Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-10T17:16:53.139075Z
Paper Citation Record · LEDGER
Learning segmentation from point trajectories
As of 11 August 2026, this Paper Citation Record lists 89 of 89 outbound references and 0 inbound Pith citation observations for arXiv:2501.12392.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
89 of 89 outbound references displayed
- verified exact4
- verified fuzzy65
- unresolved20
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation e817646b-4897-4ee7-9ff8-87117211b75e · outbound
This paper cites Determining three-dimensional motion and structure from optical flow generated by several moving objects.
Learning segmentation from point trajectories Determining three-dimensional motion and structure from optical flow generated by several moving objects
Reference 1
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation cd07257d-3eaa-4c47-bebf-a48c060ab715 · outbound
This paper cites Self-supervised object-centric learning for videos.
Learning segmentation from point trajectories Self-supervised object-centric learning for videos
Reference 2
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T17:16:52.552855Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 40fb3dd4-4cd3-44cb-8c8a-958c6d3a47bd · outbound
This paper cites It’s moving! a probabilistic model for causal motion segmentation in moving camera videos.
Learning segmentation from point trajectories It’s moving! a probabilistic model for causal motion segmentation in moving camera videos
Reference 3
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T17:16:52.560102Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3d622d38-b6aa-4c0f-903d-ce51840e554d · outbound
This paper cites The best of both worlds: Combining cnns and geometric constraints for hierarchical motion segmentation.
Learning segmentation from point trajectories The best of both worlds: Combining cnns and geometric constraints for hierarchical motion segmentation
Reference 4
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T17:16:52.565734Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8f5937b2-0f46-449f-a10a-dd1f2d14d0dc · outbound
This paper cites Large displacement optical flow: descriptor matching in variational motion estimation.
Learning segmentation from point trajectories Large displacement optical flow: descriptor matching in variational motion estimation
Reference 5
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ad9225a0-0d1c-4dc4-9370-d0dc6c1a3aef · outbound
This paper cites Object segmentation by long term analysis of point trajectories.
Learning segmentation from point trajectories Object segmentation by long term analysis of point trajectories
Reference 6
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T17:16:52.584264Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 024fd082-cd6b-48fb-87bd-09193f204268 · outbound
This paper cites One-shot video object segmentation.
Learning segmentation from point trajectories One-shot video object segmentation
Reference 7
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T17:16:52.591875Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 05bf7af5-f656-449b-a549-1dcd61cb8bfb · outbound
This paper cites Fisher III.
Reference 8
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T17:16:52.598201Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation fceca1f1-36b9-4e1a-9a2a-d75fa3781388 · outbound
This paper cites Non-negative matrix factorization of partial track data for motion segmentation.
Learning segmentation from point trajectories Non-negative matrix factorization of partial track data for motion segmentation
Reference 9
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T17:16:52.605172Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ccf73a3d-f27d-43bf-bef8-4dfcc91af9d6 · outbound
This paper cites Guess What Moves: Unsupervised Video and Image Segmentation by Anticipating Motion.
Learning segmentation from point trajectories Guess What Moves: Unsupervised Video and Image Segmentation by Anticipating Motion
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T17:16:54.935272Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 3fd5ae35-c7f1-4273-961e-ee7ce69bf092 · outbound
This paper cites A multi-body factorization method for motion analysis.
Learning segmentation from point trajectories A multi-body factorization method for motion analysis
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T17:16:54.919012Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 47b0bae8-9e7b-4895-93cd-6f36f31c1cbc · outbound
This paper cites A multibody factorization method for independently moving objects.
Learning segmentation from point trajectories A multibody factorization method for independently moving objects
Reference 12
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 3f68edfa-72ee-4860-b2c7-6cceacda7b25 · outbound
This paper cites Motion-inductive Self-supervised Object Discovery in Videos.
Learning segmentation from point trajectories Motion-inductive Self-supervised Object Discovery in Videos
Reference 13
- Resolution
- verified exact
- local_arxiv, observed 2026-08-10T17:16:53.550896Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 3eb204f5-3322-42a7-a29c-0de2db23e415 · outbound
This paper cites Tap-vid: A benchmark for tracking any point in a video.
