798602Z digest=sha256:51a0a3a409ebce6c25fbbb133e6041347b9868ff92942168acfc4dd8f857d32d

Observation b165e19c-e904-4311-ba21-23e3bf5c4ec8 · outbound

This paper cites Faster r-cnn: towards real-time object detection with region proposal networks,.

High Performance Visual Object Tracking with Unified Convolutional Networks Faster r-cnn: towards real-time object detection with region proposal networks,

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:08.925448Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:17:07.803852Z digest=sha256:529d33eb01f4287fe3afefa031d78ad00c45c85059efc0ddb3c4420aa54609da

Observation 759d49eb-a29b-4c78-b17b-0bceeb8e8351 · outbound

This paper cites Fully convolu- tional networks for semantic segmentation,.

High Performance Visual Object Tracking with Unified Convolutional Networks Fully convolu- tional networks for semantic segmentation,

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:08.909299Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:17:07.808812Z digest=sha256:5fc26a7ec6cf96003d5afcb2c7e0a6d581935fa669db7762181701ed3533fa6b

Observation 8e826822-3165-4480-ae53-393843f68501 · outbound

This paper cites Hi- erarchical convolutional features for visual tracking,.

High Performance Visual Object Tracking with Unified Convolutional Networks Hi- erarchical convolutional features for visual tracking,

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:08.892910Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:17:07.813944Z digest=sha256:2e5f544f41380ad867fc5a4ec4d4c79d816d71ff9bb7ffba34a69eebd7948815

Observation 58e88c15-749f-4f3e-968a-4ca7b4053dc0 · outbound

This paper cites Hedged deep tracking,.

High Performance Visual Object Tracking with Unified Convolutional Networks Hedged deep tracking,

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:08.875289Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:17:07.819171Z digest=sha256:471ad350fe8b535fbd862ebbda6c48f2553ddc0376b42b819d69be81d466ce90

Observation bfa5d6aa-6328-4bb8-b3b3-9099b9b3ef4d · outbound

This paper cites Beyond correlation filters: Learning continuous convo- lution operators for visual tracking,.

High Performance Visual Object Tracking with Unified Convolutional Networks Beyond correlation filters: Learning continuous convo- lution operators for visual tracking,

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:08.859523Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:17:07.824230Z digest=sha256:2c1c57006ef0c1c0a5192e1a3e6def62327041650ee128d6c90ee5c2095b62c2

Observation 739597ea-b92b-4d32-97dd-0a1336e94372 · outbound

This paper cites Convolutional features for correlation filter based visual tracking,.

High Performance Visual Object Tracking with Unified Convolutional Networks Convolutional features for correlation filter based visual tracking,

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:08.842836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:17:07.828960Z digest=sha256:8d12494ba193059248316991111ce36100c6f55b28f9e755243eab4c9217ceb6

Observation 9a16c30e-9720-4ee1-ae50-f14fb7e618d1 · outbound

This paper cites Fully-convolutional siamese networks for object tracking,.

High Performance Visual Object Tracking with Unified Convolutional Networks Fully-convolutional siamese networks for object tracking,

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:08.827365Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:17:07.834746Z digest=sha256:3e17e53021296e5cc4f3592f5c905a95562a6b727ee6373db343c2519402719f

Observation 8a6a37b0-9c88-4045-ad0a-918c28865c0d · outbound

This paper cites Visual track- ing with fully convolutional networks,.

High Performance Visual Object Tracking with Unified Convolutional Networks Visual track- ing with fully convolutional networks,

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:08.811653Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:17:07.840000Z digest=sha256:800efec0b18f40134af59bfaa7d0ac1305611142322e4c3fb03d9f9caf74ba2c

Observation 48a08e09-35f5-470a-bb66-bb26c2d491fa · outbound

This paper cites The visual object tracking vot2016 challenge results,.

