SUMO: Segment and Track Any Motion with Nonlinear State Space Models | Paper Citation Record

Observation 901429ad-815f-4c87-bd6a-f6e7903d2ee4 · outbound

This paper cites Progressive-x: Efficient, anytime, multi-model fitting algorithm.

SUMO: Segment and Track Any Motion with Nonlinear State Space Models Progressive-x: Efficient, anytime, multi-model fitting algorithm

Reference 1

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Observation a38a2c90-5760-415c-ab66-8b01c3618237 · outbound

This paper cites Time optimal tra- jectories for a car-like mobile robot.IEEE Transactions on Robotics, 38(1):421–432, 2021.

SUMO: Segment and Track Any Motion with Nonlinear State Space Models Time optimal tra- jectories for a car-like mobile robot.IEEE Transactions on Robotics, 38(1):421–432, 2021

Reference 2

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Observation 2dc632d2-bec3-472b-8563-4b4759042565 · outbound

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

SUMO: Segment and Track Any Motion with Nonlinear State Space Models Fully-convolutional siamese networks for object tracking

Reference 3

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Observation aa566301-70da-45e8-ae40-1a44310a8fb1 · outbound

This paper cites Learning discriminative model prediction for track- ing.

SUMO: Segment and Track Any Motion with Nonlinear State Space Models Learning discriminative model prediction for track- ing

Reference 4

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Observation 290b51b4-c9a2-4dc9-a9a4-03a4b1474d11 · outbound

This paper cites It’s moving! a prob- abilistic model for causal motion segmentation in moving camera videos.

SUMO: Segment and Track Any Motion with Nonlinear State Space Models It’s moving! a prob- abilistic model for causal motion segmentation in moving camera videos

Reference 5

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Observation f2002800-1dee-47a4-b9a9-4816be08a963 · outbound

This paper cites Deep Learning for Robust Motion Segmentation with Non-Static Cameras.

SUMO: Segment and Track Any Motion with Nonlinear State Space Models Deep Learning for Robust Motion Segmentation with Non-Static Cameras

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-06-30T06:44:19.471013Z

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Observation 214234d2-e6e1-4569-938f-63084530428d · outbound

This paper cites Ro- bust object modeling for visual tracking.

SUMO: Segment and Track Any Motion with Nonlinear State Space Models Ro- bust object modeling for visual tracking

Reference 7

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Observation 24cd663d-7b43-4985-b1bf-40101bbf322f · outbound

This paper cites Springer Science & Business Media, 2012.

SUMO: Segment and Track Any Motion with Nonlinear State Space Models Springer Science & Business Media, 2012

Reference 8

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Observation 8628245d-fe20-4141-a46d-dc2b052f1885 · outbound

This paper cites Learning independent object motion from unlabelled stereo- scopic videos.

SUMO: Segment and Track Any Motion with Nonlinear State Space Models Learning independent object motion from unlabelled stereo- scopic videos

Reference 9

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Observation dee8c50f-44c5-43e3-b233-74d59c824646 · outbound

This paper cites Linear rotate subspaee based visual tracking methods with application to uav stand-off target tracking.

SUMO: Segment and Track Any Motion with Nonlinear State Space Models Linear rotate subspaee based visual tracking methods with application to uav stand-off target tracking

Reference 10

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Observation 2ebb88de-6e6e-43c2-8b30-aa55afa70434 · outbound

This paper cites Transformer tracking.

SUMO: Segment and Track Any Motion with Nonlinear State Space Models Transformer tracking

Reference 11

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Observation 5ca20552-e99f-4cad-a428-9f66077a915a · outbound

This paper cites Seqtrack: Sequence to sequence learning for visual ob- ject tracking.

SUMO: Segment and Track Any Motion with Nonlinear State Space Models Seqtrack: Sequence to sequence learning for visual ob- ject tracking

Reference 12

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Observation 9532302b-d9e4-423b-8bf0-bb8321827a34 · outbound

This paper cites Siamese box adaptive network for visual tracking.