Learning segmentation from point trajectories Tap-vid: A benchmark for tracking any point in a video
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T17:16:54.886896Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 33272f7c-f2ad-4d6a-90de-1b1a64fc27ef · outbound
This paper cites TAPIR: Tracking Any Point with per-frame Initialization and temporal Refinement.
Learning segmentation from point trajectories TAPIR: Tracking Any Point with per-frame Initialization and temporal Refinement
Reference 15
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T17:16:52.648583Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4c76f0d9-cbb9-4ab1-b559-d32d3abfd1c3 · outbound
This paper cites BootsTAP: Bootstrapped Training for Tracking-Any-Point.
Learning segmentation from point trajectories BootsTAP: Bootstrapped Training for Tracking-Any-Point
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T17:16:52.655495Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 04031821-9de4-4d13-b943-fe76c6246e21 · outbound
This paper cites Sparse subspace clustering: Algorithm, theory, and applications.
Learning segmentation from point trajectories Sparse subspace clustering: Algorithm, theory, and applications
Reference 17
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 9bb080e5-67ce-4dcb-9c5c-bace2c5369d1 · outbound
This paper cites Video segmentation by non-local consensus voting.
Learning segmentation from point trajectories Video segmentation by non-local consensus voting
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T17:16:54.855519Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 783de0b7-ba2c-4f37-a8db-6cbcb04de039 · outbound
This paper cites Clustering point trajectories with various life-spans.
Learning segmentation from point trajectories Clustering point trajectories with various life-spans
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T17:16:54.839538Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 6ae9a106-b99e-48b2-9bf2-6d753b671af2 · outbound
This paper cites Kubric: A scalable dataset generator.
Learning segmentation from point trajectories Kubric: A scalable dataset generator
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T17:16:54.823264Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation b97fa365-a9f2-423c-81b6-271f6541b377 · outbound
This paper cites A critique of self-expressive deep subspace clustering.
Learning segmentation from point trajectories A critique of self-expressive deep subspace clustering
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T17:16:54.806760Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 1499f877-697e-4072-8664-ba8c7c049f64 · outbound
This paper cites Particle video revisited: Tracking through occlusions using point trajectories.
Learning segmentation from point trajectories Particle video revisited: Tracking through occlusions using point trajectories
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T17:16:54.789730Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 1cc3c079-da90-4b13-8375-003f3b134088 · outbound
This paper cites an unresolved cited work.
Learning segmentation from point trajectories Unresolved cited work
Reference 23
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 87282801-3b5d-408a-8418-5a89198459b6 · outbound
This paper cites Flowformer: A transformer architecture for optical flow.
Learning segmentation from point trajectories Flowformer: A transformer architecture for optical flow
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T17:16:54.755027Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 22aae641-4dca-4408-9da7-c751ded8e09b · outbound
This paper cites FusionSeg: Learning to combine motion and appearance for fully automatic segmention of generic objects in videos.
Learning segmentation from point trajectories FusionSeg: Learning to combine motion and appearance for fully automatic segmention of generic objects in videos
Reference 25
- Resolution
- verified exact
- local_arxiv, observed 2026-08-10T17:16:53.458175Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation eb08acfe-5d8c-4b52-b264-38fa7d9c107c · outbound
This paper cites Jepson and Michael J.
Learning segmentation from point trajectories Jepson and Michael J
Reference 26
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation e4d6bd0c-473d-40a6-b881-66af57483429 · outbound
This paper cites Jojic and B.J.
Learning segmentation from point trajectories Jojic and B.J
Reference 27
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation ab002420-b64c-4c77-8e2e-cdfc81a4b162 · outbound
This paper cites CoTracker: It is Better to Track Together.
Learning segmentation from point trajectories CoTracker: It is Better to Track Together
Reference 28
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3952219c-97d8-4fb2-a5e7-276ad1db1844 · outbound
This paper cites Unsuper- vised multi-object segmentation by predicting probable motion patterns.
Learning segmentation from point trajectories Unsuper- vised multi-object segmentation by predicting probable motion patterns
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T17:16:54.701984Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation d44f9675-8fde-41b5-98e3-b9e8d8a42165 · outbound
This paper cites Higher-order minimum cost lifted multicuts for motion segmentation.