High Performance Visual Object Tracking with Unified Convolutional Networks The visual object tracking vot2016 challenge results,

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:08.795131Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:17:07.845477Z digest=sha256:4c3c6afc967a43242084a11e6c5c60d07fc0c4293ff2f3f1ba85125f5251ea49

Observation c9feeb1c-b3b6-41ab-977a-4382d2518d7e · outbound

This paper cites UCT: Learning unified convolutional networks for real-time visual tracking,.

High Performance Visual Object Tracking with Unified Convolutional Networks UCT: Learning unified convolutional networks for real-time visual tracking,

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:08.778025Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:17:07.850551Z digest=sha256:9c857dc64e020aa2a872a9240d9b87647de0c9201c325f3c1f5161e9a3d4adc3

Observation 84de5e56-0774-4b42-be42-c107f6219ab6 · outbound

This paper cites Two-stream gated fusion convnets for action recognition,.

High Performance Visual Object Tracking with Unified Convolutional Networks Two-stream gated fusion convnets for action recognition,

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:08.760643Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:17:07.856703Z digest=sha256:83cd9bf834b3ae68498f8144363fd91e7f549c859b7d0618807cabee63d5469d

Observation fb4fcd4c-ddf7-4941-bf0e-e93adddd0c38 · outbound

This paper cites Attention-guided unified network for panoptic segmentation,.

High Performance Visual Object Tracking with Unified Convolutional Networks Attention-guided unified network for panoptic segmentation,

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:08.744164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:17:07.861822Z digest=sha256:e9fe4298f3fb163c69619e6c3967a74a37624072339bc0ca90d943ecba2e41a5

Observation c2011070-9f42-496f-8d44-bbd205e4e1ee · outbound

This paper cites Action Machine: Rethinking Action Recognition in Trimmed Videos.

High Performance Visual Object Tracking with Unified Convolutional Networks Action Machine: Rethinking Action Recognition in Trimmed Videos

Reference 45

Resolution
verified exact
local_arxiv, observed 2026-08-14T11:17:08.218197Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:17:07.867387Z digest=sha256:f5793d199c6996ba5c82318f799a0df9a42d0b7fd75960d5c241f3c65ae424f1

Observation 0bc6f8fe-bc0c-43d0-8cda-bb0a37023410 · outbound

This paper cites Learning Gating ConvNet for Two-Stream based Methods in Action Recognition.

High Performance Visual Object Tracking with Unified Convolutional Networks Learning Gating ConvNet for Two-Stream based Methods in Action Recognition

Reference 46

Resolution
verified exact
local_arxiv, observed 2026-08-14T11:17:08.192418Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:17:07.874594Z digest=sha256:54ff167d45487d6a28c40df36f3473724fa72fad494ec65aebe8491c9544b4a6

Observation 6247962e-d6a7-473f-b5af-c967d575ebd2 · outbound

This paper cites Transferring Rich Feature Hierarchies for Robust Visual Tracking.

High Performance Visual Object Tracking with Unified Convolutional Networks Transferring Rich Feature Hierarchies for Robust Visual Tracking

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-14T11:17:07.879815Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T11:17:07.879815Z digest=sha256:1a996701e1d852b2aec96de76067b491bcee5a206918c57a3048bb0d3f06dfe0

Observation e08d70c3-93e7-4090-8288-c72e9b5de361 · outbound

This paper cites Deeptrack: Learning dis- criminative feature representations online for robust vi- sual tracking,.

High Performance Visual Object Tracking with Unified Convolutional Networks Deeptrack: Learning dis- criminative feature representations online for robust vi- sual tracking,

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:08.728020Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:17:07.886392Z digest=sha256:d5624c3a80a576d5addf3760a8456743a009af75b27a346d7d925665abfdd302

Observation cdb80473-b21a-45d7-945c-49db82877881 · outbound

This paper cites Multi-hierarchical Independent Correlation Filters for Visual Tracking.