SUMO: Segment and Track Any Motion with Nonlinear State Space Models Siamese box adaptive network for visual tracking

Reference 13

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Observation fe2eb97b-74ba-4d24-80f9-85c6eb62f802 · outbound

This paper cites Mixformer: End-to-end tracking with iterative mixed atten- tion.

SUMO: Segment and Track Any Motion with Nonlinear State Space Models Mixformer: End-to-end tracking with iterative mixed atten- tion

Reference 14

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Observation 6aa0e037-5014-4486-a681-8e83ecad3450 · outbound

This paper cites Atom: Accurate tracking by overlap max- imization.

SUMO: Segment and Track Any Motion with Nonlinear State Space Models Atom: Accurate tracking by overlap max- imization

Reference 15

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Observation dd07a9f3-4ddf-4180-93d3-695cc94344ef · outbound

This paper cites Ada-track: End-to-end multi-camera 3d multi-object tracking with alternating detection and association.

SUMO: Segment and Track Any Motion with Nonlinear State Space Models Ada-track: End-to-end multi-camera 3d multi-object tracking with alternating detection and association

Reference 16

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Observation 8e839e07-e67f-4e57-a09f-e80892ed167d · outbound

This paper cites an unresolved cited work.

SUMO: Segment and Track Any Motion with Nonlinear State Space Models Unresolved cited work

Reference 17

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Observation 5d5ac315-b057-4606-86e1-4960590bdadd · outbound

This paper cites Lasot: A high-quality large-scale single object tracking benchmark.International Journal of Computer Vision, 129 (2):439–461, 2021.

SUMO: Segment and Track Any Motion with Nonlinear State Space Models Lasot: A high-quality large-scale single object tracking benchmark.International Journal of Computer Vision, 129 (2):439–461, 2021

Reference 18

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Observation 04bed902-52bb-4755-9a93-237a4aba5daa · outbound

This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

SUMO: Segment and Track Any Motion with Nonlinear State Space Models Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-06-30T06:44:19.459793Z

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Observation ce29b6a5-4f59-41db-998c-c7341df20df3 · outbound

This paper cites Masked autoencoders are scalable vision learners.

SUMO: Segment and Track Any Motion with Nonlinear State Space Models Masked autoencoders are scalable vision learners

Reference 20

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Observation 5fec19b7-e13f-464f-ba44-71a05984da69 · outbound

This paper cites Got-10k: A large high-diversity benchmark for generic object tracking in the wild.IEEE transactions on pattern analysis and machine intelligence, 43(5):1562–1577, 2019.

SUMO: Segment and Track Any Motion with Nonlinear State Space Models Got-10k: A large high-diversity benchmark for generic object tracking in the wild.IEEE transactions on pattern analysis and machine intelligence, 43(5):1562–1577, 2019

Reference 21

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Observation d236a5f5-2c2d-4914-a574-3bdefa3ef60e · outbound

This paper cites Segment any motion in videos.

SUMO: Segment and Track Any Motion with Nonlinear State Space Models Segment any motion in videos

Reference 22

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Observation 0efefdc3-185b-432c-9d66-0d3e5ecbab1c · outbound

This paper cites Accelerated reeds-shepp and under-specified reeds-shepp algorithms for mobile robot path planning.IEEE Transactions on Robotics,.

SUMO: Segment and Track Any Motion with Nonlinear State Space Models Accelerated reeds-shepp and under-specified reeds-shepp algorithms for mobile robot path planning.IEEE Transactions on Robotics,

Reference 23

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Observation 26d97fc3-4349-468b-8c80-8637c692bfe9 · outbound

This paper cites Unscented filtering and nonlinear estimation.Proceedings of the IEEE, 92(3): 401–422, 2004.

SUMO: Segment and Track Any Motion with Nonlinear State Space Models Unscented filtering and nonlinear estimation.Proceedings of the IEEE, 92(3): 401–422, 2004

Reference 24

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Observation 2cd19b65-8163-4d84-8461-1c8e5d058c5c · outbound

This paper cites Learning segmentation from point trajecto- ries.Advances in Neural Information Processing Systems, 37:112573–112597, 2024.