Learning segmentation from point trajectories Higher-order minimum cost lifted multicuts for motion segmentation
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T17:16:54.683709Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 6df718ab-c77a-48f3-be9f-21aebcad32bc · outbound
This paper cites Motion trajectory segmentation via minimum cost multicuts.
Learning segmentation from point trajectories Motion trajectory segmentation via minimum cost multicuts
Reference 31
- Resolution
- verified fuzzy
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Source-reported events for the cited work
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Observation 0e20d4cc-bf7e-4f02-b467-6ed426898387 · outbound
This paper cites A Multi-cut Formulation for Joint Segmentation and Tracking of Multiple Objects.
Learning segmentation from point trajectories A Multi-cut Formulation for Joint Segmentation and Tracking of Multiple Objects
Reference 32
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 29727fe1-fac6-4fc4-9739-4e5877badb47 · outbound
This paper cites Segmenting invisible moving objects.
Learning segmentation from point trajectories Segmenting invisible moving objects
Reference 33
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation be38bf00-edcf-4bd1-bd52-01e50dc9c187 · outbound
This paper cites Divided Attention: Unsupervised Multi-Object Discovery with Contextually Separated Slots.
Learning segmentation from point trajectories Divided Attention: Unsupervised Multi-Object Discovery with Contextually Separated Slots
Reference 34
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 711a3b1a-3577-4cda-8e8e-2afb4e1acbdc · outbound
This paper cites an unresolved cited work.
Learning segmentation from point trajectories Unresolved cited work
Reference 35
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 94ca3c5c-257f-4fc1-ab5f-369bd56f8185 · outbound
This paper cites Instance embedding transfer to unsupervised video object segmentation.
Learning segmentation from point trajectories Instance embedding transfer to unsupervised video object segmentation
Reference 36
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation fa1f5d12-0150-4663-a41d-68f567bede77 · outbound
This paper cites Bootstrapping objectness from videos by relaxed common fate and visual grouping.
Learning segmentation from point trajectories Bootstrapping objectness from videos by relaxed common fate and visual grouping
Reference 37
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 804e4e4e-44d9-4359-b52d-a43d314730c1 · outbound
This paper cites Robust recovery of subspace structures by low-rank representation.
Learning segmentation from point trajectories Robust recovery of subspace structures by low-rank representation
Reference 38
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 71747d08-e30f-4b1a-86af-1f3d792c1a5c · outbound
This paper cites Learning by analogy: Reliable supervision from transformations for unsupervised optical flow estimation.
Learning segmentation from point trajectories Learning by analogy: Reliable supervision from transformations for unsupervised optical flow estimation
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T17:16:54.561502Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 760f6c05-3d02-4e43-9bc6-f53b00f654da · outbound
This paper cites The emergence of objectness: Learning zero-shot segmentation from videos.
Learning segmentation from point trajectories The emergence of objectness: Learning zero-shot segmentation from videos
Reference 40
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 0928eda7-230d-4ac2-b669-70626683e4ff · outbound
This paper cites Robust and efficient subspace segmentation via least squares regression.
Learning segmentation from point trajectories Robust and efficient subspace segmentation via least squares regression
Reference 41
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 30feabdd-9681-4b21-bafb-2fb2d7eb22df · outbound
This paper cites See more, know more: Unsupervised video object segmentation with co-attention siamese networks.
Learning segmentation from point trajectories See more, know more: Unsupervised video object segmentation with co-attention siamese networks
Reference 42
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 773e9f16-b86d-4215-a5f5-24d5d414a14f · outbound
This paper cites Multi-subspace representation and discovery.
Learning segmentation from point trajectories Multi-subspace representation and discovery
Reference 43
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 49b9468c-c015-414b-91ce-b4be63e30fd9 · outbound
This paper cites A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation.
Learning segmentation from point trajectories A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T17:16:54.457173Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 604f1b39-0165-4a5d-9e66-2cd5f3ccd0fd · outbound
This paper cites Unsupervised space-time network for temporally-consistent segmentation of multiple motions.