High Performance Visual Object Tracking with Unified Convolutional Networks Multi-hierarchical Independent Correlation Filters for Visual Tracking

Reference 49

Resolution
verified exact
local_arxiv, observed 2026-08-14T11:17:08.142609Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:17:07.891937Z digest=sha256:a0ad82a5ef91627d2c39ef829487ebc083b5e2a9a2ac9b3b59ce74cbdc61138d

Observation 381eeb05-b51f-490a-854f-8432390765df · outbound

This paper cites Learning multi-domain convolu- tional neural networks for visual tracking,.

High Performance Visual Object Tracking with Unified Convolutional Networks Learning multi-domain convolu- tional neural networks for visual tracking,

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:08.710872Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:17:07.897403Z digest=sha256:3f77cd65a36035a4713941c181f352bb24eec470f75fd7ab90dd01622e890ff4

Observation a78a8f58-6337-48f1-9447-26b2ce4ae1c9 · outbound

This paper cites Template matching using fast normalized cross correlation,.

High Performance Visual Object Tracking with Unified Convolutional Networks Template matching using fast normalized cross correlation,

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:08.693155Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:17:07.903539Z digest=sha256:85f6a3bbb3fc15827b0f570988b3016ca433642d83a7bb47d357527119a247de

Observation fb95a89c-28ce-4ccd-bf4f-37f12b0e89a8 · outbound

This paper cites Real-time track- ing of non-rigid objects using mean shift,.

High Performance Visual Object Tracking with Unified Convolutional Networks Real-time track- ing of non-rigid objects using mean shift,

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:08.676935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:17:07.908849Z digest=sha256:525ec45a4633231f0ac7280acf8809d05eaf0704d7b33cea99be850a6fc6bc34

Observation d79c3d05-f987-40d3-b848-f8fa7237fa4b · outbound

This paper cites Visual object tracking using adaptive correlation filters,.

High Performance Visual Object Tracking with Unified Convolutional Networks Visual object tracking using adaptive correlation filters,

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:08.659147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:17:07.914911Z digest=sha256:8c7e24f88818b46254316ea8d7a43f86d1be6d723c709700b157a5fed2a880de

Observation 5013d76e-0b68-44b7-850f-dacf3e48e415 · outbound

This paper cites Staple: Complementary learners for real- time tracking,.

High Performance Visual Object Tracking with Unified Convolutional Networks Staple: Complementary learners for real- time tracking,

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:08.642551Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:17:07.920052Z digest=sha256:7d8d398ce6d3011ec20c0404028c622364670ef27be602061d66760dc9bcd072

Observation c1e1e120-c642-47e8-866d-d64c4d0a622d · outbound

This paper cites Correla- tion filters with limited boundaries,.

High Performance Visual Object Tracking with Unified Convolutional Networks Correla- tion filters with limited boundaries,

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:08.623865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:17:07.924981Z digest=sha256:020f6970d80bbe5585db5bfd5ae7e38d459f5f032eaa9770b6a4c464cde107ad

Observation 7bc83923-2d69-4c7c-9a43-6ba62fc9ca4d · outbound

This paper cites Learning to track at 100 fps with deep regression networks,.

High Performance Visual Object Tracking with Unified Convolutional Networks Learning to track at 100 fps with deep regression networks,

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:08.606372Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:17:07.929933Z digest=sha256:3587c4accfb0eab9b0bdce9ed57852a3041afa118636e85347b8c152576da7b8

Observation 7cd4197c-f051-46e9-b08e-98b58740eab3 · outbound

This paper cites End-to-end representation learning for correlation filter based tracking,.

High Performance Visual Object Tracking with Unified Convolutional Networks End-to-end representation learning for correlation filter based tracking,

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:08.589204Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:17:07.934819Z digest=sha256:c78153220899c909bf267903e1f1c181b23bc280768b88a8999dd8a56b5ee218

Observation 13b0742b-eedc-451d-adeb-9f81c497f0cd · outbound

This paper cites DCFNet: Discriminant Correlation Filters Network for Visual Tracking.