SUMO: Segment and Track Any Motion with Nonlinear State Space Models Learning segmentation from point trajecto- ries.Advances in Neural Information Processing Systems, 37:112573–112597, 2024

Reference 25

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Observation 009fcfac-9fae-49b4-8cc2-7cc4f9ec4f3c · outbound

This paper cites Nonlinear systems.3rd edition, 2002.

SUMO: Segment and Track Any Motion with Nonlinear State Space Models Nonlinear systems.3rd edition, 2002

Reference 26

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Observation 5cd846c1-74bd-4d5b-9117-db8b78e3ddb2 · outbound

This paper cites Segment any- thing.

SUMO: Segment and Track Any Motion with Nonlinear State Space Models Segment any- thing

Reference 27

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Observation 8581f8e8-d87c-4a23-96f4-bb5bc63af71f · outbound

This paper cites The weighted markov-dubins problem.IEEE Robotics and Automation Letters, 8(3):1563–1570, 2023.

SUMO: Segment and Track Any Motion with Nonlinear State Space Models The weighted markov-dubins problem.IEEE Robotics and Automation Letters, 8(3):1563–1570, 2023

Reference 28

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Observation 2957cb55-866a-479c-aab8-5c2e6ae8de01 · outbound

This paper cites Motion segmentation via a sparsity constraint.

SUMO: Segment and Track Any Motion with Nonlinear State Space Models Motion segmentation via a sparsity constraint

Reference 29

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Observation d17d5650-5275-4169-ae5d-ae44d82f2411 · outbound

This paper cites High performance visual tracking with siamese region pro- posal network.

SUMO: Segment and Track Any Motion with Nonlinear State Space Models High performance visual tracking with siamese region pro- posal network

Reference 30

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Observation d879abae-b07b-46d9-bc72-48decf2daace · outbound

This paper cites Siamrpn++: Evolution of siamese vi- sual tracking with very deep networks.

SUMO: Segment and Track Any Motion with Nonlinear State Space Models Siamrpn++: Evolution of siamese vi- sual tracking with very deep networks

Reference 31

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Observation 38493de0-4a24-4bbd-84c9-929613683a58 · outbound

This paper cites Video segmentation by tracking many figure- ground segments.

SUMO: Segment and Track Any Motion with Nonlinear State Space Models Video segmentation by tracking many figure- ground segments

Reference 32

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Observation 782e56b6-4a15-414c-a3a1-ebbd4a143a78 · outbound

This paper cites an unresolved cited work.

SUMO: Segment and Track Any Motion with Nonlinear State Space Models Unresolved cited work

Reference 33

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Observation 01fb4a9d-fa27-4095-9722-3cb6b92f506d · outbound

This paper cites Sequencing-enabled hierarchical cooperative cav on- ramp merging control with enhanced stability and feasibility.

SUMO: Segment and Track Any Motion with Nonlinear State Space Models Sequencing-enabled hierarchical cooperative cav on- ramp merging control with enhanced stability and feasibility

Reference 34

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Observation a25fcc14-4d33-4de7-a075-7ca3327b2879 · outbound

This paper cites Closed-form generation of paths for motion planning of a convexified reeds-shepp vehicle on a sphere.Available at SSRN 5227769, 2025.

SUMO: Segment and Track Any Motion with Nonlinear State Space Models Closed-form generation of paths for motion planning of a convexified reeds-shepp vehicle on a sphere.Available at SSRN 5227769, 2025

Reference 35

Resolution: unresolved; no resolver link, observed 2026-06-30T06:37:27.783172Z

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Observation c7f438bc-b1b8-4cfb-9566-b0e0a8f5c125 · outbound

This paper cites Time-optimal Convexified Reeds-Shepp Paths on a Sphere.

SUMO: Segment and Track Any Motion with Nonlinear State Space Models Time-optimal Convexified Reeds-Shepp Paths on a Sphere

Reference 36

Resolution: verified exact; local_arxiv, observed 2026-06-30T06:44:19.465688Z

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Observation b47fb67f-47ae-40ce-a70e-a8961b8031ea · outbound

This paper cites Bootstrapping objectness from videos by relaxed common fate and visual grouping.