Learning segmentation from point trajectories Unsupervised space-time network for temporally-consistent segmentation of multiple motions
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T17:16:54.438650Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 82a26081-d8e1-414b-80d9-1386b8fcbdea · outbound
This paper cites Segmenting the motion components of a video: A long-term unsupervised model.
Learning segmentation from point trajectories Segmenting the motion components of a video: A long-term unsupervised model
Reference 46
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5bd0d000-b363-40f4-971c-268067b56692 · outbound
This paper cites EM-driven unsupervised learning for efficient motion segmentation.
Learning segmentation from point trajectories EM-driven unsupervised learning for efficient motion segmentation
Reference 47
- Resolution
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- local_arxiv, observed 2026-08-10T17:16:53.314111Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 7eff44a6-688f-48de-b108-29a4ec40386c · outbound
This paper cites Object segmentation in video: a hierarchical variational approach for turning point trajectories into dense regions.
Learning segmentation from point trajectories Object segmentation in video: a hierarchical variational approach for turning point trajectories into dense regions
Reference 48
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T17:16:54.419858Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 625fa4c9-1190-4852-9e4c-37ef4b18ce32 · outbound
This paper cites Higher order motion models and spectral clustering.
Learning segmentation from point trajectories Higher order motion models and spectral clustering
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T17:16:54.403480Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation ea491c13-29df-4770-b2b5-088699210075 · outbound
This paper cites Segmentation of moving objects by long term video analysis.
Learning segmentation from point trajectories Segmentation of moving objects by long term video analysis
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T17:16:54.386691Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation d5599d22-e604-4e68-8d14-c52d8dd66e08 · outbound
This paper cites Segmentation of moving objects by long term video analysis.
Learning segmentation from point trajectories Segmentation of moving objects by long term video analysis
Reference 51
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T17:16:54.369747Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 583febcf-535f-4ab6-a112-4d3f7e8f5225 · outbound
This paper cites Fast object segmentation in unconstrained video.
Learning segmentation from point trajectories Fast object segmentation in unconstrained video
Reference 52
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T17:16:54.348350Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 0dde3e91-06a9-445b-8262-fa623c5f5950 · outbound
This paper cites Perazzi, J.
Reference 53
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T17:16:54.331073Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 29a91255-dda8-49c7-b6bd-f7971d776217 · outbound
This paper cites A simple and powerful global optimization for unsupervised video object segmentation.
Learning segmentation from point trajectories A simple and powerful global optimization for unsupervised video object segmentation
Reference 54
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T17:16:54.313239Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 26cc89cf-0f34-47a8-b3fd-69894c264855 · outbound
This paper cites Motion segmentation via robust subspace separation in the presence of outlying, incomplete, or corrupted trajectories.
Learning segmentation from point trajectories Motion segmentation via robust subspace separation in the presence of outlying, incomplete, or corrupted trajectories
Reference 55
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T17:16:54.293339Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation aa83c307-0854-4df0-814e-1199d5741b4c · outbound
This paper cites U-net: Convolutional networks for biomedical image segmentation.
Learning segmentation from point trajectories U-net: Convolutional networks for biomedical image segmentation
Reference 56
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T17:16:54.274872Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation e1706ec1-6b43-4df2-944c-84e95af675de · outbound
This paper cites Multi-object discovery by low-dimensional object motion.
Learning segmentation from point trajectories Multi-object discovery by low-dimensional object motion
Reference 57
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T17:16:54.255455Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 1a637768-e87d-49f3-9672-cf02cb469ba3 · outbound
This paper cites Simple unsupervised object-centric learning for complex and naturalistic videos.
Learning segmentation from point trajectories Simple unsupervised object-centric learning for complex and naturalistic videos
Reference 58
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T17:16:54.236902Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 1b652fea-8aca-48f3-8abf-dff4c5222e5a · outbound
This paper cites Locate: Self-supervised object discovery via flow-guided graph-cut and bootstrapped self-training.
Learning segmentation from point trajectories Locate: Self-supervised object discovery via flow-guided graph-cut and bootstrapped self-training
Reference 59
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T17:16:54.215080Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation c97a0122-b094-414d-ab2e-3b98bf6a99d4 · outbound
This paper cites Raft: Recurrent all-pairs field transforms for optical flow.