High Performance Visual Object Tracking with Unified Convolutional Networks DCFNet: Discriminant Correlation Filters Network for Visual Tracking

Reference 58

Resolution
unresolved
no resolver link, observed 2026-08-14T11:17:07.939798Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T11:17:07.939798Z digest=sha256:b418b6da0292f464e4aaf608b05c046152ab5d923d46f1869f05be60abc51032

Observation 00f792ba-cb4d-4e04-999d-47d32be98a31 · outbound

This paper cites End-to-end flow correlation tracking with spatial-temporal attention,.

High Performance Visual Object Tracking with Unified Convolutional Networks End-to-end flow correlation tracking with spatial-temporal attention,

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:08.571266Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:17:07.946546Z digest=sha256:0998a8293f5ebad465c68e58ea9a68a97c5f5caaa2d798e4d49b5e25a0ce2a40

Observation fd9a0de3-493c-47d4-b44b-086a0ab26ee9 · outbound

This paper cites End-to-end video-level representation learning for action recognition,.

High Performance Visual Object Tracking with Unified Convolutional Networks End-to-end video-level representation learning for action recognition,

Reference 60

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:08.552105Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:17:07.951952Z digest=sha256:f9fa062da9dd60fc2191823c3e190d347026424495f4e8c99c0460485fac1b04

Observation 92f2473c-c0a6-4103-a3ac-18a2d0390494 · outbound

This paper cites High performance visual tracking with siamese region proposal network,.

High Performance Visual Object Tracking with Unified Convolutional Networks High performance visual tracking with siamese region proposal network,

Reference 61

Resolution
unresolved
no resolver link, observed 2026-08-14T11:17:07.957083Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T11:17:07.957083Z digest=sha256:3b496715a329d42011c5b19cc82bf6649eea5c294ee656d109215fdb786e0af1

Observation c3b2abea-a567-49ae-ac17-aeae9e194aec · outbound

This paper cites Distractor-aware siamese networks for visual object tracking,.

High Performance Visual Object Tracking with Unified Convolutional Networks Distractor-aware siamese networks for visual object tracking,

Reference 62

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:08.523405Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:17:07.961774Z digest=sha256:e1bacac746b60f7e057dde03dd36739829305a942f23b661345651dee9bd9055

Observation 887c4d53-a424-49f9-8737-9bbf5e40114a · outbound

This paper cites DenseBox: Unifying Landmark Localization with End to End Object Detection.

High Performance Visual Object Tracking with Unified Convolutional Networks DenseBox: Unifying Landmark Localization with End to End Object Detection

Reference 63

Resolution
unresolved
no resolver link, observed 2026-08-14T11:17:07.966774Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T11:17:07.966774Z digest=sha256:136f37023d7c191ee2b6f9d7fa4e7a6531d023b32dcb0a7746dc6939a136b6e3

Observation 7b19bb41-f9b4-4255-9c10-3c32629288ed · outbound

This paper cites Learning a deep compact im- age representation for visual tracking,.

High Performance Visual Object Tracking with Unified Convolutional Networks Learning a deep compact im- age representation for visual tracking,

Reference 64

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:08.506636Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:17:07.973849Z digest=sha256:cbec4799568bb02cc959b3f1dfbdd679ba03d8205fbf6d7115308ceca03f40bc

Observation e4db4af5-dd26-4ca8-8efd-f608dd0eeb55 · outbound

This paper cites Online object tracking: A benchmark,.

High Performance Visual Object Tracking with Unified Convolutional Networks Online object tracking: A benchmark,

Reference 65

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:08.489457Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:17:07.979411Z digest=sha256:aa6339fd999f34a788ea4bc1c08110c0d8957621f8740a318eaf55b29c329eaa

Observation 4edab244-8a2b-4685-8d26-0a5f1ee66768 · outbound

This paper cites The visual object tracking vot2015 challenge results,.

