Paper Citation Record · LEDGER

Efficient Tracking and Understanding Object Transformations

As of 8 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 0 inbound Pith citation observations for arXiv:2607.19743.

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

pith.paper-citation-record.v1
2607.19743 v1

Coverage vector

measured 51 of 51 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T11:55:13.116562Z

measured 51 of 51 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

51 of 51 outbound references displayed

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External citation measurements

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Outbound references

Observation 8a2dbc7e-fd6c-4d63-8c19-233bcc142f96 · outbound

This paper cites GPT-4 Technical Report.

Efficient Tracking and Understanding Object Transformations GPT-4 Technical Report

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-01T11:55:09.112671Z

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Unavailable: canonical work link unavailable.

Observation aa074ca9-5d26-43f1-940b-ecf86f027e6c · outbound

This paper cites Learning what to learn for video object segmenta- tion.

Efficient Tracking and Understanding Object Transformations Learning what to learn for video object segmenta- tion

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-01T11:55:09.171350Z

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Observation 1d0de68f-9b07-41d9-a368-41acf1273e7b · outbound

This paper cites One- shot video object segmentation.

Efficient Tracking and Understanding Object Transformations One- shot video object segmentation

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-01T11:55:09.282049Z

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Observation 3c0ee510-43d0-4c02-bca7-0cfd347a7992 · outbound

This paper cites M$^3$-VOS: Multi-Phase, Multi-Transition, and Multi-Scenery Video Object Segmentation.

Efficient Tracking and Understanding Object Transformations M$^3$-VOS: Multi-Phase, Multi-Transition, and Multi-Scenery Video Object Segmentation

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-01T11:55:09.339686Z

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Observation b5640434-1815-46e7-b2a9-37655da1d503 · outbound

This paper cites Xmem: Long- term video object segmentation with an atkinson-shiffrin memory model.

Efficient Tracking and Understanding Object Transformations Xmem: Long- term video object segmentation with an atkinson-shiffrin memory model

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-01T11:55:09.408481Z

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Observation 8415628d-5e06-4aa9-bd55-1fd8f9745b56 · outbound

This paper cites Putting the object back into video object segmentation.

Efficient Tracking and Understanding Object Transformations Putting the object back into video object segmentation

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-01T11:55:09.505478Z

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Observation 06867b1c-c752-490a-ab56-af9a01d8e778 · outbound

This paper cites Rescaling egocentric vision: Collection, pipeline and chal- lenges for epic-kitchens-100.International Journal of Com- puter Vision, pages 1–23, 2022.

Efficient Tracking and Understanding Object Transformations Rescaling egocentric vision: Collection, pipeline and chal- lenges for epic-kitchens-100.International Journal of Com- puter Vision, pages 1–23, 2022

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-01T11:55:09.566402Z

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Observation 6ad58e50-ad8e-45d7-a97c-3031da828713 · outbound

This paper cites Mevis: A large-scale benchmark for video segmentation with motion expressions.

Efficient Tracking and Understanding Object Transformations Mevis: A large-scale benchmark for video segmentation with motion expressions

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-01T11:55:09.630682Z

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Observation b3210be7-1fc1-44cb-8a6b-97d1e8fbd8ec · outbound

This paper cites Mose: A new dataset for video object segmentation in complex scenes.

Efficient Tracking and Understanding Object Transformations Mose: A new dataset for video object segmentation in complex scenes

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-01T11:55:09.728034Z

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Observation d79a414a-a323-4915-86b1-432bf5a333f6 · outbound

This paper cites SAM2Long: Enhancing SAM 2 for Long Video Segmentation with a Training-Free Memory Tree.

Efficient Tracking and Understanding Object Transformations SAM2Long: Enhancing SAM 2 for Long Video Segmentation with a Training-Free Memory Tree

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-01T11:55:09.781555Z

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Observation 1bb0a664-994b-4eed-a5ec-ee91f146312c · outbound

This paper cites Event neural net- works.

Efficient Tracking and Understanding Object Transformations Event neural net- works

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-01T11:55:09.843883Z

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Observation b9d6d769-d358-43b4-8a41-0d8bd77a3d0f · outbound

This paper cites Lasot: A high-quality benchmark for large-scale single ob- ject tracking.