Learning segmentation from point trajectories Raft: Recurrent all-pairs field transforms for optical flow
Reference 60
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T17:16:54.198061Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 53c36f2a-c517-4ea7-9d81-2fbb79b44def · outbound
This paper cites Learning to segment moving objects.
Learning segmentation from point trajectories Learning to segment moving objects
Reference 61
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T17:16:54.179789Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 30bac829-9a40-49d3-b181-bf216ac5f825 · outbound
This paper cites an unresolved cited work.
Learning segmentation from point trajectories Unresolved cited work
Reference 62
- Resolution
- unresolved
- raw_fallback, observed 2026-08-10T17:16:54.162362Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation d911e655-503a-4a96-99c0-2612845037aa · outbound
This paper cites Differentiating the singular value decomposition.
Learning segmentation from point trajectories Differentiating the singular value decomposition
Reference 63
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T17:16:54.142909Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 9c4a5af3-e791-49f2-9cf8-1ac6100ea51d · outbound
This paper cites Low rank subspace clustering (lrsc).
Learning segmentation from point trajectories Low rank subspace clustering (lrsc)
Reference 64
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T17:16:54.122508Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 0c91f759-5e1d-4b62-b8e0-34f1b6baf3e0 · outbound
This paper cites VideoCutLER: Surprisingly Simple Unsupervised Video Instance Segmentation.
Learning segmentation from point trajectories VideoCutLER: Surprisingly Simple Unsupervised Video Instance Segmentation
Reference 65
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T17:16:52.968706Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d3697fc6-059b-4ebb-8bfd-15e66545ede4 · outbound
This paper cites Experimentelle studien uber das sehen von bewegung.
Learning segmentation from point trajectories Experimentelle studien uber das sehen von bewegung
Reference 66
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T17:16:54.099628Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 87305593-4974-43c3-a0eb-7c628262525c · outbound
This paper cites Segmenting Moving Objects via an Object-Centric Layered Representation.
Learning segmentation from point trajectories Segmenting Moving Objects via an Object-Centric Layered Representation
Reference 67
- Resolution
- verified exact
- local_arxiv, observed 2026-08-10T17:16:53.226386Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation b13c34e3-ce48-474f-8740-04d635e70a0e · outbound
This paper cites A general framework for motion segmentation: Independent, articulated, rigid, non-rigid, degenerate and non-degenerate.
Learning segmentation from point trajectories A general framework for motion segmentation: Independent, articulated, rigid, non-rigid, degenerate and non-degenerate
Reference 68
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T17:16:54.064089Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 4dbdbaab-595e-408e-b924-15a5b395bfad · outbound
This paper cites Self-supervised video object segmentation by motion grouping.
Learning segmentation from point trajectories Self-supervised video object segmentation by motion grouping
Reference 69
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T17:16:54.041914Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 3bea56a5-1b32-418f-86e0-514739e85a5f · outbound
This paper cites Unsupervised moving object detection via contextual information separation.
Learning segmentation from point trajectories Unsupervised moving object detection via contextual information separation
Reference 70
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T17:16:54.017388Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 1d19d844-146f-400c-94e6-387ead9e7ac0 · outbound
This paper cites Dystab: Unsupervised object segmentation via dynamic- static bootstrapping.
Learning segmentation from point trajectories Dystab: Unsupervised object segmentation via dynamic- static bootstrapping
Reference 71
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T17:16:53.984232Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 54dd808b-8e97-4922-8821-c5af716947f7 · outbound
This paper cites Deformable sprites for unsupervised video decomposition.
Learning segmentation from point trajectories Deformable sprites for unsupervised video decomposition
Reference 72
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T17:16:53.959659Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 181a3674-fa36-4a1b-bc06-0c919a08b870 · outbound
This paper cites Object-centric learning for real-world videos by predicting temporal feature similarities.
Learning segmentation from point trajectories Object-centric learning for real-world videos by predicting temporal feature similarities
Reference 73
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T17:16:53.935401Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 33aac3d5-3dc2-4c16-ad0b-e9046a7a7495 · outbound
This paper cites Harley, Bokui Shen, Gordon Wetzstein, and Leonidas J.