SUMO: Segment and Track Any Motion with Nonlinear State Space Models Bootstrapping objectness from videos by relaxed common fate and visual grouping

Reference 37

Resolution: unresolved; no resolver link, observed 2026-06-30T06:37:27.783172Z

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Observation b7d7ddb9-b971-4dfa-87bb-adab59203083 · outbound

This paper cites PointMamba: A Simple State Space Model for Point Cloud Analysis.

SUMO: Segment and Track Any Motion with Nonlinear State Space Models PointMamba: A Simple State Space Model for Point Cloud Analysis

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-06-30T06:44:19.456540Z

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Observation 5c99e4a2-e870-49aa-8f63-100bbf32dddc · outbound

This paper cites Swintrack: A simple and strong baseline for trans- former tracking.Advances in Neural Information Processing Systems, 35:16743–16754, 2022.

SUMO: Segment and Track Any Motion with Nonlinear State Space Models Swintrack: A simple and strong baseline for trans- former tracking.Advances in Neural Information Processing Systems, 35:16743–16754, 2022

Reference 39

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Observation 9cecafae-076d-4382-ba50-fec2f024f01a · outbound

This paper cites Tracking meets lora: Faster training, larger model, stronger performance.

SUMO: Segment and Track Any Motion with Nonlinear State Space Models Tracking meets lora: Faster training, larger model, stronger performance

Reference 40

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Observation b3ccb36c-572e-4821-a10d-323947695e32 · outbound

This paper cites Vmamba: Visual state space model.Advances in neural information processing systems, 37:103031–103063, 2024.

SUMO: Segment and Track Any Motion with Nonlinear State Space Models Vmamba: Visual state space model.Advances in neural information processing systems, 37:103031–103063, 2024

Reference 41

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Observation d82a7f68-a30e-44d8-9ef4-6b63ef0a8242 · outbound

This paper cites Cam- bridge University Press, 2017.

SUMO: Segment and Track Any Motion with Nonlinear State Space Models Cam- bridge University Press, 2017

Reference 42

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Observation adf1c428-3e67-42af-ab22-07d0c61b11b1 · outbound

This paper cites U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation.

SUMO: Segment and Track Any Motion with Nonlinear State Space Models U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation

Reference 43

Resolution: verified exact; local_arxiv, observed 2026-06-30T06:44:19.464869Z

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Observation 1b974db9-5251-4cfa-a2f3-ab64ddc9fc07 · outbound

This paper cites Transforming model prediction for tracking.

SUMO: Segment and Track Any Motion with Nonlinear State Space Models Transforming model prediction for tracking

Reference 44

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Observation 2b534731-1b9d-44cb-8a03-82691f621997 · outbound

This paper cites Em-driven unsupervised learning for efficient motion seg- mentation.IEEE Transactions on Pattern Analysis and Ma- chine Intelligence, 45(4):4462–4473, 2022.

SUMO: Segment and Track Any Motion with Nonlinear State Space Models Em-driven unsupervised learning for efficient motion seg- mentation.IEEE Transactions on Pattern Analysis and Ma- chine Intelligence, 45(4):4462–4473, 2022

Reference 45

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Observation 58c395f7-db7b-4fd0-bd50-486148893b76 · outbound

This paper cites Trackingnet: A large-scale dataset and benchmark for object tracking in the wild.

SUMO: Segment and Track Any Motion with Nonlinear State Space Models Trackingnet: A large-scale dataset and benchmark for object tracking in the wild

Reference 46

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Observation 7fe8733f-5286-47a1-a38c-3973cdda15af · outbound

This paper cites Segmentation of moving objects by long term video analysis.IEEE trans- actions on pattern analysis and machine intelligence, 36(6): 1187–1200, 2013.

SUMO: Segment and Track Any Motion with Nonlinear State Space Models Segmentation of moving objects by long term video analysis.IEEE trans- actions on pattern analysis and machine intelligence, 36(6): 1187–1200, 2013

Reference 47

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Observation 0747c2ec-eaf7-4e5d-9500-657fdfb385fe · outbound

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

SUMO: Segment and Track Any Motion with Nonlinear State Space Models A benchmark dataset and evaluation methodology for video object segmentation

Reference 48

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Observation f1c1847a-b8a9-4e1b-af0c-07ee09809b51 · outbound

This paper cites Tracking 3-d motion of dynamic objects using monocular visual-inertial sensing.IEEE Transactions on Robotics, 35 (4):799–816, 2019.