Efficient Tracking and Understanding Object Transformations Lasot: A high-quality benchmark for large-scale single ob- ject tracking

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-01T11:55:09.906884Z

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Observation c55b2d4c-06b9-40a2-8e82-f074a17953ff · outbound

This paper cites Ego4d: Around the world in 3,000 hours of egocentric video.

Efficient Tracking and Understanding Object Transformations Ego4d: Around the world in 3,000 hours of egocentric video

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-01T11:55:09.969824Z

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Observation cf7417b5-1c2b-4e13-a5d0-8108c94a0fe8 · outbound

This paper cites Skip-convolutions for efficient video processing.

Efficient Tracking and Understanding Object Transformations Skip-convolutions for efficient video processing

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-01T11:55:10.029885Z

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Observation 42246b8d-38a3-45d2-81ba-a90e3430637d · outbound

This paper cites Delta distillation for ef- ficient video processing.

Efficient Tracking and Understanding Object Transformations Delta distillation for ef- ficient video processing

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-01T11:55:10.118570Z

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Observation 9ccf52b5-cc15-4253-9e1a-6bd0739cda0e · outbound

This paper cites Lvos: A benchmark for long-term video object segmentation.

Efficient Tracking and Understanding Object Transformations Lvos: A benchmark for long-term video object segmentation

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-01T11:55:10.180497Z

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Observation c0c76a2c-6dcb-4f24-a423-ca16ba7b9eb3 · outbound

This paper cites Videomatch: Matching based video object segmentation.

Efficient Tracking and Understanding Object Transformations Videomatch: Matching based video object segmentation

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-01T11:55:10.249402Z

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Observation e9e8e9bd-b887-4b80-8ed9-2bc1e909a785 · outbound

This paper cites Segment any- thing.

Efficient Tracking and Understanding Object Transformations Segment any- thing

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-01T11:55:10.314385Z

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Observation e1b6792d-842c-48cd-9e2f-6be9074661c1 · outbound

This paper cites Scsampler: Sampling salient clips from video for efficient action recog- nition.

Efficient Tracking and Understanding Object Transformations Scsampler: Sampling salient clips from video for efficient action recog- nition

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-01T11:55:10.410233Z

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Observation 7f854cbd-ced3-4689-84bb-6a20a77ac05e · outbound

This paper cites Blade: Learning compositional behaviors from demonstration and language.

Efficient Tracking and Understanding Object Transformations Blade: Learning compositional behaviors from demonstration and language

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-01T11:55:10.480170Z

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Observation 97000b2f-58c0-4844-b857-06b78ca5da5c · outbound

This paper cites Mash, spread, slice! learning to manipulate object states via visual spatial progress.arXiv preprint arXiv:2509.24129,.

Efficient Tracking and Understanding Object Transformations Mash, spread, slice! learning to manipulate object states via visual spatial progress.arXiv preprint arXiv:2509.24129,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-01T11:55:10.512238Z

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Observation 1abb384b-a092-4b2b-bb3b-0d78c57a9c43 · outbound

This paper cites Spoc: Spatially-progressing ob- ject state change segmentation in video, 2025.

Efficient Tracking and Understanding Object Transformations Spoc: Spatially-progressing ob- ject state change segmentation in video, 2025

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-01T11:55:10.562673Z

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Observation 43e75ae2-a3c9-49ee-866d-ce714d551207 · outbound

This paper cites Video object segmentation without temporal information.IEEE transactions on pattern analysis and machine intelligence, 41(6):1515–1530, 2018.

Efficient Tracking and Understanding Object Transformations Video object segmentation without temporal information.IEEE transactions on pattern analysis and machine intelligence, 41(6):1515–1530, 2018

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-01T11:55:10.627825Z

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Observation f4e8230c-3053-4ebf-81ee-e0a77fd10d6b · outbound

This paper cites A literature review of computer vision techniques in wildlife monitoring.IJSRP, 16:282–295, 2022.

Efficient Tracking and Understanding Object Transformations A literature review of computer vision techniques in wildlife monitoring.IJSRP, 16:282–295, 2022

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-01T11:55:10.697849Z

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Observation 4dec266b-5741-45fe-a0ee-153461296471 · outbound

This paper cites Fast video object segmentation by reference- guided mask propagation.