Learning segmentation from point trajectories Harley, Bokui Shen, Gordon Wetzstein, and Leonidas J
Reference 74
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T17:16:53.908185Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 14e82cd7-43ce-44fb-8915-03c9a3684a06 · outbound
This paper cites We also considered alternative formulations of the trajectories and found them to underperform.
Learning segmentation from point trajectories We also considered alternative formulations of the trajectories and found them to underperform
Reference 75
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T17:16:53.885839Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 27a04a1b-5b4d-4ea2-93ee-4f6ba1027a61 · outbound
This paper cites Limitations.
Learning segmentation from point trajectories Limitations
Reference 76
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T17:16:53.866410Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 4bfa23c0-8cbf-4eae-8e52-4bccd32a6807 · outbound
This paper cites Guidelines: • The answer NA means that the paper does not include theoretical results.
Learning segmentation from point trajectories Guidelines: • The answer NA means that the paper does not include theoretical results
Reference 77
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T17:16:53.845250Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 19e7bb0a-7734-4912-9ef9-a53a446fd779 · outbound
This paper cites Guidelines: • The answer NA means that the paper does not include experiments.
Learning segmentation from point trajectories Guidelines: • The answer NA means that the paper does not include experiments
Reference 78
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T17:16:53.826112Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 6fa89d0a-45c2-4794-ae88-7a74ab328eed · outbound
This paper cites Guidelines: • The answer NA means that paper does not include experiments requiring code.
Learning segmentation from point trajectories Guidelines: • The answer NA means that paper does not include experiments requiring code
Reference 79
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T17:16:53.805904Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 52f7e63c-aade-4d72-96a7-c552288e7a15 · outbound
This paper cites Guidelines: • The answer NA means that the paper does not include experiments.
Learning segmentation from point trajectories Guidelines: • The answer NA means that the paper does not include experiments
Reference 80
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T17:16:53.786017Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 1c6ddd32-212e-434f-81b3-046a06185926 · outbound
This paper cites We report±σ intervals in our feasibility study.
Learning segmentation from point trajectories We report±σ intervals in our feasibility study
Reference 81
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T17:16:53.765641Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation e2d03c56-0e24-40f1-b8d9-0b3d4facf2d8 · outbound
This paper cites Guidelines: • The answer NA means that the paper does not include experiments.
Learning segmentation from point trajectories Guidelines: • The answer NA means that the paper does not include experiments
Reference 82
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T17:16:53.740021Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation df2138c4-1c3c-40fa-b97f-d4d5ad433597 · outbound
This paper cites Guidelines: • The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics.
Learning segmentation from point trajectories Guidelines: • The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics
Reference 83
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T17:16:53.712920Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation c36e6dea-f784-4582-91b8-6dd8231f40ed · outbound
This paper cites Guidelines: • The answer NA means that there is no societal impact of the work performed.
Learning segmentation from point trajectories Guidelines: • The answer NA means that there is no societal impact of the work performed
Reference 84
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T17:16:53.691804Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation f9c7441d-c7eb-4c94-bf21-fd4d185384d5 · outbound
This paper cites Guidelines: • The answer NA means that the paper poses no such risks.
Learning segmentation from point trajectories Guidelines: • The answer NA means that the paper poses no such risks
Reference 85
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T17:16:53.661927Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation d1fd20e5-a9ce-4772-b5f3-1d867d1a2c6c · outbound
This paper cites Guidelines: • The answer NA means that the paper does not use existing assets.
Learning segmentation from point trajectories Guidelines: • The answer NA means that the paper does not use existing assets
Reference 86
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T17:16:53.642600Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation f5d17fa5-d5f8-4054-98db-d0ca723218b3 · outbound
This paper cites Guidelines: • The answer NA means that the paper does not release new assets.
Learning segmentation from point trajectories Guidelines: • The answer NA means that the paper does not release new assets
Reference 87
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T17:16:53.621515Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 603be45e-3327-42d5-941b-40878e75f236 · outbound
This paper cites Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.
Learning segmentation from point trajectories Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects
Reference 88
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T17:16:53.133574Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1b6fefbe-495c-4229-ac61-ff7749119e3b · outbound
This paper cites Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.
Learning segmentation from point trajectories Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects
Reference 89
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T17:16:53.582713Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Pith citing papers
No inbound Pith citation observations are available.