SUMO: Segment and Track Any Motion with Nonlinear State Space Models Tracking 3-d motion of dynamic objects using monocular visual-inertial sensing.IEEE Transactions on Robotics, 35 (4):799–816, 2019

Reference 49

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Observation 1df9656f-901a-49d5-9717-6da189062b1e · outbound

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

SUMO: Segment and Track Any Motion with Nonlinear State Space Models SAM 2: Segment Anything in Images and Videos

Reference 50

Resolution: verified exact; local_arxiv, observed 2026-06-30T06:44:19.474162Z

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Observation 23e69eac-aa96-4514-8799-c59601cc7ff9 · outbound

This paper cites Hi- era: A hierarchical vision transformer without the bells-and- whistles.

SUMO: Segment and Track Any Motion with Nonlinear State Space Models Hi- era: A hierarchical vision transformer without the bells-and- whistles

Reference 51

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Observation dd4bc292-e300-4756-95e1-106a531c9aca · outbound

This paper cites Explicit visual prompts for visual object tracking.

SUMO: Segment and Track Any Motion with Nonlinear State Space Models Explicit visual prompts for visual object tracking

Reference 52

Resolution: unresolved; no resolver link, observed 2026-06-30T06:37:27.783172Z

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Observation 9a5b8dc0-7a22-45d2-aa5d-ff64f121a66b · outbound

This paper cites Shortest paths for the reeds-shepp car: a worked out example of the use of geomet- ric techniques in nonlinear optimal control, 1991.

SUMO: Segment and Track Any Motion with Nonlinear State Space Models Shortest paths for the reeds-shepp car: a worked out example of the use of geomet- ric techniques in nonlinear optimal control, 1991

Reference 53

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Observation 1ed54acb-370c-4f0b-b7e6-601d3e2a79c1 · outbound

This paper cites Nuscenes-spatialqa: A spatial understanding and reasoning benchmark for vision- language models in autonomous driving.

SUMO: Segment and Track Any Motion with Nonlinear State Space Models Nuscenes-spatialqa: A spatial understanding and reasoning benchmark for vision- language models in autonomous driving

Reference 54

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Observation f7e4fd4c-ffd6-4e0b-a869-415a789856e8 · outbound

This paper cites Physi- cally analyzable ai-based nonlinear platoon dynamics mod- eling during traffic oscillation: A koopman approach.IEEE Transactions on Intelligent Transportation Systems, 2025.

SUMO: Segment and Track Any Motion with Nonlinear State Space Models Physi- cally analyzable ai-based nonlinear platoon dynamics mod- eling during traffic oscillation: A koopman approach.IEEE Transactions on Intelligent Transportation Systems, 2025

Reference 55

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Observation 6d052152-91c7-4e0d-81ad-30b424040747 · outbound

This paper cites The unscented kalman filter for nonlinear estimation.

SUMO: Segment and Track Any Motion with Nonlinear State Space Models The unscented kalman filter for nonlinear estimation

Reference 56

Resolution: unresolved; no resolver link, observed 2026-06-30T06:37:27.783172Z

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Observation 1b8fbefd-6188-4093-9172-34f3da8442b1 · outbound

This paper cites Segment- ing moving objects via an object-centric layered representa- tion.Advances in neural information processing systems, 35: 28023–28036, 2022.

SUMO: Segment and Track Any Motion with Nonlinear State Space Models Segment- ing moving objects via an object-centric layered representa- tion.Advances in neural information processing systems, 35: 28023–28036, 2022

Reference 57

Resolution: unresolved; no resolver link, observed 2026-06-30T06:37:27.783172Z

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Observation 25cfa35c-76c1-433a-9614-f45b28bcf992 · outbound

This paper cites Appearance- based refinement for object-centric motion segmentation.