Efficient Tracking and Understanding Object Transformations Fast video object segmentation by reference- guided mask propagation

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-01T11:55:10.819325Z

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Observation b5f1dfd1-4ef5-43f1-8661-a7a5c4a4d484 · outbound

This paper cites Video object segmentation using space-time memory networks.

Efficient Tracking and Understanding Object Transformations Video object segmentation using space-time memory networks

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-01T11:55:10.876734Z

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Observation 44e2774a-b9d6-4397-a82a-2f4f82a31b07 · outbound

This paper cites A benchmark dataset and evaluation methodology for video object segmentation.

Efficient Tracking and Understanding Object Transformations A benchmark dataset and evaluation methodology for video object segmentation

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-01T11:55:10.924605Z

Source-reported events for the cited work

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Observation df4682ff-61ed-47fd-bc68-4518d44c06ad · outbound

This paper cites The 2017 DAVIS Challenge on Video Object Segmentation.

Efficient Tracking and Understanding Object Transformations The 2017 DAVIS Challenge on Video Object Segmentation

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-01T11:55:10.968087Z

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Observation a71abc44-b18f-4f1a-93b6-089704796abd · outbound

This paper cites High-Quality Entity Segmentation.

Efficient Tracking and Understanding Object Transformations High-Quality Entity Segmentation

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-01T11:55:11.059687Z

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Observation be2f7887-fd6f-4462-b7f4-701fea1dfcef · outbound

This paper cites SAM 2: Segment Anything in Images and Videos.

Efficient Tracking and Understanding Object Transformations SAM 2: Segment Anything in Images and Videos

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-01T11:55:11.215307Z

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Observation 960e87ca-f89a-4aff-888b-b24522c9c274 · outbound

This paper cites The visual object track- ing vot2016 challenge results.

Efficient Tracking and Understanding Object Transformations The visual object track- ing vot2016 challenge results

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-01T11:55:11.297776Z

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Observation 707e97e6-c2cd-47d9-8648-db51a93f14b2 · outbound

This paper cites Token turing machines.

Efficient Tracking and Understanding Object Transformations Token turing machines

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-01T11:55:11.392497Z

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Observation a8f4a119-5ef0-4545-87e6-4b6fc4e5ed8a · outbound

This paper cites xGen-MM-Vid (BLIP-3-Video): You Only Need 32 Tokens to Represent a Video Even in VLMs.

Efficient Tracking and Understanding Object Transformations xGen-MM-Vid (BLIP-3-Video): You Only Need 32 Tokens to Represent a Video Even in VLMs

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-01T11:55:11.477753Z

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Observation 8d0d70f6-8b79-4828-9a3d-1282ef858430 · outbound

This paper cites Tracking and understanding object transformations.

Efficient Tracking and Understanding Object Transformations Tracking and understanding object transformations

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-01T11:55:11.568889Z

Source-reported events for the cited work

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Observation 7d43954b-31a3-4899-a24f-12ad72049d98 · outbound

This paper cites Breaking the” object” in video object segmentation.

Efficient Tracking and Understanding Object Transformations Breaking the” object” in video object segmentation

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-01T11:55:11.660021Z

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Observation 26ec42ed-9de2-4b06-9d66-b9495243f4e4 · outbound

This paper cites A distractor-aware memory for visual object tracking with sam2.

Efficient Tracking and Understanding Object Transformations A distractor-aware memory for visual object tracking with sam2

Reference 36

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Observation 929525fd-87d9-4908-b8bc-153174901eec · outbound

This paper cites Feelvos: Fast end-to-end embedding learning for video object seg- mentation.

Efficient Tracking and Understanding Object Transformations Feelvos: Fast end-to-end embedding learning for video object seg- mentation

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-01T11:55:11.840339Z

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Observation 7dfe8420-5782-4432-a505-6d00eefaabcf · outbound

This paper cites Adaptive focus for efficient video recognition.

Efficient Tracking and Understanding Object Transformations Adaptive focus for efficient video recognition

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-01T11:55:11.946785Z

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Observation a47d4fdb-b372-4ce7-9317-ad3716e16f49 · outbound

This paper cites Tracking transforming objects: A benchmark.