High Performance Visual Object Tracking with Unified Convolutional Networks The visual object tracking vot2015 challenge results,

Reference 66

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:08.471986Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:17:07.984416Z digest=sha256:ec8fbd30e64e5e0cd65b79188f9fdd4f761bbe23c5e78104ff37e2f73cd68cc1

Observation ca3dfd43-e18b-442e-9fcc-d20bf87010ed · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

High Performance Visual Object Tracking with Unified Convolutional Networks Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 67

Resolution
unresolved
no resolver link, observed 2026-08-14T11:17:07.989372Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T11:17:07.989372Z digest=sha256:22b76e6c40ea56bd7795053ad3c9a9cb8478752e538eb93c7ca272a59620c3d2

Observation 0db610fd-45c2-4fb2-b6e3-3be25ab0dae3 · outbound

This paper cites Visualizing and under- standing convolutional networks,.

High Performance Visual Object Tracking with Unified Convolutional Networks Visualizing and under- standing convolutional networks,

Reference 68

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:08.453996Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:17:07.995746Z digest=sha256:0c4df024fbc25e688d560d82c47bc23e1c8269b84f8435d4d30cd97b029e6eeb

Observation c5b0470c-3b0a-4e0d-931c-183098b54b7a · outbound

This paper cites ImageNet Large Scale Visual Recognition Challenge,.

High Performance Visual Object Tracking with Unified Convolutional Networks ImageNet Large Scale Visual Recognition Challenge,

Reference 69

Resolution
unresolved
no resolver link, observed 2026-08-14T11:17:08.001388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T11:17:08.001388Z digest=sha256:3d57bb965ee469d5143bc7a02bd8ec336cd40e572522ae4864669e121031d617

Observation 182d9cd6-168f-4a6f-8297-8492ce0171d3 · outbound

This paper cites Caffe: Convolutional architecture for fast feature embedding,.

High Performance Visual Object Tracking with Unified Convolutional Networks Caffe: Convolutional architecture for fast feature embedding,

Reference 70

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:08.424373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:17:08.006525Z digest=sha256:01a86b683a91f6ac057757b3cadf28fba3985c53bae1239e87d8cd88a4129b63

Observation 5d3783e9-1cc2-49e7-8e2c-7c10b24658b0 · outbound

This paper cites Parallel tracking and verifying: A framework for real-time and high accuracy visual tracking,.

High Performance Visual Object Tracking with Unified Convolutional Networks Parallel tracking and verifying: A framework for real-time and high accuracy visual tracking,

Reference 71

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:08.407189Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:17:08.011662Z digest=sha256:f47b3f281e86e5328b47838b6e9e1c463f0ad2c9fe9c8140ad21379369b46cb7

Observation bc4fa692-eb39-4a0a-be0f-6160fba59790 · outbound

This paper cites Context-aware correlation filter tracking,.

High Performance Visual Object Tracking with Unified Convolutional Networks Context-aware correlation filter tracking,

Reference 72

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:08.390072Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:17:08.018405Z digest=sha256:aca9461945bf508e9d657cac3be24b9aa54acb4547e0c757f286222a29a8cb6e

Observation 412f9d43-3d23-4d9a-abf4-779c304d3932 · outbound

This paper cites Discriminative correlation filter with channel and spatial reliability,.

High Performance Visual Object Tracking with Unified Convolutional Networks Discriminative correlation filter with channel and spatial reliability,

Reference 73

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:08.373346Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:17:08.024618Z digest=sha256:7f972fb948a49b930761da405d60e2fb1cbb06c7fd84359748e563d82309ce7c

Observation 8fa5bb61-502f-4c70-9749-888e50f4bb9d · outbound

This paper cites Visual tracking using attention- modulated disintegration and integration,.

High Performance Visual Object Tracking with Unified Convolutional Networks Visual tracking using attention- modulated disintegration and integration,

Reference 74

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:08.355127Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:17:08.029962Z digest=sha256:c755afb8910cbd1991cc5770438633c1ad3ed25b5f3f4bdda3396c7d23efbc7a

Pith citing papers

No inbound Pith citation observations are available.