SUMO: Segment and Track Any Motion with Nonlinear State Space Models Appearance- based refinement for object-centric motion segmentation

Reference 58

Resolution: unresolved; no resolver link, observed 2026-06-30T06:37:27.783172Z

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Observation eddfedcf-6cf5-450f-ae00-0d5c2fa24aaa · outbound

This paper cites Moving object segmentation: All you need is sam (and flow).

SUMO: Segment and Track Any Motion with Nonlinear State Space Models Moving object segmentation: All you need is sam (and flow)

Reference 59

Resolution: unresolved; no resolver link, observed 2026-06-30T06:37:27.783172Z

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Observation a2b484a1-5587-4324-a12b-f5ec08bd220a · outbound

This paper cites Autore- gressive queries for adaptive tracking with spatio-temporal transformers.

SUMO: Segment and Track Any Motion with Nonlinear State Space Models Autore- gressive queries for adaptive tracking with spatio-temporal transformers

Reference 60

Resolution: unresolved; no resolver link, observed 2026-06-30T06:37:27.783172Z

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Observation 5eb2c1f1-9dcf-4132-a344-0d12fa4b2acd · outbound

This paper cites Segmamba: Long-range sequential modeling mamba for 3d medical image segmentation.

SUMO: Segment and Track Any Motion with Nonlinear State Space Models Segmamba: Long-range sequential modeling mamba for 3d medical image segmentation

Reference 61

Resolution: unresolved; no resolver link, observed 2026-06-30T06:37:27.783172Z

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Observation 565b4a8b-3386-4d8a-8002-2d78b6d365cc · outbound

This paper cites Learning spatio-temporal transformer for vi- sual tracking.

SUMO: Segment and Track Any Motion with Nonlinear State Space Models Learning spatio-temporal transformer for vi- sual tracking

Reference 62

Resolution: unresolved; no resolver link, observed 2026-06-30T06:37:27.783172Z

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Observation e22c3c29-eab2-410f-90d5-cd19a7e1716e · outbound

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

SUMO: Segment and Track Any Motion with Nonlinear State Space Models SAMURAI: Adapting Segment Anything Model for Zero-Shot Visual Tracking with Motion-Aware Memory

Reference 63

Resolution: verified exact; arxiv_id, observed 2026-06-30T06:44:19.468864Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 3b26b161-db99-4b1a-80a3-daab0c9d506b · outbound

This paper cites Unsupervised moving object detection via contextual information separation.

SUMO: Segment and Track Any Motion with Nonlinear State Space Models Unsupervised moving object detection via contextual information separation

Reference 64

Resolution: unresolved; no resolver link, observed 2026-06-30T06:37:27.783172Z

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Observation fd34be7f-aad7-4c53-9cfb-cda37681dac7 · outbound

This paper cites Joint feature learning and relation modeling for tracking: A one-stream framework.

SUMO: Segment and Track Any Motion with Nonlinear State Space Models Joint feature learning and relation modeling for tracking: A one-stream framework

Reference 65

Resolution: unresolved; no resolver link, observed 2026-06-30T06:37:27.783172Z

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Observation df537f35-f0a6-40ad-bc4f-9bb2e8d8043a · outbound

This paper cites Deeper and wider siamese networks for real-time visual tracking.

SUMO: Segment and Track Any Motion with Nonlinear State Space Models Deeper and wider siamese networks for real-time visual tracking

Reference 66

Resolution: unresolved; no resolver link, observed 2026-06-30T06:37:27.783172Z

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Observation e5c0c6b0-ffe5-4df1-8db6-0525ba67c7cc · outbound

This paper cites Mdnet: A semantically and visually inter- pretable medical image diagnosis network.

SUMO: Segment and Track Any Motion with Nonlinear State Space Models Mdnet: A semantically and visually inter- pretable medical image diagnosis network

Reference 67

Resolution: unresolved; no resolver link, observed 2026-06-30T06:37:27.783172Z

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Observation 06b5f73d-074f-4b29-ab39-6079b6bf9ebc · outbound

This paper cites Odtrack: Online dense temporal token learning for visual tracking.

SUMO: Segment and Track Any Motion with Nonlinear State Space Models Odtrack: Online dense temporal token learning for visual tracking

Reference 68

Resolution: unresolved; no resolver link, observed 2026-06-30T06:37:27.783172Z

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