Efficient Tracking and Understanding Object Transformations Tracking transforming objects: A benchmark

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-01T11:55:12.029372Z

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Observation 111fb18e-add2-4f83-a643-915daa82f3a9 · outbound

This paper cites Adaframe: Adaptive frame selection for fast video recognition.

Efficient Tracking and Understanding Object Transformations Adaframe: Adaptive frame selection for fast video recognition

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-01T11:55:12.115537Z

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Observation 03865801-13f9-4caa-a751-e859f728910d · outbound

This paper cites Efficient track anything.

Efficient Tracking and Understanding Object Transformations Efficient track anything

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-01T11:55:12.213919Z

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Observation 5ce48b93-8211-4296-970d-707ed72f4c5d · outbound

This paper cites Youtube-vos: Sequence-to-sequence video object segmentation.

Efficient Tracking and Understanding Object Transformations Youtube-vos: Sequence-to-sequence video object segmentation

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-01T11:55:12.303996Z

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Observation 73299579-45fd-4bc9-889a-da9246eb8647 · outbound

This paper cites Learn- ing object state changes in videos: An open-world perspec- tive, 2024.

Efficient Tracking and Understanding Object Transformations Learn- ing object state changes in videos: An open-world perspec- tive, 2024

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-01T11:55:12.401325Z

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Observation e7e59353-1601-43bf-a8ba-dfaa5d970eaa · outbound

This paper cites SAMURAI: Adapting Segment Anything Model for Zero-Shot Visual Tracking with Motion-Aware Memory.

Efficient Tracking and Understanding Object Transformations SAMURAI: Adapting Segment Anything Model for Zero-Shot Visual Tracking with Motion-Aware Memory

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-01T11:55:12.500740Z

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Observation ad64c713-bd2c-4008-b0a8-bda4da06154e · outbound

This paper cites Efficient video object seg- mentation via network modulation.

Efficient Tracking and Understanding Object Transformations Efficient video object seg- mentation via network modulation

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-01T11:55:12.593019Z

Source-reported events for the cited work

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Observation eb79461b-0af3-4626-a540-5a30c843f777 · outbound

This paper cites Collaborative video object segmentation by foreground-background inte- gration.

Efficient Tracking and Understanding Object Transformations Collaborative video object segmentation by foreground-background inte- gration

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-01T11:55:12.691476Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a9e80751-2f38-4a99-b663-7556db8d9e0b · outbound

This paper cites Associating ob- jects with transformers for video object segmentation.Ad- vances in Neural Information Processing Systems, 34:2491– 2502, 2021.

Efficient Tracking and Understanding Object Transformations Associating ob- jects with transformers for video object segmentation.Ad- vances in Neural Information Processing Systems, 34:2491– 2502, 2021

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-01T11:55:12.792068Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f312575e-f5a8-4706-b840-366e5e12a8ef · outbound

This paper cites Video state-changing object segmentation.

Efficient Tracking and Understanding Object Transformations Video state-changing object segmentation

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-01T11:55:12.884011Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0fe1e955-8f06-46ef-9fbd-8dcccf533950 · outbound

This paper cites Convolutions die hard: Open-vocabulary seg- mentation with single frozen convolutional clip.Advances in Neural Information Processing Systems, 36:32215–32234,.

Efficient Tracking and Understanding Object Transformations Convolutions die hard: Open-vocabulary seg- mentation with single frozen convolutional clip.Advances in Neural Information Processing Systems, 36:32215–32234,

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-01T11:55:12.936381Z

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Unavailable: canonical work link unavailable.

Observation bcdb979b-3065-4091-bddc-b9c315c73e2d · outbound

This paper cites TrajTok: Learning Trajectory Tokens enables better Video Understanding.

Efficient Tracking and Understanding Object Transformations TrajTok: Learning Trajectory Tokens enables better Video Understanding

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-01T11:55:13.026221Z

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Observation ad502b1d-f351-491c-b7ea-4401158d740a · outbound

This paper cites Edgetam: On-device track anything model.

Efficient Tracking and Understanding Object Transformations Edgetam: On-device track anything model

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-01T11:55:13.116562Z

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Pith citing papers

No inbound Pith citation observations are available.